diff --git a/notebooks/statsmodels_TP.ipynb b/notebooks/statsmodels_TP.ipynb
index 6bfccfe7425810417e5fc03a7de1bcdf5c58e1c4..c9fbbd99afa7eb7b5a7a1a6d1cfe2d0f42a0641d 100644
--- a/notebooks/statsmodels_TP.ipynb
+++ b/notebooks/statsmodels_TP.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "4e16caf7",
"metadata": {},
"outputs": [],
@@ -20,84 +20,240 @@
},
{
"cell_type": "markdown",
- "id": "00aaf24e",
+ "id": "4f4198c3",
"metadata": {},
"source": [
- "# Toy dataset for ANOVAs"
+ "# Toy dataset for ANOVAs and planned comparisons"
]
},
{
"cell_type": "markdown",
- "id": "ace9ca6b",
+ "id": "4b5a7dba",
"metadata": {},
"source": [
"## Q\n",
"\n",
- "Load the `../data/wheat.txt` toy dataset [[1]](https://campus.murraystate.edu/academic/faculty/cmecklin/STA565/wheat.txt)."
+ "Load the `../data/wheat.txt` toy dataset [[1]](https://campus.murraystate.edu/academic/faculty/cmecklin/STA565/wheat.txt) with the adequate separator."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e90dac4",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "2b27d79d",
+ "id": "5b4a7e26",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d17c0ff",
"metadata": {},
+ "source": [
+ "## Q\n",
+ "\n",
+ "Perform a one-way ANOVA using a Wilkinson formula to specify a linear model of response variable `yield` with `variety` as independent variable. Print the summary tables and, if necessary, an ANOVA table."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca353d75",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7af3849e",
+ "metadata": {
+ "hidden": true
+ },
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "3ad2e6b4",
+ "id": "f024bed7",
"metadata": {},
"source": [
"## Q\n",
"\n",
- "Perform a one-way ANOVA fitting a linear model of response variable `yield` using `variety` only as independent variable. Print the summary tables and, if necessary, an ANOVA table."
+ "Perform a two-way ANOVA of `yield` using `variety` and `location` as categorical variables. Can we introduce an interaction term?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6b946a85",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "72bd487f",
+ "id": "b478ee2e",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "479601d6",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## Q\n",
+ "\n",
+ "`variety` now appears to have a significant effect. Run pairwise *t* tests to determine which varieties exhibit different yields, with Sidak-Holm correction for multiple comparisons."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8caafefb",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "66bf1fec",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f05bf78-6108-4c46-bda1-cec5d451ca48",
+ "metadata": {},
+ "source": [
+ "## Q\n",
+ "\n",
+ "Fit a mixed-effect linear model treating factor `location` as a random effect."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77ccd5ef-b6ee-4028-bdc7-585f808a8d29",
+ "metadata": {},
+ "source": [
+ "## A"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e3dd22f6-b4bb-46d3-b61a-0f96c0b42ed6",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "a6ef5a35",
+ "id": "39dbd8dd-91c1-4ccf-a0a5-46febad022f5",
"metadata": {},
"source": [
"## Q\n",
"\n",
- "Perform a two-way ANOVA of `yield` using `variety` and `location` as categorical variables. Can we introduce an interaction term?"
+ "Perform a Wald test to determine whether `variety` also exhibits a significant effect with this model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a5eed031-9789-4d93-a092-661258439102",
+ "metadata": {},
+ "source": [
+ "## A"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "4042d114",
+ "id": "0b7b04bd-a961-4fda-8d21-4f4f0b40a15c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "f02ea988",
+ "id": "caa21cf7-b76f-448a-bfff-26fbc905ad62",
"metadata": {},
"source": [
"## Q\n",
"\n",
- "`variety` now appears to have a significant effect. Run pairwise *t* tests to determine which varieties exhibit different yields, with corrections for multiple comparisons."
+ "Perform pairwise Wald tests for each pair of different varieties, and make a dataframe with a `pvalue` column and as many rows (10) as `X-Y` comparisons."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0c24cb5-3723-467e-87e3-0635eb94c599",
+ "metadata": {},
+ "source": [
+ "## A"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "bc21bb5b",
+ "id": "5118dc82-7e06-4981-acfa-3724262e70da",
"metadata": {},
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "id": "1a11bcee-430b-4060-888b-9f6d330d024c",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "## Q\n",
+ "\n",
+ "Correct the p-values for multiple comparisons, and add a `corrected pvalue` column to the result dataframe."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c35b139-8159-4c50-ac2b-70325f90e8a9",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "051d5e9e-95de-40ab-b676-97982ab2d4f3",
+ "metadata": {},
+ "source": [
+ "At least one difference now shows up as significant."
+ ]
+ },
{
"cell_type": "markdown",
"id": "358dce7a",
@@ -108,7 +264,7 @@
},
{
"cell_type": "markdown",
- "id": "e8300730",
+ "id": "143dff65",
"metadata": {},
"source": [
"## Q\n",
@@ -118,17 +274,29 @@
"Exclude the null-fare tickets."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "9c8a8d0f",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "305bd372",
- "metadata": {},
+ "id": "f06ebd84",
+ "metadata": {
+ "hidden": true
+ },
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "3080f062",
+ "id": "0e6efdb8",
"metadata": {},
"source": [
"Meaning of some columns:\n",
@@ -145,44 +313,70 @@
"\n",
"Instead of the classical `Survived` variable, we will try to explain the variations in `Fare`.\n",
"\n",
- "Let us first consider the first-class tickets only. In order not to loose many data, replace the missing deck information by an empty string.\n",
+ "Let us first consider the first-class tickets only. In order not to loose many data, replace the missing deck information by an empty string (`''`).\n",
"\n",
"Fit a _standard_ linear model for `Fare` as response variable, using `Embarked`, `Deck`, `Cabins`, `Passengers` and `Children` as independent variables (no interaction), and print the summary tables."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "ae10616e",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "2ec31a78",
- "metadata": {},
+ "id": "6be244ef",
+ "metadata": {
+ "hidden": true
+ },
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "dc3bcf67",
+ "id": "074c8e82",
"metadata": {},
"source": [
- "You may notice several issues, including the non-normality of the residuals, with high skewness and kurtosis.\n",
+ "##\n",
+ "\n",
+ "If you used `ols`, you may notice several issues, including the non-normality of the residuals, with high skewness and kurtosis.\n",
"\n",
- "If you defined all variables as categorical, you may also be warned about multicollinearity.\n",
+ "If you defined all variables as categorical, you may also be warned about multicollinearity. Let us ignore these warnings for now.\n",
"\n",
"## Q\n",
"\n",
"Print the residuals as a function of the predicted values."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "aad150f8",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "604e52a6",
- "metadata": {},
+ "id": "8571c87e",
+ "metadata": {
+ "hidden": true
+ },
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "b4c8a595",
+ "id": "5c6150f9",
"metadata": {},
"source": [
"## Q\n",
@@ -193,35 +387,59 @@
"Plot the Cook's distance for each ticket. Remove the outlier(s) and fit the model again."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "57c49da8",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "56266705",
- "metadata": {},
+ "id": "c2b97f70",
+ "metadata": {
+ "hidden": true
+ },
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "e2996d44",
+ "id": "94cb7b0a",
"metadata": {},
"source": [
"## Q\n",
"\n",
- "Plot the density of `Fare` for first-class passengers and overlay a fitted distribution function from the exponential family."
+ "Plot the density of `Fare` for first-class passengers and overlay a fitted distribution function from the exponential family. `scipy.stats.invgauss` and `scipy.stats.gamma` may be useful here."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9b31306d",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "d353cf03",
- "metadata": {},
+ "id": "d2ec65b5",
+ "metadata": {
+ "hidden": true
+ },
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "79fb1852",
+ "id": "6e8d2e6b",
"metadata": {},
"source": [
"## Q\n",
@@ -231,17 +449,29 @@
"Compare fares between decks, with corrections for multiple comparisons."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "0f7a8edc",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "d38d67dc",
- "metadata": {},
+ "id": "e58c6f4c",
+ "metadata": {
+ "hidden": true
+ },
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "dafeaa5b",
+ "id": "a7c01894",
"metadata": {},
"source": [
"## Q\n",
@@ -249,17 +479,29 @@
"Group the decks so that *A*, *B* and *C* are labelled *ABC*, and *D* and *E* are labelled *DE*. Check whether the simplified model unveils any difference of fare between the grouped decks."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "e72dd608",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "c0e0048b",
- "metadata": {},
+ "id": "7e1e4b40",
+ "metadata": {
+ "hidden": true
+ },
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "62268b17",
+ "id": "36556c9e",
"metadata": {},
"source": [
"## Q\n",
@@ -269,17 +511,29 @@
"You may for example look for the best model among those with a single `A * B` interaction term, and then repeat the procedure with the `A * B` term as a replacement for both `A` and `B`."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "b7706819",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "d08ec808",
- "metadata": {},
+ "id": "63983757",
+ "metadata": {
+ "hidden": true
+ },
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "6071b347",
+ "id": "89bfc75b",
"metadata": {},
"source": [
"## Q\n",
@@ -287,23 +541,36 @@
"Draw a [stripplot](https://seaborn.pydata.org/generated/seaborn.stripplot.html) of the AIC for the various models explored.\n",
"\n",
"Example:\n",
+ "\n",
"<img src=\"images/stripplot.png\" />"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "e596ffca",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "e39ff8fd",
- "metadata": {},
+ "id": "70bda649",
+ "metadata": {
+ "hidden": true
+ },
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "scientific_python",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "scientific_python"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -315,7 +582,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.4"
+ "version": "3.10.12"
},
"toc": {
"base_numbering": 1,
diff --git a/notebooks/statsmodels_TP_solutions.ipynb b/notebooks/statsmodels_TP_solutions.ipynb
index 94b9daea061b0fd885851b292156d23573c0da17..8bba90d3a3b00ad89e9e6190739d1b315f51bdeb 100644
--- a/notebooks/statsmodels_TP_solutions.ipynb
+++ b/notebooks/statsmodels_TP_solutions.ipynb
@@ -23,7 +23,7 @@
"id": "4f4198c3",
"metadata": {},
"source": [
- "# Toy dataset for ANOVAs"
+ "# Toy dataset for ANOVAs and planned comparisons"
]
},
{
@@ -33,7 +33,7 @@
"source": [
"## Q\n",
"\n",
- "Load the `../data/wheat.txt` toy dataset [[1]](https://campus.murraystate.edu/academic/faculty/cmecklin/STA565/wheat.txt)."
+ "Load the `../data/wheat.txt` toy dataset [[1]](https://campus.murraystate.edu/academic/faculty/cmecklin/STA565/wheat.txt) with the adequate separator."
]
},
{
@@ -309,16 +309,6 @@
"df"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ce3c48fc",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"id": "5d17c0ff",
@@ -326,7 +316,7 @@
"source": [
"## Q\n",
"\n",
- "Perform a one-way ANOVA fitting a linear model of response variable `yield` using `variety` only as independent variable. Print the summary tables and, if necessary, an ANOVA table."
+ "Perform a one-way ANOVA using a Wilkinson formula to specify a linear model of response variable `yield` with `variety` as independent variable. Print the summary tables and, if necessary, an ANOVA table."
]
},
{
@@ -339,6 +329,12 @@
"## A"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "b58e22ea-ff29-4a40-b778-e076c4006791",
+ "metadata": {},
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 3,
@@ -374,10 +370,10 @@
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 2.492</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Date:</th> <td>Mon, 26 Sep 2022</td> <th> Prob (F-statistic):</th> <td>0.0688</td> \n",
+ " <th>Date:</th> <td>Mon, 21 Aug 2023</td> <th> Prob (F-statistic):</th> <td>0.0688</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Time:</th> <td>15:17:00</td> <th> Log-Likelihood: </th> <td> -61.811</td>\n",
+ " <th>Time:</th> <td>15:56:22</td> <th> Log-Likelihood: </th> <td> -61.811</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 30</td> <th> AIC: </th> <td> 133.6</td>\n",
@@ -427,6 +423,44 @@
"</tr>\n",
"</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\toprule\n",
+ "\\textbf{Dep. Variable:} & Yield & \\textbf{ R-squared: } & 0.285 \\\\\n",
+ "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.171 \\\\\n",
+ "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 2.492 \\\\\n",
+ "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Prob (F-statistic):} & 0.0688 \\\\\n",
+ "\\textbf{Time:} & 15:56:22 & \\textbf{ Log-Likelihood: } & -61.811 \\\\\n",
+ "\\textbf{No. Observations:} & 30 & \\textbf{ AIC: } & 133.6 \\\\\n",
+ "\\textbf{Df Residuals:} & 25 & \\textbf{ BIC: } & 140.6 \\\\\n",
+ "\\textbf{Df Model:} & 4 & \\textbf{ } & \\\\\n",
+ "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lcccccc}\n",
+ " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
+ "\\midrule\n",
+ "\\textbf{Intercept} & 34.3500 & 0.849 & 40.443 & 0.000 & 32.601 & 36.099 \\\\\n",
+ "\\textbf{C(variety)[T.B]} & -1.2333 & 1.201 & -1.027 & 0.314 & -3.707 & 1.240 \\\\\n",
+ "\\textbf{C(variety)[T.C]} & 0.9500 & 1.201 & 0.791 & 0.436 & -1.524 & 3.424 \\\\\n",
+ "\\textbf{C(variety)[T.D]} & 1.2667 & 1.201 & 1.055 & 0.302 & -1.207 & 3.740 \\\\\n",
+ "\\textbf{C(variety)[T.E]} & -1.8167 & 1.201 & -1.512 & 0.143 & -4.290 & 0.657 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\textbf{Omnibus:} & 3.001 & \\textbf{ Durbin-Watson: } & 1.651 \\\\\n",
+ "\\textbf{Prob(Omnibus):} & 0.223 & \\textbf{ Jarque-Bera (JB): } & 1.436 \\\\\n",
+ "\\textbf{Skew:} & 0.131 & \\textbf{ Prob(JB): } & 0.488 \\\\\n",
+ "\\textbf{Kurtosis:} & 1.961 & \\textbf{ Cond. No. } & 5.83 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "%\\caption{OLS Regression Results}\n",
+ "\\end{center}\n",
+ "\n",
+ "Notes: \\newline\n",
+ " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+ ],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
@@ -435,8 +469,8 @@
"Dep. Variable: Yield R-squared: 0.285\n",
"Model: OLS Adj. R-squared: 0.171\n",
"Method: Least Squares F-statistic: 2.492\n",
- "Date: Mon, 26 Sep 2022 Prob (F-statistic): 0.0688\n",
- "Time: 15:17:00 Log-Likelihood: -61.811\n",
+ "Date: Mon, 21 Aug 2023 Prob (F-statistic): 0.0688\n",
+ "Time: 15:56:22 Log-Likelihood: -61.811\n",
"No. Observations: 30 AIC: 133.6\n",
"Df Residuals: 25 BIC: 140.6\n",
"Df Model: 4 \n",
@@ -486,8 +520,7 @@
"execution_count": 5,
"id": "656bda87",
"metadata": {
- "hidden": true,
- "scrolled": true
+ "hidden": true
},
"outputs": [
{
@@ -511,31 +544,27 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>sum_sq</th>\n",
" <th>df</th>\n",
+ " <th>sum_sq</th>\n",
+ " <th>mean_sq</th>\n",
" <th>F</th>\n",
" <th>PR(>F)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>Intercept</th>\n",
- " <td>7079.535000</td>\n",
- " <td>1.0</td>\n",
- " <td>1635.676494</td>\n",
- " <td>2.644622e-24</td>\n",
- " </tr>\n",
- " <tr>\n",
" <th>C(variety)</th>\n",
- " <td>43.136667</td>\n",
" <td>4.0</td>\n",
+ " <td>43.136667</td>\n",
+ " <td>10.784167</td>\n",
" <td>2.491605</td>\n",
- " <td>6.884095e-02</td>\n",
+ " <td>0.068841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Residual</th>\n",
- " <td>108.205000</td>\n",
" <td>25.0</td>\n",
+ " <td>108.205000</td>\n",
+ " <td>4.328200</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
@@ -544,10 +573,9 @@
"</div>"
],
"text/plain": [
- " sum_sq df F PR(>F)\n",
- "Intercept 7079.535000 1.0 1635.676494 2.644622e-24\n",
- "C(variety) 43.136667 4.0 2.491605 6.884095e-02\n",
- "Residual 108.205000 25.0 NaN NaN"
+ " df sum_sq mean_sq F PR(>F)\n",
+ "C(variety) 4.0 43.136667 10.784167 2.491605 0.068841\n",
+ "Residual 25.0 108.205000 4.328200 NaN NaN"
]
},
"execution_count": 5,
@@ -556,19 +584,9 @@
}
],
"source": [
- "sm.stats.anova_lm(model, typ=3)"
+ "sm.stats.anova_lm(model)"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1ede2496",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"id": "f024bed7",
@@ -594,8 +612,7 @@
"execution_count": 6,
"id": "b478ee2e",
"metadata": {
- "hidden": true,
- "scrolled": true
+ "hidden": true
},
"outputs": [
{
@@ -613,10 +630,10 @@
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 3.340</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Date:</th> <td>Mon, 26 Sep 2022</td> <th> Prob (F-statistic):</th> <td>0.0118</td> \n",
+ " <th>Date:</th> <td>Mon, 21 Aug 2023</td> <th> Prob (F-statistic):</th> <td>0.0118</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Time:</th> <td>15:17:01</td> <th> Log-Likelihood: </th> <td> -53.081</td>\n",
+ " <th>Time:</th> <td>15:56:22</td> <th> Log-Likelihood: </th> <td> -53.081</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 30</td> <th> AIC: </th> <td> 126.2</td>\n",
@@ -681,6 +698,49 @@
"</tr>\n",
"</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\toprule\n",
+ "\\textbf{Dep. Variable:} & Yield & \\textbf{ R-squared: } & 0.600 \\\\\n",
+ "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.421 \\\\\n",
+ "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 3.340 \\\\\n",
+ "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Prob (F-statistic):} & 0.0118 \\\\\n",
+ "\\textbf{Time:} & 15:56:22 & \\textbf{ Log-Likelihood: } & -53.081 \\\\\n",
+ "\\textbf{No. Observations:} & 30 & \\textbf{ AIC: } & 126.2 \\\\\n",
+ "\\textbf{Df Residuals:} & 20 & \\textbf{ BIC: } & 140.2 \\\\\n",
+ "\\textbf{Df Model:} & 9 & \\textbf{ } & \\\\\n",
+ "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lcccccc}\n",
+ " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
+ "\\midrule\n",
+ "\\textbf{Intercept} & 33.4667 & 1.004 & 33.338 & 0.000 & 31.373 & 35.561 \\\\\n",
+ "\\textbf{C(variety)[T.B]} & -1.2333 & 1.004 & -1.229 & 0.233 & -3.327 & 0.861 \\\\\n",
+ "\\textbf{C(variety)[T.C]} & 0.9500 & 1.004 & 0.946 & 0.355 & -1.144 & 3.044 \\\\\n",
+ "\\textbf{C(variety)[T.D]} & 1.2667 & 1.004 & 1.262 & 0.222 & -0.827 & 3.361 \\\\\n",
+ "\\textbf{C(variety)[T.E]} & -1.8167 & 1.004 & -1.810 & 0.085 & -3.911 & 0.277 \\\\\n",
+ "\\textbf{C(location)[T.2]} & -0.5800 & 1.100 & -0.527 & 0.604 & -2.874 & 1.714 \\\\\n",
+ "\\textbf{C(location)[T.3]} & -0.4800 & 1.100 & -0.436 & 0.667 & -2.774 & 1.814 \\\\\n",
+ "\\textbf{C(location)[T.4]} & 1.8200 & 1.100 & 1.655 & 0.114 & -0.474 & 4.114 \\\\\n",
+ "\\textbf{C(location)[T.5]} & 2.4200 & 1.100 & 2.201 & 0.040 & 0.126 & 4.714 \\\\\n",
+ "\\textbf{C(location)[T.6]} & 2.1200 & 1.100 & 1.928 & 0.068 & -0.174 & 4.414 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\textbf{Omnibus:} & 2.453 & \\textbf{ Durbin-Watson: } & 2.188 \\\\\n",
+ "\\textbf{Prob(Omnibus):} & 0.293 & \\textbf{ Jarque-Bera (JB): } & 1.509 \\\\\n",
+ "\\textbf{Skew:} & -0.282 & \\textbf{ Prob(JB): } & 0.470 \\\\\n",
+ "\\textbf{Kurtosis:} & 2.057 & \\textbf{ Cond. No. } & 7.47 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "%\\caption{OLS Regression Results}\n",
+ "\\end{center}\n",
+ "\n",
+ "Notes: \\newline\n",
+ " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+ ],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
@@ -689,8 +749,8 @@
"Dep. Variable: Yield R-squared: 0.600\n",
"Model: OLS Adj. R-squared: 0.421\n",
"Method: Least Squares F-statistic: 3.340\n",
- "Date: Mon, 26 Sep 2022 Prob (F-statistic): 0.0118\n",
- "Time: 15:17:01 Log-Likelihood: -53.081\n",
+ "Date: Mon, 21 Aug 2023 Prob (F-statistic): 0.0118\n",
+ "Time: 15:56:22 Log-Likelihood: -53.081\n",
"No. Observations: 30 AIC: 126.2\n",
"Df Residuals: 20 BIC: 140.2\n",
"Df Model: 9 \n",
@@ -735,9 +795,87 @@
"execution_count": 7,
"id": "4b7e44f7",
"metadata": {
- "hidden": true,
- "scrolled": true
+ "hidden": true
},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>df</th>\n",
+ " <th>sum_sq</th>\n",
+ " <th>mean_sq</th>\n",
+ " <th>F</th>\n",
+ " <th>PR(>F)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>C(variety)</th>\n",
+ " <td>4.0</td>\n",
+ " <td>43.136667</td>\n",
+ " <td>10.784167</td>\n",
+ " <td>3.567176</td>\n",
+ " <td>0.023675</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C(location)</th>\n",
+ " <td>5.0</td>\n",
+ " <td>47.741667</td>\n",
+ " <td>9.548333</td>\n",
+ " <td>3.158388</td>\n",
+ " <td>0.029151</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Residual</th>\n",
+ " <td>20.0</td>\n",
+ " <td>60.463333</td>\n",
+ " <td>3.023167</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " df sum_sq mean_sq F PR(>F)\n",
+ "C(variety) 4.0 43.136667 10.784167 3.567176 0.023675\n",
+ "C(location) 5.0 47.741667 9.548333 3.158388 0.029151\n",
+ "Residual 20.0 60.463333 3.023167 NaN NaN"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sm.stats.anova_lm(model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "0b06e2e0-cd16-4725-872b-9fc054206819",
+ "metadata": {},
"outputs": [
{
"data": {
@@ -789,63 +927,675 @@
" <td>2.915088e-02</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>Residual</th>\n",
- " <td>60.463333</td>\n",
- " <td>20.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
+ " <th>Residual</th>\n",
+ " <td>60.463333</td>\n",
+ " <td>20.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sum_sq df F PR(>F)\n",
+ "Intercept 3360.053333 1.0 1111.435029 5.290791e-19\n",
+ "C(variety) 43.136667 4.0 3.567176 2.367502e-02\n",
+ "C(location) 47.741667 5.0 3.158388 2.915088e-02\n",
+ "Residual 60.463333 20.0 NaN NaN"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sm.stats.anova_lm(model, typ=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "479601d6",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## Q\n",
+ "\n",
+ "`variety` now appears to have a significant effect. Run pairwise *t* tests to determine which varieties exhibit different yields, with Sidak-Holm correction for multiple comparisons."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8caafefb",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## A"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "66bf1fec",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>coef</th>\n",
+ " <th>std err</th>\n",
+ " <th>t</th>\n",
+ " <th>P>|t|</th>\n",
+ " <th>Conf. Int. Low</th>\n",
+ " <th>Conf. Int. Upp.</th>\n",
+ " <th>pvalue-hs</th>\n",
+ " <th>reject-hs</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>B-A</th>\n",
+ " <td>-1.233333</td>\n",
+ " <td>1.003854</td>\n",
+ " <td>-1.228599</td>\n",
+ " <td>0.233484</td>\n",
+ " <td>-3.327335</td>\n",
+ " <td>0.860669</td>\n",
+ " <td>0.714116</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C-A</th>\n",
+ " <td>0.950000</td>\n",
+ " <td>1.003854</td>\n",
+ " <td>0.946353</td>\n",
+ " <td>0.355263</td>\n",
+ " <td>-1.144002</td>\n",
+ " <td>3.044002</td>\n",
+ " <td>0.731992</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D-A</th>\n",
+ " <td>1.266667</td>\n",
+ " <td>1.003854</td>\n",
+ " <td>1.261804</td>\n",
+ " <td>0.221537</td>\n",
+ " <td>-0.827335</td>\n",
+ " <td>3.360669</td>\n",
+ " <td>0.714116</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>E-A</th>\n",
+ " <td>-1.816667</td>\n",
+ " <td>1.003854</td>\n",
+ " <td>-1.809693</td>\n",
+ " <td>0.085398</td>\n",
+ " <td>-3.910669</td>\n",
+ " <td>0.277335</td>\n",
+ " <td>0.414682</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C-B</th>\n",
+ " <td>2.183333</td>\n",
+ " <td>1.003854</td>\n",
+ " <td>2.174952</td>\n",
+ " <td>0.041804</td>\n",
+ " <td>0.089331</td>\n",
+ " <td>4.277335</td>\n",
+ " <td>0.258383</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D-B</th>\n",
+ " <td>2.500000</td>\n",
+ " <td>1.003854</td>\n",
+ " <td>2.490403</td>\n",
+ " <td>0.021673</td>\n",
+ " <td>0.405998</td>\n",
+ " <td>4.594002</td>\n",
+ " <td>0.160786</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>E-B</th>\n",
+ " <td>-0.583333</td>\n",
+ " <td>1.003854</td>\n",
+ " <td>-0.581094</td>\n",
+ " <td>0.567669</td>\n",
+ " <td>-2.677335</td>\n",
+ " <td>1.510669</td>\n",
+ " <td>0.813090</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D-C</th>\n",
+ " <td>0.316667</td>\n",
+ " <td>1.003854</td>\n",
+ " <td>0.315451</td>\n",
+ " <td>0.755687</td>\n",
+ " <td>-1.777335</td>\n",
+ " <td>2.410669</td>\n",
+ " <td>0.813090</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>E-C</th>\n",
+ " <td>-2.766667</td>\n",
+ " <td>1.003854</td>\n",
+ " <td>-2.756046</td>\n",
+ " <td>0.012183</td>\n",
+ " <td>-4.860669</td>\n",
+ " <td>-0.672665</td>\n",
+ " <td>0.104454</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>E-D</th>\n",
+ " <td>-3.083333</td>\n",
+ " <td>1.003854</td>\n",
+ " <td>-3.071497</td>\n",
+ " <td>0.006021</td>\n",
+ " <td>-5.177335</td>\n",
+ " <td>-0.989331</td>\n",
+ " <td>0.058608</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " coef std err t P>|t| Conf. Int. Low Conf. Int. Upp. \\\n",
+ "B-A -1.233333 1.003854 -1.228599 0.233484 -3.327335 0.860669 \n",
+ "C-A 0.950000 1.003854 0.946353 0.355263 -1.144002 3.044002 \n",
+ "D-A 1.266667 1.003854 1.261804 0.221537 -0.827335 3.360669 \n",
+ "E-A -1.816667 1.003854 -1.809693 0.085398 -3.910669 0.277335 \n",
+ "C-B 2.183333 1.003854 2.174952 0.041804 0.089331 4.277335 \n",
+ "D-B 2.500000 1.003854 2.490403 0.021673 0.405998 4.594002 \n",
+ "E-B -0.583333 1.003854 -0.581094 0.567669 -2.677335 1.510669 \n",
+ "D-C 0.316667 1.003854 0.315451 0.755687 -1.777335 2.410669 \n",
+ "E-C -2.766667 1.003854 -2.756046 0.012183 -4.860669 -0.672665 \n",
+ "E-D -3.083333 1.003854 -3.071497 0.006021 -5.177335 -0.989331 \n",
+ "\n",
+ " pvalue-hs reject-hs \n",
+ "B-A 0.714116 False \n",
+ "C-A 0.731992 False \n",
+ "D-A 0.714116 False \n",
+ "E-A 0.414682 False \n",
+ "C-B 0.258383 False \n",
+ "D-B 0.160786 False \n",
+ "E-B 0.813090 False \n",
+ "D-C 0.813090 False \n",
+ "E-C 0.104454 False \n",
+ "E-D 0.058608 False "
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.t_test_pairwise('C(variety)').result_frame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f9fd4d7a-7f1f-481d-9058-f62cf7a8e82a",
+ "metadata": {},
+ "source": [
+ "We fail to catch significant effects, partly because of the correction for multiple comparisons."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f05bf78-6108-4c46-bda1-cec5d451ca48",
+ "metadata": {},
+ "source": [
+ "## Q\n",
+ "\n",
+ "Fit a mixed-effect linear model treating factor `location` as a random effect."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77ccd5ef-b6ee-4028-bdc7-585f808a8d29",
+ "metadata": {},
+ "source": [
+ "## A"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "e3dd22f6-b4bb-46d3-b61a-0f96c0b42ed6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<table class=\"simpletable\">\n",
+ "<tr>\n",
+ " <td>Model:</td> <td>MixedLM</td> <td>Dependent Variable:</td> <td>Yield</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>No. Observations:</td> <td>30</td> <td>Method:</td> <td>REML</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>No. Groups:</td> <td>6</td> <td>Scale:</td> <td>3.0232</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>Min. group size:</td> <td>5</td> <td>Log-Likelihood:</td> <td>-56.6568</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>Max. group size:</td> <td>5</td> <td>Converged:</td> <td>Yes</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>Mean group size:</td> <td>5.0</td> <td></td> <td></td> \n",
+ "</tr>\n",
+ "</table>\n",
+ "<table class=\"simpletable\">\n",
+ "<tr>\n",
+ " <td></td> <th>Coef.</th> <th>Std.Err.</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Intercept</th> <td>34.350</td> <td>0.849</td> <td>40.444</td> <td>0.000</td> <td>32.685</td> <td>36.015</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>C(variety)[T.B]</th> <td>-1.233</td> <td>1.004</td> <td>-1.229</td> <td>0.219</td> <td>-3.201</td> <td>0.734</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>C(variety)[T.C]</th> <td>0.950</td> <td>1.004</td> <td>0.946</td> <td>0.344</td> <td>-1.018</td> <td>2.918</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>C(variety)[T.D]</th> <td>1.267</td> <td>1.004</td> <td>1.262</td> <td>0.207</td> <td>-0.701</td> <td>3.234</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>C(variety)[T.E]</th> <td>-1.817</td> <td>1.004</td> <td>-1.810</td> <td>0.070</td> <td>-3.784</td> <td>0.151</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>location Var</th> <td>1.305</td> <td>0.777</td> <td></td> <td></td> <td></td> <td></td> \n",
+ "</tr>\n",
+ "</table><br/>\n"
+ ],
+ "text/latex": [
+ "\\begin{table}\n",
+ "\\caption{Mixed Linear Model Regression Results}\n",
+ "\\label{}\n",
+ "\\begin{center}\n",
+ "\\begin{tabular}{llll}\n",
+ "\\hline\n",
+ "Model: & MixedLM & Dependent Variable: & Yield \\\\\n",
+ "No. Observations: & 30 & Method: & REML \\\\\n",
+ "No. Groups: & 6 & Scale: & 3.0232 \\\\\n",
+ "Min. group size: & 5 & Log-Likelihood: & -56.6568 \\\\\n",
+ "Max. group size: & 5 & Converged: & Yes \\\\\n",
+ "Mean group size: & 5.0 & & \\\\\n",
+ "\\hline\n",
+ "\\end{tabular}\n",
+ "\\end{center}\n",
+ "\n",
+ "\\begin{center}\n",
+ "\\begin{tabular}{lrrrrrr}\n",
+ "\\hline\n",
+ " & Coef. & Std.Err. & z & P$> |$z$|$ & [0.025 & 0.975] \\\\\n",
+ "\\hline\n",
+ "Intercept & 34.350 & 0.849 & 40.444 & 0.000 & 32.685 & 36.015 \\\\\n",
+ "C(variety)[T.B] & -1.233 & 1.004 & -1.229 & 0.219 & -3.201 & 0.734 \\\\\n",
+ "C(variety)[T.C] & 0.950 & 1.004 & 0.946 & 0.344 & -1.018 & 2.918 \\\\\n",
+ "C(variety)[T.D] & 1.267 & 1.004 & 1.262 & 0.207 & -0.701 & 3.234 \\\\\n",
+ "C(variety)[T.E] & -1.817 & 1.004 & -1.810 & 0.070 & -3.784 & 0.151 \\\\\n",
+ "location Var & 1.305 & 0.777 & & & & \\\\\n",
+ "\\hline\n",
+ "\\end{tabular}\n",
+ "\\end{center}\n",
+ "\\end{table}\n",
+ "\\bigskip\n"
+ ],
+ "text/plain": [
+ "<class 'statsmodels.iolib.summary2.Summary'>\n",
+ "\"\"\"\n",
+ " Mixed Linear Model Regression Results\n",
+ "==========================================================\n",
+ "Model: MixedLM Dependent Variable: Yield \n",
+ "No. Observations: 30 Method: REML \n",
+ "No. Groups: 6 Scale: 3.0232 \n",
+ "Min. group size: 5 Log-Likelihood: -56.6568\n",
+ "Max. group size: 5 Converged: Yes \n",
+ "Mean group size: 5.0 \n",
+ "----------------------------------------------------------\n",
+ " Coef. Std.Err. z P>|z| [0.025 0.975]\n",
+ "----------------------------------------------------------\n",
+ "Intercept 34.350 0.849 40.444 0.000 32.685 36.015\n",
+ "C(variety)[T.B] -1.233 1.004 -1.229 0.219 -3.201 0.734\n",
+ "C(variety)[T.C] 0.950 1.004 0.946 0.344 -1.018 2.918\n",
+ "C(variety)[T.D] 1.267 1.004 1.262 0.207 -0.701 3.234\n",
+ "C(variety)[T.E] -1.817 1.004 -1.810 0.070 -3.784 0.151\n",
+ "location Var 1.305 0.777 \n",
+ "==========================================================\n",
+ "\n",
+ "\"\"\""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model = smf.mixedlm('Yield ~ C(variety)', df, groups='location').fit()\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "39dbd8dd-91c1-4ccf-a0a5-46febad022f5",
+ "metadata": {},
+ "source": [
+ "## Q\n",
+ "\n",
+ "Perform a Wald test to determine whether `variety` also exhibits a significant effect with this model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a5eed031-9789-4d93-a092-661258439102",
+ "metadata": {},
+ "source": [
+ "## A"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "0b7b04bd-a961-4fda-8d21-4f4f0b40a15c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
+ "<Wald test (chi2): statistic=14.26858622241916, p-value=0.006485383872451716, df_denom=4>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.wald_test('C(variety)[T.B] = C(variety)[T.C] = C(variety)[T.D] = C(variety)[T.E] = 0', scalar=True, use_f=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "caa21cf7-b76f-448a-bfff-26fbc905ad62",
+ "metadata": {},
+ "source": [
+ "## Q\n",
+ "\n",
+ "Perform pairwise Wald tests for each pair of different varieties, and make a dataframe with a `pvalue` column and as many rows (10) as `X-Y` comparisons."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0c24cb5-3723-467e-87e3-0635eb94c599",
+ "metadata": {},
+ "source": [
+ "## A"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99e0c766-ae3e-4998-8c68-49bc3786e6aa",
+ "metadata": {},
+ "source": [
+ "Reminder: comparisons with level `A` are expressed as a slope equal to 0, while comparisons between any other 2 levels are expressed as equal slopes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "5118dc82-7e06-4981-acfa-3724262e70da",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
+ "<Wald test (chi2): statistic=1.5094424340075572, p-value=0.21922418570860835, df_denom=1>"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.wald_test('C(variety)[T.B] = 0', scalar=True, use_f=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "bc38736a-b4cc-4f5e-83e4-c301bc41d948",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
+ "<Wald test (chi2): statistic=4.730376481008711, p-value=0.02963439793410425, df_denom=1>"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.wald_test('C(variety)[T.B] = C(variety)[T.C]', scalar=True, use_f=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19093d20-8af2-4b18-94fe-831b39d0f644",
+ "metadata": {},
+ "source": [
+ "We also need to look in more details at the structure of the returned results:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "641ceb04-0427-4f48-ba51-7c039ae5099b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1.5094424340075572, 0.21922418570860835)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results = model.wald_test('C(variety)[T.B] = 0', scalar=True, use_f=False)\n",
+ "results.statistic, results.pvalue"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "cf48a791-3d49-4bd8-b49d-c62d3490d27d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>chi2</th>\n",
+ " <th>pvalue</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>A-B</th>\n",
+ " <td>1.509442</td>\n",
+ " <td>0.219224</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>A-C</th>\n",
+ " <td>0.895577</td>\n",
+ " <td>0.343971</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>A-D</th>\n",
+ " <td>1.592137</td>\n",
+ " <td>0.207021</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>A-E</th>\n",
+ " <td>3.274961</td>\n",
+ " <td>0.070345</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>B-C</th>\n",
+ " <td>4.730376</td>\n",
+ " <td>0.029634</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>B-D</th>\n",
+ " <td>6.202055</td>\n",
+ " <td>0.012760</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>B-E</th>\n",
+ " <td>0.337667</td>\n",
+ " <td>0.561179</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C-D</th>\n",
+ " <td>0.099509</td>\n",
+ " <td>0.752420</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C-E</th>\n",
+ " <td>7.595726</td>\n",
+ " <td>0.005851</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D-E</th>\n",
+ " <td>9.434015</td>\n",
+ " <td>0.002130</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " sum_sq df F PR(>F)\n",
- "Intercept 3360.053333 1.0 1111.435029 5.290791e-19\n",
- "C(variety) 43.136667 4.0 3.567176 2.367502e-02\n",
- "C(location) 47.741667 5.0 3.158388 2.915088e-02\n",
- "Residual 60.463333 20.0 NaN NaN"
+ " chi2 pvalue\n",
+ "A-B 1.509442 0.219224\n",
+ "A-C 0.895577 0.343971\n",
+ "A-D 1.592137 0.207021\n",
+ "A-E 3.274961 0.070345\n",
+ "B-C 4.730376 0.029634\n",
+ "B-D 6.202055 0.012760\n",
+ "B-E 0.337667 0.561179\n",
+ "C-D 0.099509 0.752420\n",
+ "C-E 7.595726 0.005851\n",
+ "D-E 9.434015 0.002130"
]
},
- "execution_count": 7,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "sm.stats.anova_lm(model, typ=3)"
+ "levels = list('ABCDE')\n",
+ "\n",
+ "table = pd.DataFrame()\n",
+ "\n",
+ "for level in levels[1:]:\n",
+ " label = f\"A-{level}\"\n",
+ " results = model.wald_test(f\"C(variety)[T.{level}] = 0\", scalar=True, use_f=False)\n",
+ " table.loc[label, 'chi2'] = results.statistic\n",
+ " table.loc[label, 'pvalue'] = results.pvalue\n",
+ "\n",
+ "for i, level1 in enumerate(levels[1:-1]):\n",
+ " for level2 in levels[i+2:]:\n",
+ " label = f\"{level1}-{level2}\"\n",
+ " results = model.wald_test(f\"C(variety)[T.{level1}] = C(variety)[T.{level2}]\", scalar=True, use_f=False)\n",
+ " table.loc[label, 'chi2'] = results.statistic\n",
+ " table.loc[label, 'pvalue'] = results.pvalue\n",
+ "\n",
+ "table"
]
},
{
"cell_type": "markdown",
- "id": "479601d6",
+ "id": "1a11bcee-430b-4060-888b-9f6d330d024c",
"metadata": {
- "heading_collapsed": true
+ "hidden": true
},
"source": [
"## Q\n",
"\n",
- "`variety` now appears to have a significant effect. Run pairwise *t* tests to determine which varieties exhibit different yields, with corrections for multiple comparisons."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8caafefb",
- "metadata": {
- "heading_collapsed": true
- },
- "source": [
- "## A"
+ "Correct the p-values for multiple comparisons, and add a `corrected pvalue` column to the result dataframe."
]
},
{
"cell_type": "code",
- "execution_count": 8,
- "id": "66bf1fec",
- "metadata": {
- "hidden": true,
- "scrolled": false
- },
+ "execution_count": 16,
+ "id": "5c35b139-8159-4c50-ac2b-70325f90e8a9",
+ "metadata": {},
"outputs": [
{
"data": {
@@ -868,186 +1618,108 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>coef</th>\n",
- " <th>std err</th>\n",
- " <th>t</th>\n",
- " <th>P>|t|</th>\n",
- " <th>Conf. Int. Low</th>\n",
- " <th>Conf. Int. Upp.</th>\n",
- " <th>pvalue-hs</th>\n",
- " <th>reject-hs</th>\n",
+ " <th>chi2</th>\n",
+ " <th>pvalue</th>\n",
+ " <th>corrected pvalue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>B-A</th>\n",
- " <td>-1.233333</td>\n",
- " <td>1.003854</td>\n",
- " <td>-1.228599</td>\n",
- " <td>0.233484</td>\n",
- " <td>-3.327335</td>\n",
- " <td>0.860669</td>\n",
- " <td>0.714116</td>\n",
- " <td>False</td>\n",
+ " <th>A-B</th>\n",
+ " <td>1.509442</td>\n",
+ " <td>0.219224</td>\n",
+ " <td>0.686449</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>C-A</th>\n",
- " <td>0.950000</td>\n",
- " <td>1.003854</td>\n",
- " <td>0.946353</td>\n",
- " <td>0.355263</td>\n",
- " <td>-1.144002</td>\n",
- " <td>3.044002</td>\n",
- " <td>0.731992</td>\n",
- " <td>False</td>\n",
+ " <th>A-C</th>\n",
+ " <td>0.895577</td>\n",
+ " <td>0.343971</td>\n",
+ " <td>0.717662</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>D-A</th>\n",
- " <td>1.266667</td>\n",
- " <td>1.003854</td>\n",
- " <td>1.261804</td>\n",
- " <td>0.221537</td>\n",
- " <td>-0.827335</td>\n",
- " <td>3.360669</td>\n",
- " <td>0.714116</td>\n",
- " <td>False</td>\n",
+ " <th>A-D</th>\n",
+ " <td>1.592137</td>\n",
+ " <td>0.207021</td>\n",
+ " <td>0.686449</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>E-A</th>\n",
- " <td>-1.816667</td>\n",
- " <td>1.003854</td>\n",
- " <td>-1.809693</td>\n",
- " <td>0.085398</td>\n",
- " <td>-3.910669</td>\n",
- " <td>0.277335</td>\n",
- " <td>0.414682</td>\n",
- " <td>False</td>\n",
+ " <th>A-E</th>\n",
+ " <td>3.274961</td>\n",
+ " <td>0.070345</td>\n",
+ " <td>0.354447</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>C-B</th>\n",
- " <td>2.183333</td>\n",
- " <td>1.003854</td>\n",
- " <td>2.174952</td>\n",
- " <td>0.041804</td>\n",
- " <td>0.089331</td>\n",
- " <td>4.277335</td>\n",
- " <td>0.258383</td>\n",
- " <td>False</td>\n",
+ " <th>B-C</th>\n",
+ " <td>4.730376</td>\n",
+ " <td>0.029634</td>\n",
+ " <td>0.189883</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>D-B</th>\n",
- " <td>2.500000</td>\n",
- " <td>1.003854</td>\n",
- " <td>2.490403</td>\n",
- " <td>0.021673</td>\n",
- " <td>0.405998</td>\n",
- " <td>4.594002</td>\n",
- " <td>0.160786</td>\n",
- " <td>False</td>\n",
+ " <th>B-D</th>\n",
+ " <td>6.202055</td>\n",
+ " <td>0.012760</td>\n",
+ " <td>0.097637</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>E-B</th>\n",
- " <td>-0.583333</td>\n",
- " <td>1.003854</td>\n",
- " <td>-0.581094</td>\n",
- " <td>0.567669</td>\n",
- " <td>-2.677335</td>\n",
- " <td>1.510669</td>\n",
- " <td>0.813090</td>\n",
- " <td>False</td>\n",
+ " <th>B-E</th>\n",
+ " <td>0.337667</td>\n",
+ " <td>0.561179</td>\n",
+ " <td>0.807436</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>D-C</th>\n",
- " <td>0.316667</td>\n",
- " <td>1.003854</td>\n",
- " <td>0.315451</td>\n",
- " <td>0.755687</td>\n",
- " <td>-1.777335</td>\n",
- " <td>2.410669</td>\n",
- " <td>0.813090</td>\n",
- " <td>False</td>\n",
+ " <th>C-D</th>\n",
+ " <td>0.099509</td>\n",
+ " <td>0.752420</td>\n",
+ " <td>0.807436</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>E-C</th>\n",
- " <td>-2.766667</td>\n",
- " <td>1.003854</td>\n",
- " <td>-2.756046</td>\n",
- " <td>0.012183</td>\n",
- " <td>-4.860669</td>\n",
- " <td>-0.672665</td>\n",
- " <td>0.104454</td>\n",
- " <td>False</td>\n",
+ " <th>C-E</th>\n",
+ " <td>7.595726</td>\n",
+ " <td>0.005851</td>\n",
+ " <td>0.051441</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>E-D</th>\n",
- " <td>-3.083333</td>\n",
- " <td>1.003854</td>\n",
- " <td>-3.071497</td>\n",
- " <td>0.006021</td>\n",
- " <td>-5.177335</td>\n",
- " <td>-0.989331</td>\n",
- " <td>0.058608</td>\n",
- " <td>False</td>\n",
+ " <th>D-E</th>\n",
+ " <td>9.434015</td>\n",
+ " <td>0.002130</td>\n",
+ " <td>0.021097</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " coef std err t P>|t| Conf. Int. Low Conf. Int. Upp. \\\n",
- "B-A -1.233333 1.003854 -1.228599 0.233484 -3.327335 0.860669 \n",
- "C-A 0.950000 1.003854 0.946353 0.355263 -1.144002 3.044002 \n",
- "D-A 1.266667 1.003854 1.261804 0.221537 -0.827335 3.360669 \n",
- "E-A -1.816667 1.003854 -1.809693 0.085398 -3.910669 0.277335 \n",
- "C-B 2.183333 1.003854 2.174952 0.041804 0.089331 4.277335 \n",
- "D-B 2.500000 1.003854 2.490403 0.021673 0.405998 4.594002 \n",
- "E-B -0.583333 1.003854 -0.581094 0.567669 -2.677335 1.510669 \n",
- "D-C 0.316667 1.003854 0.315451 0.755687 -1.777335 2.410669 \n",
- "E-C -2.766667 1.003854 -2.756046 0.012183 -4.860669 -0.672665 \n",
- "E-D -3.083333 1.003854 -3.071497 0.006021 -5.177335 -0.989331 \n",
- "\n",
- " pvalue-hs reject-hs \n",
- "B-A 0.714116 False \n",
- "C-A 0.731992 False \n",
- "D-A 0.714116 False \n",
- "E-A 0.414682 False \n",
- "C-B 0.258383 False \n",
- "D-B 0.160786 False \n",
- "E-B 0.813090 False \n",
- "D-C 0.813090 False \n",
- "E-C 0.104454 False \n",
- "E-D 0.058608 False "
+ " chi2 pvalue corrected pvalue\n",
+ "A-B 1.509442 0.219224 0.686449\n",
+ "A-C 0.895577 0.343971 0.717662\n",
+ "A-D 1.592137 0.207021 0.686449\n",
+ "A-E 3.274961 0.070345 0.354447\n",
+ "B-C 4.730376 0.029634 0.189883\n",
+ "B-D 6.202055 0.012760 0.097637\n",
+ "B-E 0.337667 0.561179 0.807436\n",
+ "C-D 0.099509 0.752420 0.807436\n",
+ "C-E 7.595726 0.005851 0.051441\n",
+ "D-E 9.434015 0.002130 0.021097"
]
},
- "execution_count": 8,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "model.t_test_pairwise('C(variety)').result_frame"
+ "_, table['corrected pvalue'], _, _ = multipletests(table['pvalue'])\n",
+ "table"
]
},
{
"cell_type": "markdown",
- "id": "02b5fc83",
- "metadata": {
- "hidden": true
- },
+ "id": "051d5e9e-95de-40ab-b676-97982ab2d4f3",
+ "metadata": {},
"source": [
- "We performed too many comparisons! We should have been more careful in choosing varieties of interest."
+ "At least one difference now shows up as significant."
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "049f323a",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"id": "358dce7a",
@@ -1080,7 +1752,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 17,
"id": "f06ebd84",
"metadata": {
"hidden": true
@@ -1310,7 +1982,7 @@
"[929 rows x 11 columns]"
]
},
- "execution_count": 31,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -1322,11 +1994,10 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 18,
"id": "78c8e549",
"metadata": {
- "hidden": true,
- "scrolled": true
+ "hidden": true
},
"outputs": [
{
@@ -1550,7 +2221,7 @@
"LINE 0 0 0 0 "
]
},
- "execution_count": 32,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -1561,7 +2232,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 19,
"id": "de15fca7",
"metadata": {
"hidden": true
@@ -1615,7 +2286,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 20,
"id": "6be244ef",
"metadata": {
"hidden": true
@@ -1845,7 +2516,7 @@
"[182 rows x 11 columns]"
]
},
- "execution_count": 34,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -1858,7 +2529,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 21,
"id": "583888a9",
"metadata": {
"hidden": true
@@ -1879,10 +2550,10 @@
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 43.65</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Date:</th> <td>Mon, 26 Sep 2022</td> <th> Prob (F-statistic):</th> <td>5.51e-47</td>\n",
+ " <th>Date:</th> <td>Mon, 21 Aug 2023</td> <th> Prob (F-statistic):</th> <td>5.51e-47</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Time:</th> <td>16:47:16</td> <th> Log-Likelihood: </th> <td> -861.37</td>\n",
+ " <th>Time:</th> <td>15:56:23</td> <th> Log-Likelihood: </th> <td> -861.37</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 182</td> <th> AIC: </th> <td> 1751.</td>\n",
@@ -1959,6 +2630,53 @@
"</tr>\n",
"</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\toprule\n",
+ "\\textbf{Dep. Variable:} & Fare & \\textbf{ R-squared: } & 0.772 \\\\\n",
+ "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.754 \\\\\n",
+ "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 43.65 \\\\\n",
+ "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Prob (F-statistic):} & 5.51e-47 \\\\\n",
+ "\\textbf{Time:} & 15:56:23 & \\textbf{ Log-Likelihood: } & -861.37 \\\\\n",
+ "\\textbf{No. Observations:} & 182 & \\textbf{ AIC: } & 1751. \\\\\n",
+ "\\textbf{Df Residuals:} & 168 & \\textbf{ BIC: } & 1796. \\\\\n",
+ "\\textbf{Df Model:} & 13 & \\textbf{ } & \\\\\n",
+ "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lcccccc}\n",
+ " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
+ "\\midrule\n",
+ "\\textbf{Intercept} & -11.6346 & 6.807 & -1.709 & 0.089 & -25.073 & 1.804 \\\\\n",
+ "\\textbf{C(Embarked)[T.Q]} & -23.9597 & 29.490 & -0.812 & 0.418 & -82.179 & 34.260 \\\\\n",
+ "\\textbf{C(Embarked)[T.S]} & -3.5748 & 4.549 & -0.786 & 0.433 & -12.555 & 5.405 \\\\\n",
+ "\\textbf{C(Deck)[T.A]} & 4.9509 & 8.045 & 0.615 & 0.539 & -10.931 & 20.833 \\\\\n",
+ "\\textbf{C(Deck)[T.B]} & 18.2136 & 7.233 & 2.518 & 0.013 & 3.934 & 32.493 \\\\\n",
+ "\\textbf{C(Deck)[T.C]} & 0.5132 & 6.418 & 0.080 & 0.936 & -12.157 & 13.184 \\\\\n",
+ "\\textbf{C(Deck)[T.D]} & -4.6450 & 7.440 & -0.624 & 0.533 & -19.333 & 10.043 \\\\\n",
+ "\\textbf{C(Deck)[T.E]} & -11.8527 & 8.103 & -1.463 & 0.145 & -27.849 & 4.143 \\\\\n",
+ "\\textbf{C(Cabins)[T.2]} & -0.8461 & 9.114 & -0.093 & 0.926 & -18.839 & 17.147 \\\\\n",
+ "\\textbf{C(Cabins)[T.3]} & -16.3908 & 13.354 & -1.227 & 0.221 & -42.754 & 9.972 \\\\\n",
+ "\\textbf{C(Cabins)[T.4]} & 107.5908 & 17.838 & 6.032 & 0.000 & 72.375 & 142.806 \\\\\n",
+ "\\textbf{C(Cabins)[T.5]} & -36.0599 & 35.618 & -1.012 & 0.313 & -106.377 & 34.257 \\\\\n",
+ "\\textbf{Passengers} & 41.6937 & 3.585 & 11.631 & 0.000 & 34.617 & 48.771 \\\\\n",
+ "\\textbf{Children} & -58.0156 & 12.642 & -4.589 & 0.000 & -82.973 & -33.059 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\textbf{Omnibus:} & 173.832 & \\textbf{ Durbin-Watson: } & 2.009 \\\\\n",
+ "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 6658.055 \\\\\n",
+ "\\textbf{Skew:} & 3.273 & \\textbf{ Prob(JB): } & 0.00 \\\\\n",
+ "\\textbf{Kurtosis:} & 31.899 & \\textbf{ Cond. No. } & 41.2 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "%\\caption{OLS Regression Results}\n",
+ "\\end{center}\n",
+ "\n",
+ "Notes: \\newline\n",
+ " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+ ],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
@@ -1967,8 +2685,8 @@
"Dep. Variable: Fare R-squared: 0.772\n",
"Model: OLS Adj. R-squared: 0.754\n",
"Method: Least Squares F-statistic: 43.65\n",
- "Date: Mon, 26 Sep 2022 Prob (F-statistic): 5.51e-47\n",
- "Time: 16:47:16 Log-Likelihood: -861.37\n",
+ "Date: Mon, 21 Aug 2023 Prob (F-statistic): 5.51e-47\n",
+ "Time: 15:56:23 Log-Likelihood: -861.37\n",
"No. Observations: 182 AIC: 1751.\n",
"Df Residuals: 168 BIC: 1796.\n",
"Df Model: 13 \n",
@@ -2002,7 +2720,7 @@
"\"\"\""
]
},
- "execution_count": 54,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -2012,16 +2730,6 @@
"model.summary()"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5fd3a0f3",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"id": "074c8e82",
@@ -2050,7 +2758,7 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 22,
"id": "8571c87e",
"metadata": {
"hidden": true
@@ -2073,7 +2781,7 @@
"Length: 182, dtype: float64"
]
},
- "execution_count": 55,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -2084,7 +2792,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 23,
"id": "a13c82cf",
"metadata": {
"hidden": true
@@ -2107,7 +2815,7 @@
"Length: 182, dtype: float64"
]
},
- "execution_count": 56,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -2118,7 +2826,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 24,
"id": "c0ee64b9",
"metadata": {
"hidden": true
@@ -2126,7 +2834,7 @@
"outputs": [
{
"data": {
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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -2144,16 +2852,6 @@
"ax.axhline(0, linestyle=':', linewidth=1);"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "adb6d2be",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"id": "5c6150f9",
@@ -2179,7 +2877,7 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 25,
"id": "c2b97f70",
"metadata": {
"hidden": true
@@ -2189,15 +2887,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/home/flaurent/Projects/scientific_python/lib/python3.10/site-packages/statsmodels/stats/outliers_influence.py:696: RuntimeWarning: invalid value encountered in sqrt\n",
- " return self.resid / sigma / np.sqrt(1 - hii)\n",
- "/home/flaurent/Projects/scientific_python/lib/python3.10/site-packages/statsmodels/stats/outliers_influence.py:716: RuntimeWarning: divide by zero encountered in divide\n",
- " cooks_d2 *= hii / (1 - hii)\n"
+ "/home/flaurent/Boxes/jammy-1/Projects/scientific_python/lib/python3.10/site-packages/statsmodels/stats/outliers_influence.py:848: RuntimeWarning: invalid value encountered in sqrt\n",
+ " return self.resid / sigma / np.sqrt(1 - hii)\n"
]
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -2214,7 +2910,7 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 26,
"id": "1a794460",
"metadata": {
"hidden": true
@@ -2232,22 +2928,22 @@
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.825</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 85.62</td>\n",
+ " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 94.83</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Date:</th> <td>Mon, 26 Sep 2022</td> <th> Prob (F-statistic):</th> <td>5.14e-61</td>\n",
+ " <th>Date:</th> <td>Mon, 21 Aug 2023</td> <th> Prob (F-statistic):</th> <td>1.54e-61</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Time:</th> <td>16:47:40</td> <th> Log-Likelihood: </th> <td> -790.26</td>\n",
+ " <th>Time:</th> <td>15:56:23</td> <th> Log-Likelihood: </th> <td> -786.39</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>No. Observations:</th> <td> 181</td> <th> AIC: </th> <td> 1603.</td>\n",
+ " <th>No. Observations:</th> <td> 180</td> <th> AIC: </th> <td> 1593.</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Df Residuals:</th> <td> 170</td> <th> BIC: </th> <td> 1638.</td>\n",
+ " <th>Df Residuals:</th> <td> 170</td> <th> BIC: </th> <td> 1625.</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Df Model:</th> <td> 10</td> <th> </th> <td> </td> \n",
+ " <th>Df Model:</th> <td> 9</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
@@ -2261,9 +2957,6 @@
" <th>Intercept</th> <td> -10.3426</td> <td> 4.469</td> <td> -2.314</td> <td> 0.022</td> <td> -19.164</td> <td> -1.521</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>C(Embarked)[T.Q]</th> <td> -26.2113</td> <td> 20.221</td> <td> -1.296</td> <td> 0.197</td> <td> -66.128</td> <td> 13.706</td>\n",
- "</tr>\n",
- "<tr>\n",
" <th>C(Embarked)[T.S]</th> <td> -3.3575</td> <td> 3.108</td> <td> -1.080</td> <td> 0.282</td> <td> -9.492</td> <td> 2.777</td>\n",
"</tr>\n",
"<tr>\n",
@@ -2293,19 +2986,62 @@
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
- " <th>Omnibus:</th> <td>114.735</td> <th> Durbin-Watson: </th> <td> 2.002</td> \n",
+ " <th>Omnibus:</th> <td>113.844</td> <th> Durbin-Watson: </th> <td> 2.003</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>1355.085</td> \n",
+ " <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>1330.344</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Skew:</th> <td> 2.116</td> <th> Prob(JB): </th> <td>5.58e-295</td>\n",
+ " <th>Skew:</th> <td> 2.110</td> <th> Prob(JB): </th> <td>1.32e-289</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Kurtosis:</th> <td>15.719</td> <th> Cond. No. </th> <td> 37.1</td> \n",
+ " <th>Kurtosis:</th> <td>15.632</td> <th> Cond. No. </th> <td> 15.9</td> \n",
"</tr>\n",
"</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\toprule\n",
+ "\\textbf{Dep. Variable:} & Fare & \\textbf{ R-squared: } & 0.834 \\\\\n",
+ "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.825 \\\\\n",
+ "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 94.83 \\\\\n",
+ "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Prob (F-statistic):} & 1.54e-61 \\\\\n",
+ "\\textbf{Time:} & 15:56:23 & \\textbf{ Log-Likelihood: } & -786.39 \\\\\n",
+ "\\textbf{No. Observations:} & 180 & \\textbf{ AIC: } & 1593. \\\\\n",
+ "\\textbf{Df Residuals:} & 170 & \\textbf{ BIC: } & 1625. \\\\\n",
+ "\\textbf{Df Model:} & 9 & \\textbf{ } & \\\\\n",
+ "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lcccccc}\n",
+ " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
+ "\\midrule\n",
+ "\\textbf{Intercept} & -10.3426 & 4.469 & -2.314 & 0.022 & -19.164 & -1.521 \\\\\n",
+ "\\textbf{C(Embarked)[T.S]} & -3.3575 & 3.108 & -1.080 & 0.282 & -9.492 & 2.777 \\\\\n",
+ "\\textbf{C(Deck)[T.A]} & 3.9948 & 5.504 & 0.726 & 0.469 & -6.871 & 14.860 \\\\\n",
+ "\\textbf{C(Deck)[T.B]} & 7.4801 & 4.939 & 1.514 & 0.132 & -2.270 & 17.230 \\\\\n",
+ "\\textbf{C(Deck)[T.C]} & 4.4306 & 4.379 & 1.012 & 0.313 & -4.213 & 13.075 \\\\\n",
+ "\\textbf{C(Deck)[T.D]} & -5.0043 & 5.085 & -0.984 & 0.326 & -15.043 & 5.034 \\\\\n",
+ "\\textbf{C(Deck)[T.E]} & -7.5249 & 5.559 & -1.354 & 0.178 & -18.499 & 3.449 \\\\\n",
+ "\\textbf{Cabins} & -0.3417 & 3.554 & -0.096 & 0.924 & -7.358 & 6.674 \\\\\n",
+ "\\textbf{Passengers} & 40.8216 & 2.369 & 17.229 & 0.000 & 36.145 & 45.499 \\\\\n",
+ "\\textbf{Children} & -40.5959 & 8.482 & -4.786 & 0.000 & -57.339 & -23.853 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\textbf{Omnibus:} & 113.844 & \\textbf{ Durbin-Watson: } & 2.003 \\\\\n",
+ "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 1330.344 \\\\\n",
+ "\\textbf{Skew:} & 2.110 & \\textbf{ Prob(JB): } & 1.32e-289 \\\\\n",
+ "\\textbf{Kurtosis:} & 15.632 & \\textbf{ Cond. No. } & 15.9 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "%\\caption{OLS Regression Results}\n",
+ "\\end{center}\n",
+ "\n",
+ "Notes: \\newline\n",
+ " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+ ],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
@@ -2313,18 +3049,17 @@
"==============================================================================\n",
"Dep. Variable: Fare R-squared: 0.834\n",
"Model: OLS Adj. R-squared: 0.825\n",
- "Method: Least Squares F-statistic: 85.62\n",
- "Date: Mon, 26 Sep 2022 Prob (F-statistic): 5.14e-61\n",
- "Time: 16:47:40 Log-Likelihood: -790.26\n",
- "No. Observations: 181 AIC: 1603.\n",
- "Df Residuals: 170 BIC: 1638.\n",
- "Df Model: 10 \n",
+ "Method: Least Squares F-statistic: 94.83\n",
+ "Date: Mon, 21 Aug 2023 Prob (F-statistic): 1.54e-61\n",
+ "Time: 15:56:23 Log-Likelihood: -786.39\n",
+ "No. Observations: 180 AIC: 1593.\n",
+ "Df Residuals: 170 BIC: 1625.\n",
+ "Df Model: 9 \n",
"Covariance Type: nonrobust \n",
"====================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------------\n",
"Intercept -10.3426 4.469 -2.314 0.022 -19.164 -1.521\n",
- "C(Embarked)[T.Q] -26.2113 20.221 -1.296 0.197 -66.128 13.706\n",
"C(Embarked)[T.S] -3.3575 3.108 -1.080 0.282 -9.492 2.777\n",
"C(Deck)[T.A] 3.9948 5.504 0.726 0.469 -6.871 14.860\n",
"C(Deck)[T.B] 7.4801 4.939 1.514 0.132 -2.270 17.230\n",
@@ -2335,10 +3070,10 @@
"Passengers 40.8216 2.369 17.229 0.000 36.145 45.499\n",
"Children -40.5959 8.482 -4.786 0.000 -57.339 -23.853\n",
"==============================================================================\n",
- "Omnibus: 114.735 Durbin-Watson: 2.002\n",
- "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1355.085\n",
- "Skew: 2.116 Prob(JB): 5.58e-295\n",
- "Kurtosis: 15.719 Cond. No. 37.1\n",
+ "Omnibus: 113.844 Durbin-Watson: 2.003\n",
+ "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1330.344\n",
+ "Skew: 2.110 Prob(JB): 1.32e-289\n",
+ "Kurtosis: 15.632 Cond. No. 15.9\n",
"==============================================================================\n",
"\n",
"Notes:\n",
@@ -2346,27 +3081,17 @@
"\"\"\""
]
},
- "execution_count": 61,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "firstclass_clean = firstclass.drop(labels=['PC 17755'])\n",
+ "firstclass_clean = firstclass.drop(labels=['19928', 'PC 17755'])\n",
"model = smf.ols('Fare ~ C(Embarked) + C(Deck) + Cabins + Passengers + Children', firstclass_clean).fit()\n",
"model.summary()"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "45574c5f",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"id": "94cb7b0a",
@@ -2389,7 +3114,7 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 27,
"id": "d2ec65b5",
"metadata": {
"hidden": true
@@ -2397,7 +3122,7 @@
"outputs": [
{
"data": {
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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -2420,16 +3145,6 @@
"ax.set_xlim(xmin, xmax);"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a16c3246",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"id": "6e8d2e6b",
@@ -2454,7 +3169,7 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": 28,
"id": "e58c6f4c",
"metadata": {
"hidden": true
@@ -2475,16 +3190,16 @@
" <th>Model Family:</th> <td>InverseGaussian</td> <th> Df Model: </th> <td> 10</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Link Function:</th> <td>log</td> <th> Scale: </th> <td>0.0016691</td>\n",
+ " <th>Link Function:</th> <td>Log</td> <th> Scale: </th> <td>0.0016691</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -725.75</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Date:</th> <td>Mon, 26 Sep 2022</td> <th> Deviance: </th> <td> 0.29921</td> \n",
+ " <th>Date:</th> <td>Mon, 21 Aug 2023</td> <th> Deviance: </th> <td> 0.29921</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Time:</th> <td>16:49:53</td> <th> Pearson chi2: </th> <td> 0.285</td> \n",
+ " <th>Time:</th> <td>15:56:23</td> <th> Pearson chi2: </th> <td> 0.285</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>34</td> <th> Pseudo R-squ. (CS):</th> <td>0.9871</td> \n",
@@ -2532,6 +3247,40 @@
"</tr>\n",
"</table>"
],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\toprule\n",
+ "\\textbf{Dep. Variable:} & Fare & \\textbf{ No. Observations: } & 182 \\\\\n",
+ "\\textbf{Model:} & GLM & \\textbf{ Df Residuals: } & 171 \\\\\n",
+ "\\textbf{Model Family:} & InverseGaussian & \\textbf{ Df Model: } & 10 \\\\\n",
+ "\\textbf{Link Function:} & Log & \\textbf{ Scale: } & 0.0016691 \\\\\n",
+ "\\textbf{Method:} & IRLS & \\textbf{ Log-Likelihood: } & -725.75 \\\\\n",
+ "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Deviance: } & 0.29921 \\\\\n",
+ "\\textbf{Time:} & 15:56:23 & \\textbf{ Pearson chi2: } & 0.285 \\\\\n",
+ "\\textbf{No. Iterations:} & 34 & \\textbf{ Pseudo R-squ. (CS):} & 0.9871 \\\\\n",
+ "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lcccccc}\n",
+ " & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
+ "\\midrule\n",
+ "\\textbf{Intercept} & 2.9337 & 0.071 & 41.130 & 0.000 & 2.794 & 3.074 \\\\\n",
+ "\\textbf{C(Embarked)[T.Q]} & -0.6461 & 0.397 & -1.628 & 0.104 & -1.424 & 0.132 \\\\\n",
+ "\\textbf{C(Embarked)[T.S]} & -0.1006 & 0.043 & -2.331 & 0.020 & -0.185 & -0.016 \\\\\n",
+ "\\textbf{C(Deck)[T.A]} & 0.1500 & 0.069 & 2.172 & 0.030 & 0.015 & 0.285 \\\\\n",
+ "\\textbf{C(Deck)[T.B]} & 0.1134 & 0.069 & 1.651 & 0.099 & -0.021 & 0.248 \\\\\n",
+ "\\textbf{C(Deck)[T.C]} & 0.0508 & 0.057 & 0.896 & 0.370 & -0.060 & 0.162 \\\\\n",
+ "\\textbf{C(Deck)[T.D]} & -0.0231 & 0.066 & -0.351 & 0.726 & -0.152 & 0.106 \\\\\n",
+ "\\textbf{C(Deck)[T.E]} & -0.0561 & 0.070 & -0.800 & 0.423 & -0.194 & 0.081 \\\\\n",
+ "\\textbf{Cabins} & -0.3206 & 0.056 & -5.727 & 0.000 & -0.430 & -0.211 \\\\\n",
+ "\\textbf{Passengers} & 0.8273 & 0.042 & 19.571 & 0.000 & 0.744 & 0.910 \\\\\n",
+ "\\textbf{Children} & -0.8798 & 0.209 & -4.201 & 0.000 & -1.290 & -0.469 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "%\\caption{Generalized Linear Model Regression Results}\n",
+ "\\end{center}"
+ ],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
@@ -2540,10 +3289,10 @@
"Dep. Variable: Fare No. Observations: 182\n",
"Model: GLM Df Residuals: 171\n",
"Model Family: InverseGaussian Df Model: 10\n",
- "Link Function: log Scale: 0.0016691\n",
+ "Link Function: Log Scale: 0.0016691\n",
"Method: IRLS Log-Likelihood: -725.75\n",
- "Date: Mon, 26 Sep 2022 Deviance: 0.29921\n",
- "Time: 16:49:53 Pearson chi2: 0.285\n",
+ "Date: Mon, 21 Aug 2023 Deviance: 0.29921\n",
+ "Time: 15:56:23 Pearson chi2: 0.285\n",
"No. Iterations: 34 Pseudo R-squ. (CS): 0.9871\n",
"Covariance Type: nonrobust \n",
"====================================================================================\n",
@@ -2564,19 +3313,19 @@
"\"\"\""
]
},
- "execution_count": 65,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "model = smf.glm('Fare ~ C(Embarked) + C(Deck) + Cabins + Passengers + Children', firstclass, family=sm.families.InverseGaussian(sm.families.links.log())).fit()\n",
+ "model = smf.glm('Fare ~ C(Embarked) + C(Deck) + Cabins + Passengers + Children', firstclass, family=sm.families.InverseGaussian(sm.families.links.Log())).fit()\n",
"model.summary()"
]
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 29,
"id": "c9767999",
"metadata": {
"hidden": true
@@ -2819,7 +3568,7 @@
"E-D 0.958609 False "
]
},
- "execution_count": 66,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
@@ -2850,7 +3599,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 30,
"id": "5fdcbbd8",
"metadata": {
"hidden": true
@@ -2866,7 +3615,7 @@
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 31,
"id": "7e1e4b40",
"metadata": {
"hidden": true
@@ -2887,16 +3636,16 @@
" <th>Model Family:</th> <td>InverseGaussian</td> <th> Df Model: </th> <td> 7</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Link Function:</th> <td>log</td> <th> Scale: </th> <td>0.0016739</td>\n",
+ " <th>Link Function:</th> <td>Log</td> <th> Scale: </th> <td>0.0016739</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -726.86</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Date:</th> <td>Mon, 26 Sep 2022</td> <th> Deviance: </th> <td> 0.30293</td> \n",
+ " <th>Date:</th> <td>Mon, 21 Aug 2023</td> <th> Deviance: </th> <td> 0.30293</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Time:</th> <td>16:56:30</td> <th> Pearson chi2: </th> <td> 0.291</td> \n",
+ " <th>Time:</th> <td>15:56:23</td> <th> Pearson chi2: </th> <td> 0.291</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>33</td> <th> Pseudo R-squ. (CS):</th> <td>0.9868</td> \n",
@@ -2935,6 +3684,37 @@
"</tr>\n",
"</table>"
],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\toprule\n",
+ "\\textbf{Dep. Variable:} & Fare & \\textbf{ No. Observations: } & 182 \\\\\n",
+ "\\textbf{Model:} & GLM & \\textbf{ Df Residuals: } & 174 \\\\\n",
+ "\\textbf{Model Family:} & InverseGaussian & \\textbf{ Df Model: } & 7 \\\\\n",
+ "\\textbf{Link Function:} & Log & \\textbf{ Scale: } & 0.0016739 \\\\\n",
+ "\\textbf{Method:} & IRLS & \\textbf{ Log-Likelihood: } & -726.86 \\\\\n",
+ "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Deviance: } & 0.30293 \\\\\n",
+ "\\textbf{Time:} & 15:56:23 & \\textbf{ Pearson chi2: } & 0.291 \\\\\n",
+ "\\textbf{No. Iterations:} & 33 & \\textbf{ Pseudo R-squ. (CS):} & 0.9868 \\\\\n",
+ "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lcccccc}\n",
+ " & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
+ "\\midrule\n",
+ "\\textbf{Intercept} & 2.9418 & 0.070 & 42.150 & 0.000 & 2.805 & 3.079 \\\\\n",
+ "\\textbf{C(Embarked)[T.Q]} & -0.6779 & 0.397 & -1.708 & 0.088 & -1.456 & 0.100 \\\\\n",
+ "\\textbf{C(Embarked)[T.S]} & -0.1094 & 0.043 & -2.565 & 0.010 & -0.193 & -0.026 \\\\\n",
+ "\\textbf{C(Deck)[T.ABC]} & 0.0954 & 0.048 & 1.999 & 0.046 & 0.002 & 0.189 \\\\\n",
+ "\\textbf{C(Deck)[T.DE]} & -0.0357 & 0.055 & -0.646 & 0.518 & -0.144 & 0.073 \\\\\n",
+ "\\textbf{Cabins} & -0.3121 & 0.054 & -5.732 & 0.000 & -0.419 & -0.205 \\\\\n",
+ "\\textbf{Passengers} & 0.8175 & 0.042 & 19.612 & 0.000 & 0.736 & 0.899 \\\\\n",
+ "\\textbf{Children} & -0.8372 & 0.210 & -3.982 & 0.000 & -1.249 & -0.425 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "%\\caption{Generalized Linear Model Regression Results}\n",
+ "\\end{center}"
+ ],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
@@ -2943,10 +3723,10 @@
"Dep. Variable: Fare No. Observations: 182\n",
"Model: GLM Df Residuals: 174\n",
"Model Family: InverseGaussian Df Model: 7\n",
- "Link Function: log Scale: 0.0016739\n",
+ "Link Function: Log Scale: 0.0016739\n",
"Method: IRLS Log-Likelihood: -726.86\n",
- "Date: Mon, 26 Sep 2022 Deviance: 0.30293\n",
- "Time: 16:56:30 Pearson chi2: 0.291\n",
+ "Date: Mon, 21 Aug 2023 Deviance: 0.30293\n",
+ "Time: 15:56:23 Pearson chi2: 0.291\n",
"No. Iterations: 33 Pseudo R-squ. (CS): 0.9868\n",
"Covariance Type: nonrobust \n",
"====================================================================================\n",
@@ -2964,19 +3744,19 @@
"\"\"\""
]
},
- "execution_count": 73,
+ "execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "model = smf.glm('Fare ~ C(Embarked) + C(Deck) + Cabins + Passengers + Children', firstclass, family=sm.families.InverseGaussian(sm.families.links.log())).fit()\n",
+ "model = smf.glm('Fare ~ C(Embarked) + C(Deck) + Cabins + Passengers + Children', firstclass, family=sm.families.InverseGaussian(sm.families.links.Log())).fit()\n",
"model.summary()"
]
},
{
"cell_type": "code",
- "execution_count": 74,
+ "execution_count": 32,
"id": "ebc787b2",
"metadata": {
"hidden": true
@@ -3063,7 +3843,7 @@
"DE-ABC -0.031936 0.028389 True "
]
},
- "execution_count": 74,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -3072,16 +3852,6 @@
"model.t_test_pairwise('C(Deck)').result_frame"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "625feb31",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"id": "36556c9e",
@@ -3106,7 +3876,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 33,
"id": "63983757",
"metadata": {
"hidden": true
@@ -3120,15 +3890,15 @@
" 'Fare ~ C(Embarked) * Cabins + C(Deck) + Passengers + Children': 1450.589879261779,\n",
" 'Fare ~ C(Embarked) * Passengers + C(Deck) + Cabins + Children': 1466.6673025135624,\n",
" 'Fare ~ C(Embarked) * Children + C(Deck) + Cabins + Passengers': 1471.730470133521,\n",
- " 'Fare ~ C(Deck) * Cabins + C(Embarked) + Passengers + Children': 1471.7331290112245,\n",
- " 'Fare ~ C(Deck) * Passengers + C(Embarked) + Cabins + Children': 1472.4105103002053,\n",
- " 'Fare ~ C(Deck) * Children + C(Embarked) + Cabins + Passengers': 1471.7304701335215,\n",
- " 'Fare ~ Cabins * Passengers + C(Embarked) + C(Deck) + Children': 1442.7498384491118,\n",
+ " 'Fare ~ C(Deck) * Cabins + C(Embarked) + Passengers + Children': 1471.7331282583878,\n",
+ " 'Fare ~ C(Deck) * Passengers + C(Embarked) + Cabins + Children': 1472.4105103002057,\n",
+ " 'Fare ~ C(Deck) * Children + C(Embarked) + Cabins + Passengers': 1471.7304701335213,\n",
+ " 'Fare ~ Cabins * Passengers + C(Embarked) + C(Deck) + Children': 1442.7498384491114,\n",
" 'Fare ~ Cabins * Children + C(Embarked) + C(Deck) + Passengers': 1471.6186668619553,\n",
" 'Fare ~ Passengers * Children + C(Embarked) + C(Deck) + Cabins': 1470.8386820677097}"
]
},
- "execution_count": 24,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -3146,7 +3916,7 @@
" if k not in (i, j):\n",
" model_terms.append(terms[k])\n",
" model_spec = ' + '.join(model_terms)\n",
- " fitted_model = smf.glm(model_spec, firstclass, family=sm.families.InverseGaussian(sm.families.links.log())).fit()\n",
+ " fitted_model = smf.glm(model_spec, firstclass, family=sm.families.InverseGaussian(sm.families.links.Log())).fit()\n",
" model_aic[model_spec] = fitted_model.aic\n",
" return model_aic\n",
"\n",
@@ -3156,7 +3926,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 34,
"id": "173f5ca5",
"metadata": {
"hidden": true
@@ -3165,15 +3935,15 @@
{
"data": {
"text/plain": [
- "{'Fare ~ C(Embarked) * C(Deck) + Cabins * Passengers + Children': 1446.202974050631,\n",
- " 'Fare ~ C(Embarked) * Cabins * Passengers + C(Deck) + Children': 1399.9999464641298,\n",
+ "{'Fare ~ C(Embarked) * C(Deck) + Cabins * Passengers + Children': 1446.2029740506312,\n",
+ " 'Fare ~ C(Embarked) * Cabins * Passengers + C(Deck) + Children': 1399.9999464641296,\n",
" 'Fare ~ C(Embarked) * Children + C(Deck) + Cabins * Passengers': 1440.048662430043,\n",
- " 'Fare ~ C(Deck) * Cabins * Passengers + C(Embarked) + Children': 1432.1006575382407,\n",
+ " 'Fare ~ C(Deck) * Cabins * Passengers + C(Embarked) + Children': 1432.100822258053,\n",
" 'Fare ~ C(Deck) * Children + C(Embarked) + Cabins * Passengers': 1440.0486624300434,\n",
- " 'Fare ~ Cabins * Passengers * Children + C(Embarked) + C(Deck)': 1437.8075309435865}"
+ " 'Fare ~ Cabins * Passengers * Children + C(Embarked) + C(Deck)': 1437.8075309435867}"
]
},
- "execution_count": 25,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@@ -3185,7 +3955,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 35,
"id": "487aa1b3",
"metadata": {
"hidden": true
@@ -3194,12 +3964,12 @@
{
"data": {
"text/plain": [
- "{'Fare ~ C(Embarked) * Cabins * Passengers * C(Deck) + Children': 1416.4827989888995,\n",
- " 'Fare ~ C(Embarked) * Cabins * Passengers * Children + C(Deck)': 1403.1712552041527,\n",
- " 'Fare ~ C(Deck) * Children + C(Embarked) * Cabins * Passengers': 1402.1020940398735}"
+ "{'Fare ~ C(Embarked) * Cabins * Passengers * C(Deck) + Children': 1416.4827989799123,\n",
+ " 'Fare ~ C(Embarked) * Cabins * Passengers * Children + C(Deck)': 1403.1712552041522,\n",
+ " 'Fare ~ C(Deck) * Children + C(Embarked) * Cabins * Passengers': 1402.1020940398741}"
]
},
- "execution_count": 26,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -3219,6 +3989,7 @@
"Draw a [stripplot](https://seaborn.pydata.org/generated/seaborn.stripplot.html) of the AIC for the various models explored.\n",
"\n",
"Example:\n",
+ "\n",
"<img src=\"images/stripplot.png\" />"
]
},
@@ -3234,7 +4005,7 @@
},
{
"cell_type": "code",
- "execution_count": 81,
+ "execution_count": 36,
"id": "6102bdce",
"metadata": {
"hidden": true
@@ -3248,24 +4019,24 @@
" 'Fare ~ C(Embarked) * Cabins + C(Deck) + Passengers + Children': 1450.589879261779,\n",
" 'Fare ~ C(Embarked) * Passengers + C(Deck) + Cabins + Children': 1466.6673025135624,\n",
" 'Fare ~ C(Embarked) * Children + C(Deck) + Cabins + Passengers': 1471.730470133521,\n",
- " 'Fare ~ C(Deck) * Cabins + C(Embarked) + Passengers + Children': 1471.7331290112245,\n",
- " 'Fare ~ C(Deck) * Passengers + C(Embarked) + Cabins + Children': 1472.4105103002053,\n",
- " 'Fare ~ C(Deck) * Children + C(Embarked) + Cabins + Passengers': 1471.7304701335215,\n",
- " 'Fare ~ Cabins * Passengers + C(Embarked) + C(Deck) + Children': 1442.7498384491118,\n",
+ " 'Fare ~ C(Deck) * Cabins + C(Embarked) + Passengers + Children': 1471.7331282583878,\n",
+ " 'Fare ~ C(Deck) * Passengers + C(Embarked) + Cabins + Children': 1472.4105103002057,\n",
+ " 'Fare ~ C(Deck) * Children + C(Embarked) + Cabins + Passengers': 1471.7304701335213,\n",
+ " 'Fare ~ Cabins * Passengers + C(Embarked) + C(Deck) + Children': 1442.7498384491114,\n",
" 'Fare ~ Cabins * Children + C(Embarked) + C(Deck) + Passengers': 1471.6186668619553,\n",
" 'Fare ~ Passengers * Children + C(Embarked) + C(Deck) + Cabins': 1470.8386820677097,\n",
- " 'Fare ~ C(Embarked) * C(Deck) + Cabins * Passengers + Children': 1446.202974050631,\n",
- " 'Fare ~ C(Embarked) * Cabins * Passengers + C(Deck) + Children': 1399.9999464641298,\n",
+ " 'Fare ~ C(Embarked) * C(Deck) + Cabins * Passengers + Children': 1446.2029740506312,\n",
+ " 'Fare ~ C(Embarked) * Cabins * Passengers + C(Deck) + Children': 1399.9999464641296,\n",
" 'Fare ~ C(Embarked) * Children + C(Deck) + Cabins * Passengers': 1440.048662430043,\n",
- " 'Fare ~ C(Deck) * Cabins * Passengers + C(Embarked) + Children': 1432.1006575382407,\n",
+ " 'Fare ~ C(Deck) * Cabins * Passengers + C(Embarked) + Children': 1432.100822258053,\n",
" 'Fare ~ C(Deck) * Children + C(Embarked) + Cabins * Passengers': 1440.0486624300434,\n",
- " 'Fare ~ Cabins * Passengers * Children + C(Embarked) + C(Deck)': 1437.8075309435865,\n",
- " 'Fare ~ C(Embarked) * Cabins * Passengers * C(Deck) + Children': 1416.4827989888995,\n",
- " 'Fare ~ C(Embarked) * Cabins * Passengers * Children + C(Deck)': 1403.1712552041527,\n",
- " 'Fare ~ C(Deck) * Children + C(Embarked) * Cabins * Passengers': 1402.1020940398735}"
+ " 'Fare ~ Cabins * Passengers * Children + C(Embarked) + C(Deck)': 1437.8075309435867,\n",
+ " 'Fare ~ C(Embarked) * Cabins * Passengers * C(Deck) + Children': 1416.4827989799123,\n",
+ " 'Fare ~ C(Embarked) * Cabins * Passengers * Children + C(Deck)': 1403.1712552041522,\n",
+ " 'Fare ~ C(Deck) * Children + C(Embarked) * Cabins * Passengers': 1402.1020940398741}"
]
},
- "execution_count": 81,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
@@ -3284,7 +4055,7 @@
},
{
"cell_type": "code",
- "execution_count": 82,
+ "execution_count": 37,
"id": "29b1f1d6",
"metadata": {
"hidden": true
@@ -3344,7 +4115,7 @@
" <tr>\n",
" <th>5</th>\n",
" <td>Fare ~ C(Deck) * Cabins + C(Embarked) + Passen...</td>\n",
- " <td>1471.733129</td>\n",
+ " <td>1471.733128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
@@ -3389,7 +4160,7 @@
" <tr>\n",
" <th>14</th>\n",
" <td>Fare ~ C(Deck) * Cabins * Passengers + C(Embar...</td>\n",
- " <td>1432.100658</td>\n",
+ " <td>1432.100822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
@@ -3427,7 +4198,7 @@
"2 Fare ~ C(Embarked) * Cabins + C(Deck) + Passen... 1450.589879\n",
"3 Fare ~ C(Embarked) * Passengers + C(Deck) + Ca... 1466.667303\n",
"4 Fare ~ C(Embarked) * Children + C(Deck) + Cabi... 1471.730470\n",
- "5 Fare ~ C(Deck) * Cabins + C(Embarked) + Passen... 1471.733129\n",
+ "5 Fare ~ C(Deck) * Cabins + C(Embarked) + Passen... 1471.733128\n",
"6 Fare ~ C(Deck) * Passengers + C(Embarked) + Ca... 1472.410510\n",
"7 Fare ~ C(Deck) * Children + C(Embarked) + Cabi... 1471.730470\n",
"8 Fare ~ Cabins * Passengers + C(Embarked) + C(D... 1442.749838\n",
@@ -3436,7 +4207,7 @@
"11 Fare ~ C(Embarked) * C(Deck) + Cabins * Passen... 1446.202974\n",
"12 Fare ~ C(Embarked) * Cabins * Passengers + C(D... 1399.999946\n",
"13 Fare ~ C(Embarked) * Children + C(Deck) + Cabi... 1440.048662\n",
- "14 Fare ~ C(Deck) * Cabins * Passengers + C(Embar... 1432.100658\n",
+ "14 Fare ~ C(Deck) * Cabins * Passengers + C(Embar... 1432.100822\n",
"15 Fare ~ C(Deck) * Children + C(Embarked) + Cabi... 1440.048662\n",
"16 Fare ~ Cabins * Passengers * Children + C(Emba... 1437.807531\n",
"17 Fare ~ C(Embarked) * Cabins * Passengers * C(D... 1416.482799\n",
@@ -3444,7 +4215,7 @@
"19 Fare ~ C(Deck) * Children + C(Embarked) * Cabi... 1402.102094"
]
},
- "execution_count": 82,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
@@ -3456,7 +4227,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 38,
"id": "70bda649",
"metadata": {
"hidden": true
@@ -3464,7 +4235,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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+//xzqV27dhrLqDb8/Pyk6dOnaxX2r7/+kgBp0aJFRYatWrWq9Mknn8j/5+bmShUrVpRWrlwpSVL+a/r1MtivXz+pY8eOanH27dtXMjc3l/+fPn26VKZMGenhw4fyZ5GRkZKZmVm+78EaNWpIoaGh8nbGxsZSamqqvP7LL7+UmjZtWnQm5FGqPe55W+8EQRAE4b9OmaOkmXdDzh66VHTg/6+ZdwOUOcr34vtSlQYzMzPMzMyKDN+2bVtWrlwp/29iYgK8em54zpw5XLt2jdTUVHJycsjIyCA9PV0eiaevr4+rq6u8bUxMDEqlEkdHR7V9ZGZmFtiDBzBmzBhatmzJpEmTuHnzJlu2bKFx48ZkZ2djZWXFzJkzC9xW0nLSI9VICNWwybxezwNA7oFRyXuclSpVAsDFxUXts4yMDFJTU+V8t7Ozo0qVKnKY5s2bk5uby/Xr17G2tuavv/5iypQpRERE8PDhQ5RKJenp6SQnJ6vtu6BRAp6enjRp0oRt27bJvUovXrwgMTGRwYMHExAQIIfNycmRJxmLi4vD1dVVLS+aN2+ucR9GRkbyKA5NIiMj6dChg/x/VlYWkiSxY8cO+bPQ0FB8fX01bl+c86fp3BVEFa+qhzQ6OppTp06p9ZwplUq5TF+5coUPPvggX9l9PQ2tWrWif//+Bc6M/b7kV3x8PNOmTSMqKopHjx7J808kJyerTfyV97zr6enRqFEj4uLiCozX2NiYGjVqyP/b2Njw8OFD4NU14+/vj7e3N56ennh4eNCnTx9sbGwKjK9OnTry8HPVseXtoW3VqpXaKBZN+vXrh66uLi9fvqRChQqsXbsWV1dXlEolwcHBbN++nXv37pGVlUVmZqZ8/6pXrx7t27fHxcUFb29vvLy86NWrF+XLly/yWIoqT6p95L1vmJiYYGZmJufX9evXady4sdqxNGnSRO3/6Ohofv/9d3mUjiqfcnNzuXXrFs7OzkD+e4S/vz+enp7UqlULHx8fOnfujJeXV4F5uGnTJj777DP5/8zMTBQKBQsXLpQ/O3jwIK1atcq3rbZlUiVvnigUCqytreU8KUpcXBw9evRQ+6x58+YcOnRI7bOqVatSoUIF+f/o6GjS0tLyfQ+9fPlS7RESe3t7tdFOecu3tsR73AVBEAThHdHV06WJtxvmVmZaTVBXroIZTbzcNA6p/TcwMTGhZs2aap8lJSXRuXNnhg8fzuzZs7GwsODkyZMMHjyYrKws+UepkZGR2jDYtLQ0dHV1uXjxYr78eH24ZF7r16+Xh7S7uLjQrVs3MjMzefr0KdbW1oWmX1XRunbtWoGVTwArKysAnj59qvaDrqA8eF3eRhnVMWv6rDgT9Pn5+fH48WOWLl1K1apVMTAwoHnz5vkmU1I1pryuU6dO7Ny5k9jYWLkRQTUPwZo1a2jatKla+H9SRp88eaJWUXtdo0aN1J5HXbZsGffu3WPevHnyZ6qGDk0cHR25du1akemwsrLi6dOn2iUa5MqnaqLFtLQ0Zs6cSc+ePfOFNTQ0lIdYF8bAwAAPDw/27dvHl19+qdYoo/K+5FeXLl2oWrUqa9asoXLlyuTm5lK3bt03nqjr9cZJhUKhVnELDw8nMDCQQ4cOsW3bNqZMmcLRo0c1zk0Ar+YuUM3Lce/ePdzd3dXyR5vzsnjxYjw8PDA3N1e7thcsWMDSpUtZsmSJPF/H6NGj5TzQ1dXl6NGjnD59miNHjrB8+XKCgoKIioqiWrVqhR5LUeWpsPwqzj0iLS2Nzz77jMDAwHzr7Ozs5L9fv0c0aNCAW7ducfDgQY4dO0afPn3w8PBQayDKq2vXrmr3i4kTJ1KlShW1/Woq7wAVKlSgXLlyWpVLePM80cbr+ZGWloaNjY3G+VbyvkrubaRNVNwFQRAE4Z1SMDx4IPOHryj0lXA6ujoMm+0HvLtnON+FixcvkpubS0hIiPw87Pbt24vczs3NDaVSycOHDzX2zBRE0zt4VRPGFcXLywsrKyvmz5/P7t27861/9uwZ5cqVo0aNGpiZmREbG1tor+rblJyczJ9//ik/93n27Fl0dHTkZzlPnTrFihUr6NixI/Dq2elHjx5pHf/cuXMpW7Ys7du3JyIigtq1a1OpUiUqV67MzZs3C+y1dXZ2ZsOGDWRkZMiVjLNnz2oMe/XqVXr16lVgGoyMjNQaPSwsLEhNTS2yIUSlf//+TJ48mcuXL+d7bjs7O5usrCxMTExwc3Nj48aNWsWZm5srz8+girNBgwZcv369wHS5urpy9+5dbty4UWD50NHRYcOGDfTv35+2bdsSERGR75ne9yG/MjIyuH79OmvWrJGvw5MnT2qM7+zZs7Ru3Rp4NSrj4sWLjBo1Squ0FMTNzQ03NzcmTZpE8+bN2bx5c4EV97xveFDNJK5tXqhYW1tr3ObUqVN069aNTz75BHhVLm7cuEHt2rXlMAqFgpYtW9KyZUumTZtG1apV2b17N2PHji30WIoqT9qoVasWBw4cUPvs/Pnzav83aNCA2NjYf7QfMzMz+vbtS9++fenVqxc+Pj48efIk32giAFNTU7WeZlNTUywsLLTar46ODh9//DEbNmxg+vTp+a6JtLQ0DA0N38pM8c7OzkRFRal9VtC9K68GDRrw4MED9PT05NdJlhTxOjhBEARBeId0dXWo16ouE1aOoFwFzcPNy1UwY8LKEdRrVQdd3f/WV3XNmjXJzs5m+fLl3Lx5kw0bNrBq1aoit3N0dMTX15eBAweya9cubt26xblz55gzZw779+8vkbSamJgQFhbG/v376dq1K8eOHSMpKYkLFy4wYcIEhg0bBrz6cenh4aGxApOZmcmDBw/UluJUoAtiaGiIn58f0dHRREZGEhgYSJ8+feQGCQcHBzZs2EBcXBxRUVH4+vpq1cOY18KFC/H19aVdu3Zyj9fMmTOZM2cOy5Yt48aNG8TExBAeHi6/Y7x///4oFAoCAgKIjY3lwIEDakNiVZKSkrh37x4eHh5vmBMFGz16NC1btqR9+/Z89913REdHc/PmTbZv306zZs3k97F7e3vzxx9/aOx1f/z4MQ8ePODmzZvymwXOnTunNnHctGnTWL9+PTNnzuSPP/4gLi6OrVu3MmXKFADatGlD69at+eijjzh69KjcW/n6EFxdXV02bdpEvXr1aNeuHQ8ePJDXvS/5Vb58eSwtLVm9ejUJCQmcOHFCroi+7rvvvmP37t1cu3aNkSNH8vTpUz799NN/lLZbt24xadIkzpw5w+3btzly5Ajx8fHycO53zcHBQe5Rj4uL47PPPuOvv/6S10dFRREcHMyFCxdITk5m165d/P333zg7Oxd5LEWVJ2189tlnXLt2jYkTJ3Ljxg22b9/OunXrgP8bwTNx4kROnz7NqFGjuHLlCvHx8fz8889FNq4sWrSILVu2cO3aNW7cuMGPP/6ItbW1xkbSt2H27NnY2trStGlT1q9fT2xsLPHx8Xz//fe4ubmpvZHkTahGQCxcuJD4+Hi+/fbbfNeoJh4eHjRv3pzu3btz5MgRkpKSOH36NEFBQVy4cOGtpE1WrCfi3xLV5HQpKSmlsXtBEARBKHU5OUopJydHOn3gvLToi1Bplv9iadEXodLpA+elnJwcKSdHWdpJzKc4399+fn5St27dNK5btGiRZGNjIxkZGUne3t7S+vXr801UlXdCIJWsrCxp2rRpkr29vVSmTBnJxsZG6tGjh/T777+/wVEV7fz581LPnj2lChUqSAYGBlLNmjWloUOHSvHx8XKYAwcOSFWqVJGUyv87b35+fhKQb6lVq5YchjwTYEmS5knRXp9Eafr06VK9evWkFStWSJUrV5YMDQ2lXr16qU2id+nSJalRo0aSoaGh5ODgIP34449S1apVpcWLFxe4b037kqRXk1HZ2NjIk6tt2rRJql+/vqSvry+VL19eat26tbRr1y45/JkzZ6R69epJ+vr6Uv369aWdO3fmO6bg4GDJ29tbm+yXFXeyNUmSpIyMDGnOnDmSi4uLZGhoKFlYWEgtW7aU1q1bJ2VnZ8vhmjRpIq1atUr+X3UeVIuxsbHk7OwsjRgxQu28qxw6dEhq0aKFZGRkJJmZmUlNmjSRVq9eLa9//PixNGjQIMnS0lIyNDSU6tatK+3bt0+SpPzlPTs7W+rZs6fk7Ows/fXXX5IkvV/5dfToUcnZ2VkyMDCQXF1dpYiICLWypMq7zZs3S02aNJH09fWl2rVrSydOnJD3o2lyutev+d27d8sTyj148EDq3r27ZGNjI+nr60tVq1aVpk2bpna9FeZNJ6d73ePHj6Vu3bpJZcuWlSpWrChNmTJFGjhwoHzPi42Nlby9veV7hqOjo7R8+XKtj6Wo8qQpbebm5lJ4eLj8/88//yzVrFlTMjAwkNzd3eVJIvNOHHnu3DnJ09NTKlu2rGRiYiK5urqqTZ75+j1DkiRp9erVUv369SUTExPJzMxMat++vXTp0iWt87U4k9OpPHv2TPrqq68kBwcHSV9fX6pUqZLk4eEh7d69W54kT1Na69WrJ++rqMnpJEmS1q5dK33wwQeSkZGR1KVLF2nhwoX5JqerV69evvSlpqZKn3/+uVS5cmWpTJkykq2treTr6ytPWKppu8WLF0tVq1YtVj4oJKmYT/2/BampqZibm5OSkqLV5DaCIAiC8F+lzFGiq6db4P/vE/H9XTBJkmjatCljxoyhX79+pZ2c91pWVhYODg5s3ryZli1blnZyANi/fz9ffvklV69eLfD1gKXlfcwv4d9n9uzZrFq1qsRe3SaUPPGMuyAIgiCUotcr6e9rpV0onEKhYPXq1cTExJR2Ut57ycnJTJ48+b2qhHbq1In4+Hju3buHra1taSdHzfuYX8L7b8WKFTRu3BhLS0tOnTrFggUL3niOAaF0iR53QRAEQRC0Ir6/BUEQ/h3GjBnDtm3bePLkCXZ2dgwYMIBJkya9lYnchNIhKu6CIAiCIGhFfH8LgiAIQul4vx7iEQRBEARBEARBEARBjai4C4IgCMJ/hDJHWej/giAIgiD8O4mHHARBEAThXy5XmYuExMWjV7hw5BLpqekYmxnTyKsBDb3qo0CBzn/sffCCIAiC8L9EfIsLgiAIwr+YlCtx9VQs49oGsWrcWi4cvkzsmetcOHyZVePWMq5tEFdPxSLlvvMpbf7nTJ06laFDh5b4fmbMmEH9+vVLJO6IiAgUCgXPnj17a3EmJSWhUCi4cuUKALGxsXzwwQe8ePHire2juAYMGEBwcHCp7FuhUPDTTz8VuL5Zs2bs3Lnz3SXoDbm7uzN69OhCw9jb27NkyZJ3kh7hv0+be+Dr5VKbMljUtVnaRMVdEARBEP6lcpW5xJz8g2UjV5H6KFVjmNRHqSwbuYqYk3+Qq8x9Z2nz9/dHoVDkWxISEt5ZGt6Wy5cv07t3bypVqoShoSEODg4EBARw48YNOcyDBw9YunQpQUFB8mcF5YGPj09pHMZ7o3bt2jRr1oxFixZpFd7f35+kpCStwv7yyy907NgRS0tLjI2NqV27NuPGjePevXtymOjoaA4cOEBgYKD8mbu7u3x+DAwMqFKlCl26dGHXrl3FOra3YcqUKXz11Vfk5pb89apNfr0N58+ffyeNWv9U3mtVX1+fmjVr8vXXX5OTk1PaSfuftHPnTtzd3TE3N6ds2bK4urry9ddf8+TJE63j2LVrF7NmzSrBVL57ouIuCIIgCP9SEhLfT9lYZIU8V5lL+JSNvOs+dx8fH+7fv6+2VKtWrdjxKJXKd1KJ0WTfvn00a9aMzMxMNm3aRFxcHBs3bsTc3JypU6fK4cLCwmjRogVVq1ZV215THmzZsuVdH4ZWsrOz39m+Bg0axMqVKwusGD158oTvvvuOvC8/SkxMZNOmTQXGGRoaioeHB9bW1uzcuZPY2FhWrVpFSkoKISEhcrjly5fTu3dvypYtq7Z9QEAA9+/fJzExkZ07d1K7dm0+/vjjd17h7NChA8+fP+fgwYNab6Ma1VAc2ubX21ChQgWMjY3fapyFcXd3Z926dcXaRnWtxsfHM27cOGbMmMGCBQtKJoH/AllZWW8cR0REBPb29sXaJigoiL59+9K4cWMOHjzI1atXCQkJITo6mg0bNmgdj4WFBaampsVMceHe5T1SE1FxFwRBEIR/IWWOkotHrhTY0/66lEepXDp6+Z1OWGdgYIC1tbXaoqury6JFi3BxccHExARbW1tGjBhBWlqavN26desoV64ce/bsoXbt2hgYGJCcnExmZibjx4+nSpUqmJiY0LRpUyIiIkos/enp6QwaNIiOHTuyZ88ePDw8qFatGk2bNmXhwoWEhobKYbdu3UqXLl20yoPy5cvL6xUKBaGhoXTu3BljY2OcnZ05c+YMCQkJuLu7Y2JiQosWLUhMTMwXd2hoKLa2thgbG9OnTx9SUlLkdefPn8fT0xMrKyvMzc1p06YNly5dUtteoVCwcuVKunbtiomJCbNnz9aYBx06dKBly5by8PmwsDCcnZ0xNDTEycmJFStWqG1z7tw53NzcMDQ0pFGjRly+fDlfvJ6enjx58oRff/1VY94bGhpy7949fHx8uHv3LqtWrcLf37/Ahp+7d+8SGBhIYGAg33//Pe7u7tjb29O6dWvCwsKYNm0a8KoRaMeOHRrPlbGxMdbW1nzwwQc0a9aMefPmERoaypo1azh27Jgc7s6dO/Tp04dy5cphYWFBt27d8o0I+P7776lTpw4GBgbY2NgwatQojekGmD59OjY2Nvz+++8A6Orq0rFjR7Zu3VrgNm9K2/x6/Pgx/fr1o0qVKhgbG+Pi4qKx4SknJ4dRo0Zhbm6OlZUVU6dOVWt0eX2YskKhICwsjB49emBsbIyDgwN79uyR1z99+hRfX18qVKiAkZERDg4OhIeHl1h+wP9dq1WrVmX48OF4eHjIaSrqnnX79m26dOlC+fLlMTExoU6dOhw4cECrYymqPPn7+9O9e3cWLlyIjY0NlpaWjBw5Uq0Sef/+fTp16oSRkRHVqlVj8+bN+fL82bNnDBkyhAoVKmBmZka7du2Ijo6W16uGn4eFhVGtWjUMDQ0B2LFjBy4uLhgZGWFpaYmHh0eJPeZy7tw5goODCQkJYcGCBbRo0QJ7e3s8PT3ZuXMnfn5+auE3bNiAvb095ubmfPzxxzx//lxeV9QjHPHx8bRu3RpDQ0Nq167N0aNH1darGsO2bdtGmzZtMDQ0lBsOC7sHqrbbtWsXbdu2xdjYmHr16nHmzJk3zp9SnZwuOzu71FsuBEEQBOHfqEyZMlw4cqnogHlcOHKZxj4N//F3r2q71FT1xgIDAwMMDAy0jkdHR4dly5ZRrVo1bt68yYgRI5gwYYLaj5/09HTmzZtHWFgYlpaWVKxYkVGjRhEbG8vWrVupXLkyu3fvxsfHh5iYGBwcHP7RMeXm5qKjo7kf4/Dhwzx69IgJEyZoXF+uXDngVe9wbGwsjRo1+kdpmDVrFosWLWLRokVMnDiR/v37U716dSZNmoSdnR2ffvopo0aNUuuBTUhIYPv27ezdu5fU1FQGDx7MiBEj5B+Wz58/x8/Pj+XLlyNJEiEhIXTs2JH4+Hi1XqgZM2Ywd+5clixZgp6eHjdv3pTXPXv2jE6dOlG2bFmOHj2KsbExmzZtYtq0aXz77be4ublx+fJlAgICMDExwc/Pj7S0NDp37oynpycbN27k1q1bfPHFF/mOWV9fn/r16xMZGUn79u3zrTc2NiY4OJgDBw7QtWtXcnJyOHHiBGXKlNGYhz/++CNZWVlFnqvff/+dlJQUrc+Vn58f48aNY9euXXh4eJCdnY23tzfNmzcnMjISPT09vvnmG3x8fPj999/R19dn5cqVjB07lrlz59KhQwdSUlI4depUvrglSSIwMJB9+/YRGRlJzZo15XVNmjRh7ty5WqXxn9A2vzIyMmjYsCETJ07EzMyM/fv3M2DAAGrUqEGTJk3k8D/88AODBw/m3LlzXLhwgaFDh2JnZ0dAQECBaZg5cybz589nwYIFLF++HF9fX27fvo2FhQVTp04lNjaWgwcPYmVlRUJCAi9fvnyreVAUIyMjHj9+DBR9zxo5ciRZWVn89ttvmJiYEBsbK4/oKOxYtClP8OqRBhsbG3755RcSEhLo27cv9evXl/N34MCBPHr0iIiICMqUKcPYsWN5+PCh2vH07t0bIyMjDh48iLm5OaGhobRv354bN25gYWEBvLqv7Ny5k127dqGrq8v9+/fp168f8+fPp0ePHjx//pzIyEi1Rpm3adOmTZQtW5YRI0ZoXK8ql/BqBM5PP/3Evn37ePr0KX369GHu3LkaGyBfl5ubS8+ePalUqRJRUVGkpKQUWMn/6quvCAkJkRsji7oHqgQFBbFw4UIcHBwICgqiX79+JCQkoKf3z6vfpVpxP3LkyDsdNiMIgiAI/wVGRkZ4eXmRnpperO1epLwK/8svv/yjH8Hp6a+2t7W1Vft8+vTpzJgxI1/4ffv2qQ1H7tChAz/++GO+CYO++eYbhg0bplZxz87OZsWKFdSrVw+A5ORkwsPDSU5OpnLlygCMHz+eQ4cOER4eXuBEY4mJiUydOpVjx45Rvnx5evTowYABA6hTpw4xMTFMnjyZvXv3atw2Pj4eACcnp0LzJTk5GUmS5HQVlgcAkydPZvLkyfL/gwYNok+fPgBMnDiR5s2bM3XqVLy9vQH44osvGDRokFocGRkZrF+/nipVqgCvhn936tSJkJAQrK2tadeunVr41atXU65cOX799Vc6d+4sf96/f3+1uFUV9wcPHtC3b18cHBzYvHmzXIGYPn06ISEh9OzZE4Bq1aoRGxtLaGgofn5+bN68mdzcXNauXYuhoSF16tTh7t27DB8+PF/eVK5cmdu3b2vM04yMDIKDg4mKisLd3Z1GjRrh4eHBggUL1CqMKvHx8ZiZmWFjY6MxPpXbt2+jq6tLxYoVCw2noqOjg6Ojo9wDum3bNnJzcwkLC5OHpoeHh1OuXDkiIiLw8vLim2++Ydy4cWoNFo0bN1aLNycnh08++YTLly9z8uRJ+TyqVK5cmTt37hTasPQmtM2vKlWqMH78ePn/zz//nMOHD7N9+3a182Bra8vixYtRKBTUqlWLmJgYFi9eXGjF3d/fn379+gEQHBzMsmXLOHfuHD4+PiQnJ+Pm5iY3sBR3uPWbkCSJ48ePc/jwYT7//HOAIu9ZycnJfPTRR7i4uABQvXp1OXxhx6JNeQIoX7483377Lbq6ujg5OdGpUyeOHz9OQEAA165d49ixY5w/f17eR1hYmFpj5smTJzl37hwPHz6UG1kXLlzITz/9xI4dO+THQbKysli/fj0VKlQA4NKlS+Tk5NCzZ0/5MSDVMZaE+Ph4qlevXmADXV65ubmsW7dObogcMGAAx48f16rifuzYMa5du8bhw4fl+3ZwcDAdOnTIF3b06NHy/Q6KvgeqjB8/nk6dOgGvGqnq1KlDQkJCkd8nhSnViruXlxdmZmalmQRBEARB+NcyNite47eJ+avwbdu2/Uf7U/W037lzR+37u6De9rZt27Jy5cr/27+JCfDqR9OcOXO4du0aqamp5OTkkJGRQXp6utygr6+vj6urq7xtTEwMSqUSR0dHtX1kZmZiaWlZYJrHjBlDy5YtmTRpEjdv3mTLli00btyY7OxsrKysmDlzZoHbaturpGoEUQ0tzev1PADk3i2VvMdZqVIlQP3HcaVKlcjIyCA1NVXOdzs7O7XKXvPmzcnNzeX69etYW1vz119/MWXKFCIiInj48CFKpZL09HSSk5PV9l1Qz7OnpydNmjRh27Zt6OrqAvDixQsSExMZPHiwWoUsJycHc3NzAOLi4nB1dVXLi+bNm2vch5GRkdwY9Lr09HQqVarEoUOHGDRoEMOGDSMgIIAzZ85orLhLkqTVM94vX77EwMCgWM+D5407OjqahISEfM/OZmRkkJiYyMOHD/nzzz81jiLIa8yYMRgYGHD27FmsrKzyrTcyMiI3N5fMzEyMjIw0xlGnTh254UNVVvM2ErVq1arA5+S1zS+lUklwcDDbt2/n3r17ZGVlkZmZma/jrVmzZmrxNW/enJCQEJRKpVx+Xpe33JuYmGBmZib3Eg8fPpyPPvqIS5cu4eXlRffu3WnRokWB6QwODlZrvHv58iVnz55Ve0QhNjYWOzu7AuNQNbJlZ2eTm5tL//795QbJou5ZgYGBDB8+nCNHjuDh4cFHH30kH19hx1JUeVKpU6eOWj7a2NgQExMDwPXr19HT06NBgwby+po1a6o9khMdHU1aWlq+e+XLly/V9lO1alW50g5Qr1492rdvj4uLC97e3nh5edGrVy+1uF+XtwwqlUoyMzPVPvvkk09YtWqVxm2L05Nvb2+vlm82Njb5RhkUJC4uDltbW7XG1oLuU3nvkdrcA1Xylm9VA9nDhw//vRX3MmXKaNWiIgiCIAiCOmWOkkZeDbhwOP/zwwVp5OWGMkf5j797VduZmZlp1fBuYmKiNvwXXj3/17lzZ4YPH87s2bOxsLDg5MmTDB48mKysLLlCYGRkpFYRSEtLQ1dXl4sXL+arCLzeo53X+vXr5eGVLi4udOvWjczMTJ4+fYq1tXWh6Vc1Ely7dq3AH3WAXPF6+vSp2o/egvLgdXnPh+qYNX1WnAn6/Pz8ePz4MUuXLqVq1aoYGBjQvHnzfBNOqRpTXtepUyd5wjJVI4Lqmd41a9bQtGlTtfAFVc4K8+TJE2rUqKFxnYWFBSNHjlT7rEaNGgWGd3R0JCUlhfv37xfai2xlZUV6ejpZWVnyKILCKJVK4uPj5R7ztLQ0GjZsqHGSvAoVKmjdO+7p6cmWLVs4fPgwvr6++dY/efIEExOTAivtAAcOHJAfX7l37x7u7u7yK/eAQrfVNr8WLFjA0qVLWbJkifyM9+jRo9/KxGWv34cUCoVcxjt06MDt27c5cOAAR48epX379owcOZKFCxdqjGvYsGHyqBUAX19fPvroI7WeUk0jYvJSNbLp6+tTuXJleUizNvesIUOG4O3tzf79+zly5Ahz5swhJCSEzz//vNBjKao8aZNX2khLS8PGxkbjnCB5h5+/fj/Q1dXl6NGjnD59miNHjrB8+XKCgoKIiooqcL6JvGUwKiqKiRMnqu23sO8OR0dHTp48SXZ2dpHfU2+aJ9rKmyfFuQe+6T1cEzE5nSAIgiD8C+nq6dLQqz5mVtqNXDO3MqOBpxu6esWvYL1NFy9eJDc3l5CQEJo1a4ajoyN//vlnkdu5ubmhVCp5+PAhNWvWVFsKq4Dn/VGqopqEqiheXl5YWVkxf/58jetVk7XVqFEDMzMzYmNji4zzbUlOTlbLt7Nnz6Kjo0OtWrUAOHXqFIGBgXTs2FGeJO3Ro0daxz937lz8/Pxo3769fFyVKlWicuXK3Lx5M985UP2Id3Z25vfffycjI0MtbZpcvXoVNze3ItOybt26IodK9+rVC319/SLPlerdz9qeqx9++IGnT5/y0UcfAdCgQQPi4+OpWLFivjwwNzfH1NQUe3t7jh8/Xmi8Xbt2ZfPmzQwZMkTjJHTa5E3VqlXlfauGMedNz+vD7/PSNr9OnTpFt27d+OSTT6hXrx7Vq1dXew2iSlRUlNr/Z8+excHB4R816KhUqFABPz8/Nm7cyJIlS1i9enWBYS0sLNSO3cjIKN85KurZYlUjm52dnVpYbe9Ztra2DBs2jF27djFu3DjWrFlT5LEUVZ60UatWLXJyctQmgUxISODp06fy/w0aNODBgwfo6enl24+mER95KRQKWrZsycyZM7l8+TL6+vrs3r27wPCvl8HX91nYYyr9+/cnLS0t34SXKqpy+aacnZ25c+cO9+/flz8r6D6Vlzb3wJIkKu6CIAiC8C+lQMGn33yCjm7hX+c6ujoM+mYAxXtZVMmoWbMm2dnZLF++nJs3b7Jhw4YCh03m5ejoiK+vLwMHDmTXrl3cunWLc+fOMWfOHPbv318iaTUxMSEsLIz9+/fTtWtXjh07RlJSEhcuXGDChAkMGzYMePUctIeHBydPnswXR2ZmJg8ePFBbilOBLoihoSF+fn5ER0cTGRlJYGAgffr0kRskHBwc2LBhA3FxcURFReHr61toD6wmCxcuxNfXl3bt2nHt2jXg1bOac+bMYdmyZdy4cYOYmBjCw8Pld7L3798fhUJBQEAAsbGxHDhwQGMvaVJSEvfu3cPDw+MNc+IV1TPWS5cuZfDgwfz666/cvn2bU6dO8dlnn8nvc65QoQINGjTQeK7S09N58OABd+/e5ezZs0ycOJFhw4YxfPhw+fESX19frKys6NatG5GRkdy6dYuIiAgCAwO5e/cu8GrCv5CQEJYtW0Z8fDyXLl1i+fLl+fbXo0cPNmzYwKBBg9ixY4fausjISPn55pKgbX45ODjIPa5xcXF89tln/PXXX/niS05OZuzYsVy/fp0tW7awfPlyjZMSamvatGn8/PPPJCQk8Mcff7Bv3z6cnZ3/cXxvQpt71ujRozl8+DC3bt3i0qVL/PLLL3J6CzsWbcpTUZycnPDw8GDo0KGcO3eOy5cvM3ToULVRSx4eHjRv3pzu3btz5MgRkpKSOH36NEFBQVy4cKHAuKOioggODubChQskJyeza9cu/v777xI7F02bNmXChAmMGzeOCRMmcObMGW7fvs3x48fp3bs3P/zww1vZj4eHB46Ojmr30KCgIK22LeoeWJJExV0QBEEQ/qV0dHVw+bAOgd8Nw7yAnndzKzMCvxuGy4e1i6zgvwv16tVj0aJFzJs3j7p167Jp0ybmzJmj1bbh4eEMHDiQcePGUatWLbp378758+cLfW71TXXr1o3Tp09TpkwZ+vfvj5OTE/369SMlJYVvvvlGDqfqOX19KOShQ4ewsbFRWz788MM3TlfNmjXp2bMnHTt2xMvLC1dXV7VeqrVr1/L06VMaNGjAgAEDCAwM1HpCtrwWL15Mnz59aNeuHTdu3GDIkCGEhYURHh6Oi4sLbdq0Yd26dXJvU9myZdm7dy8xMTG4ubkRFBTEvHnz8sW7ZcsWvLy88r33/k2MGDGCI0eOcO/ePXr06IGTkxNDhgzBzMxMbYK1IUOGaByavGbNGmxsbKhRowY9e/YkNjaWbdu2qeWrsbExv/32G3Z2dvTs2RNnZ2cGDx5MRkaGPATYz8+PJUuWsGLFCurUqUPnzp3liQ5f16tXL3744QcGDBjArl27gFfD3k+fPp1vQsK3TZv8mjJlCg0aNMDb2xt3d3esra3p3r17vrgGDhzIy5cvadKkCSNHjuSLL76QJzz7J/T19Zk0aRKurq60bt0aXV3dEn09XmG0uWcplUpGjhyJs7MzPj4+ODo6yuWmsGPRpjxpY/369VSqVInWrVvTo0cPAgICMDU1leeaUCgUHDhwgNatWzNo0CAcHR35+OOPuX37tjyvhiZmZmb89ttvdOzYEUdHR6ZMmUJISIjGSdzelnnz5rF582aioqLw9vamTp06jB07FldX13yvg/undHR02L17t1xmhwwZotWkdkCR98CSpJBKaj7/QqSmpmJubk5KSoqYnE4QBEEQ3lCuMhcJuHT0MheOXOZFSjom5sY08nKjgacbCngrlXbx/V0wSZJo2rQpY8aMkWfKFjTLysqSZ6tv2bLlO9//y5cvqVWrFtu2bSt07oLSMnHiRJ4+fVro0HBBKMzdu3extbXl2LFjRU6UKPx7lOrkdIIgCIIgvDlVpbyBR30a+zSUP1fmKNF9D3rZ/xcoFApWr14tz/QsFCw5OZnJkyeXSqUdXk3atn79+rfyyEJJqFixImPHji3tZAj/IidOnCAtLQ0XFxfu37/PhAkTsLe3p3Xr1qWdNOEtEj3ugiAIgiBoRXx/C4IgvH8OHz7MuHHjuHnzJqamprRo0YIlS5a81UdRhNInKu6CIAiCIGhFfH8LgiAIQukQ4+cEQRAEQRAEQRAE4T0mKu6CIAj/AsocZaH/C4IgCIIgCP9dYnI6QRCE91iu8tWrpaKPRXP5yBVepqZjZGaMm1d96nvWB97ObOGCIAiCIAjC+0tU3AVBEN5TUq5E3Ok4Nk3ZzPPHz9XWXTlyBVNLU3y/6U/tlrVR6ChKKZWCIAiCIAhCSRPdNIIgCO+hXGUusadiWT1qTb5Ku8rzx89ZPWoNsadi5Z55QRBKz9SpUxk6dGiJ72fGjBnUr1+/ROKOiIhAoVDw7NmztxZnUlISCoWCK1euABAbG8sHH3zAixcv3to+3iZ3d3dGjx5daBh7e3uWLFnyTtJT0o4fP46zszNK5bt/BMvf35/u3bsXuH7VqlV06dLl3SVI+FfQ5h74+nWszTWrUCj46aef3jh9JUVU3AVBEN5Tm6ZsLrJCnqvMZfPULe8oRYKgPX9/fxQKRb4lISGhtJNWbJcvX6Z3795UqlQJQ0NDHBwcCAgI4MaNG3KYBw8esHTpUoKCguTPCsoDHx+f0jiM90bt2rVp1qwZixYt0iq8v78/SUlJWoX95Zdf6NixI5aWlhgbG1O7dm3GjRvHvXv33iDF+Z0/f/6dNNK8iQcPHvD5559TvXp1DAwMsLW1pUuXLhw/flwt3IQJE5gyZQq6uroArFu3Ti6rurq6lC9fnqZNm/L111+TkpLyTo/h008/5dKlS0RGRpbofvJeq/r6+tSsWZOvv/6anJycEt2voNnOnTtxd3fH3NycsmXL4urqytdff82TJ0+0jmPXrl3MmjWrBFP57omKuyAIwntGmaPkytErBfa0vy71USpXjkWLCeuE946Pjw/3799XW6pVq1bseJRKJbm5pTOqZN++fTRr1ozMzEw2bdpEXFwcGzduxNzcnKlTp8rhwsLCaNGiRb73JmvKgy1b3s/Gtuzs7He2r0GDBrFy5coCK0ZPnjzhu+++I+9bixMTE9m0aVOBcYaGhuLh4YG1tTU7d+4kNjaWVatWkZKSQkhIyFtNf4UKFTA2Nn6rcRbG3d2ddevWaR0+KSmJhg0bcuLECRYsWEBMTAyHDh2ibdu2jBw5Ug538uRJEhMT+eijj9S2NzMz4/79+9y9e5fTp08zdOhQ1q9fT/369fnzzz/f1mEVSV9fn/79+7Ns2bJibVfc/IL/u1bj4+MZN24cM2bMYMGCBcWK478kKyvrjeOIiIjA3t6+WNsEBQXRt29fGjduzMGDB7l69SohISFER0ezYcMGreOxsLDA1NS0mCku3Lu8R2pSqhX37OxssYhFLGIRy2uLrp4ul49cKdb99MqRK+jq6ZZ62sXy31/g1fvc8y6ZmZkay6WBgQHW1tZqi66uLosWLcLFxQUTExNsbW0ZMWIEaWlp8nbr1q2jXLly7Nmzh9q1a2NgYEBycjKZmZmMHz+eKlWqYGJiQtOmTYmIiPinP0OKlJ6ezqBBg+jYsSN79uzBw8ODatWq0bRpUxYuXEhoaKgcduvWrRqH9GrKg/Lly8vrFQoFoaGhdO7cGWNjY5ydnTlz5gwJCQm4u7tjYmJCixYtSExMzBd3aGgotra2GBsb06dPH7Xe0PPnz+Pp6YmVlRXm5ua0adOGS5cuqW2vUChYuXIlXbt2xcTEhNmzZ2vMgw4dOtCyZUt5+HxYWBjOzs4YGhri5OTEihUr1LY5d+4cbm5uGBoa0qhRIy5fvpwvXk9PT548ecKvv/6qMe8NDQ25d+8ePj4+3L17l1WrVuHv719gw8/du3cJDAwkMDCQ77//Hnd3d+zt7WndujVhYWFMmzYNgMePH9OvXz+qVKmCsbExLi4uGhtScnJyGDVqFObm5lhZWTF16lS1RoTXh90qFArCwsLo0aMHxsbGODg4sGfPHnn906dP8fX1pUKFChgZGeHg4EB4eLjGY3kbRowYgUKh4Ny5c3z00Uc4OjpSp04dxo4dy9mzZ+VwW7duxdPTE0NDQ7XtFQoF1tbW2NjY4OzszODBgzl9+jRpaWlMmDBBDpebm8ucOXOoVq0aRkZG1KtXjx07dqjF9ccff9C5c2fMzMwwNTWlVatWGsszvCq3FSpUYN68efJnXbp0Yc+ePbx8+fJtZE2BVNdq1apVGT58OB4eHvI5LOqedfv2bbp06UL58uUxMTGhTp06HDhwACj63N+5c4c+ffpQrlw5LCws6Natm9oIE9XjBAsXLsTGxgZLS0tGjhwp348B7t+/T6dOnTAyMqJatWps3rw5Xxl99uwZQ4YMoUKFCpiZmdGuXTuio6Pl9arh52FhYVSrVk0uEzt27MDFxQUjIyMsLS3x8PAoscdczp07R3BwMCEhISxYsIAWLVpgb2+Pp6cnO3fuxM/PTy38hg0bsLe3x9zcnI8//pjnz/+vw6OoR17i4+Np3bo1hoaG1K5dm6NHj6qtVz3is23bNtq0aYOhoaHccFjYPVC13a5du2jbti3GxsbUq1ePM2fOvHH+lOrkdEeOHHmnrZWCIAjvOyMjI7y8vHiZml6s7dL/f/hffvmlxH/cCP+70tNflTNbW1u1z6dPn86MGTO0jkdHR4dly5ZRrVo1bt68yYgRI5gwYYLaj5/09HTmzZtHWFgYlpaWVKxYkVGjRhEbG8vWrVupXLkyu3fvxsfHh5iYGBwcHP7RMeXm5qKjo7kf4/Dhwzx69EitopJXuXLlgFe9w7GxsTRq1OgfpWHWrFksWrSIRYsWMXHiRPr370/16tWZNGkSdnZ2fPrpp4waNYqDBw/K2yQkJLB9+3b27t1LamoqgwcPZsSIEfIPy+fPn+Pn58fy5cuRJImQkBA6duxIfHy8Wi/UjBkzmDt3LkuWLEFPT4+bN2/K6549e0anTp0oW7YsR48exdjYmE2bNjFt2jS+/fZb3NzcuHz5MgEBAZiYmODn50daWhqdO3fG09OTjRs3cuvWLb744ot8x6yvr0/9+vWJjIykffv2+dYbGxsTHBzMgQMH6Nq1Kzk5OZw4cYIyZcpozMMff/yRrKysIs9VRkYGDRs2ZOLEiZiZmbF//34GDBhAjRo1aNKkiRz+hx9+YPDgwZw7d44LFy4wdOhQ7OzsCAgIKPA8zpw5k/nz57NgwQKWL1+Or68vt2/fxsLCgqlTpxIbG8vBgwexsrIiISGhxO7VT5484dChQ8yePRsTE5N861V5ARAZGUn//v21irdixYr4+vry/fffo1Qq0dXVZc6cOWzcuJFVq1bh4ODAb7/9xieffEKFChVo06YN9+7do3Xr1ri7u3PixAnMzMw4deqUxpEWJ06coGfPnsyfP1/tMYRGjRqRk5NDVFQU7u7uxc6Pf8rIyIjHjx8DRd+zRo4cSVZWFr/99hsmJibExsZStmxZgELPfXZ2Nt7e3jRv3pzIyEj09PT45ptv8PHx4ffff0dfXx949d1uY2PDL7/8QkJCAn379qV+/fpyeRw4cCCPHj0iIiKCMmXKMHbsWB4+fKh2PL1798bIyIiDBw9ibm5OaGgo7du358aNG1hYWACv7is7d+5k165d6Orqcv/+ffr168f8+fPp0aMHz58/JzIyUq0R623atGkTZcuWZcSIERrX5y27iYmJ/PTTT+zbt4+nT5/Sp08f5s6dq7EB8nW5ubn07NmTSpUqERUVRUpKSoGV/K+++oqQkBC5MbKoe6BKUFAQCxcuxMHBgaCgIPr160dCQgJ6ev+8+l2qFXcvLy/MzMxKMwmCIAjvJSOz4jVqGv//8G3bti2J5AgC8KqnHV71EOX9/jYwMNAYft++ffKPV4AOHTrw448/5psw6JtvvmHYsGFqFffs7GxWrFhBvXr1AEhOTiY8PJzk5GQqV64MwPjx4zl06BDh4eEEBwdrTENiYiJTp07l2LFjlC9fnh49ejBgwADq1KlDTEwMkydPZu/evRq3jY+PB8DJyanQfElOTkaSJDldheUBwOTJk5k8ebL8/6BBg+jTpw8AEydOpHnz5kydOhVvb28AvvjiCwYNGqQWR0ZGBuvXr6dKlSoALF++nE6dOhESEoK1tTXt2rVTC7969WrKlSvHr7/+SufOneXP+/fvrxa3quL+4MED+vbti4ODA5s3b5YrENOnTyckJISePXsCUK1aNWJjYwkNDcXPz4/NmzeTm5vL2rVrMTQ0pE6dOty9e5fhw4fny5vKlStz+/ZtjXmakZFBcHCwXFlr1KgRHh4eLFiwQK2CrRIfH4+ZmRk2NjYa41OpUqUK48ePl////PPPOXz4MNu3b1eL19bWlsWLF6NQKKhVqxYxMTEsXry40Iq7v78//fr1AyA4OJhly5Zx7tw5fHx8SE5Oxs3NTW7cKe7w4eJISEhAkqQiyy286inWVG4L4uTkxPPnz3n8+DHm5uYEBwdz7NgxmjdvDkD16tU5efIkoaGhtGnThu+++w5zc3O2bt0qN7o4Ojrmi3f37t0MHDiQsLAw+vbtq7bO2NgYc3PzAsvK2yZJEsePH+fw4cN8/vnnAEXes5KTk/noo49wcXEBXuWDSmHnftu2beTm5hIWFoZC8ertMOHh4ZQrV46IiAi8vLwAKF++PN9++y26uro4OTnRqVMnjh8/TkBAANeuXePYsWOcP39e3kdYWJhaY+bJkyc5d+4cDx8+lO/XCxcu5KeffmLHjh1yQ0lWVhbr16+nQoUKAFy6dImcnBx69uwpPwakOsaSEB8fT/Xq1QtsoMsrNzeXdevWyQ2RAwYM4Pjx41pV3I8dO8a1a9c4fPiwXP6Dg4Pp0KFDvrCjR4+W73dQ9D1QZfz48XTq1Al41ahXp04dEhIStLouC1KqFfcyZcpodWIEQRD+lyhzlLh51edKMYbL1/eqjzJHKe6pQolSlS8zMzOtGt7btm3LypUr5f9VvX/Hjh1jzpw5XLt2jdTUVHJycsjIyCA9PV0eiaevr4+rq6u8bUxMDEqlMt+P/szMTCwtLQtMw5gxY2jZsiWTJk3i5s2bbNmyhcaNG5OdnY2VlRUzZ84scFtte5VUvWevDzeG/HkAyL1bKnmPs1KlSoD6j+NKlSqRkZFBamqqnO92dnZypR2gefPm5Obmcv36daytrfnrr7+YMmUKERERPHz4EKVSSXp6OsnJyWr7LmiUgKenJ02aNGHbtm3ypGUvXrwgMTGRwYMHq1Vgc3JyMDc3ByAuLg5XV1e1vFBV6l5nZGQkj+J4XXp6OpUqVeLQoUMMGjSIYcOGERAQwJkzZzRW3CVJkis+hVEqlQQHB7N9+3bu3btHVlYWmZmZ+UaANmvWTC2+5s2bExISIvc0a5L3PJqYmGBmZib3eg4fPpyPPvqIS5cu4eXlRffu3WnRokWB6QwODlZrjHr58iVnz55l1KhR8mexsbHY2dnl27Y4vaEvX77UWG4LoopbNdFkeno6np6eamGysrJwc3MD4MqVK7Rq1arQ76aoqCj27dvHjh07CpxhvrCyAm+WXyqqRrbs7Gxyc3Pp37+/PJKoqHtWYGAgw4cP58iRI3h4ePDRRx/J5aGwcx8dHU1CQkK+Z7EzMjLUHieoU6eOWrmzsbEhJiYGgOvXr6Onp0eDBg3k9TVr1lR7JCc6Opq0tLR898qXL1+q7adq1apypR2gXr16tG/fHhcXF7y9vfHy8qJXr15qcb8ub0OlUqkkMzNT7bNPPvmEVatWady2OGXX3t5eLd9sbGzyjTIoSFxcHLa2tmqNVgXdp/LeI7W5B6rkvR+oGhQfPnz47624C4IgCPnp6ulS37M+ppamWk1QZ2ZlRn2PeujoivlGhfeLiYkJNWvWVPssKSmJzp07M3z4cGbPno2FhQUnT55k8ODBZGVlyRUoIyMjtYpTWloaurq6XLx4MV/F6fUe7bzWr18vD690cXGhW7duZGZm8vTpU6ytrQtNv6qR4Nq1awX+qAOwsrICXj3LmvdHb0F58Lq8lRrVMWv6rDgT9Pn5+fH48WOWLl1K1apVMTAwoHnz5vkmnNI0lBqgU6dO8gRvqkYE1TO9a9asoWnTpmrhC6rMFubJkyfUqFFD4zoLCwu1SdQAatSoUWB4R0dHUlJSuH//fqG97gsWLGDp0qUsWbJEfmZ59OjRb2UirtcrpwqFQj5nHTp04Pbt2xw4cICjR4/Svn17Ro4cycKFCzXGNWzYMHkUBoCvry8fffSRWs9fQT3lDg4OKBQKrl27VmSaraysePr0aZHhVOLi4jAzM8PS0lIenbF//361RiT4v1E4RkZGRcZZo0YNLC0t+f777+nUqZPGSv6TJ0/yXVt5vUl+qaga2fT19alcubI8pFmbe9aQIUPw9vZm//79HDlyhDlz5hASEsLnn39e6LlPS0ujYcOGGiddzHu8hZUtbaSlpWFjY6NxTpC8w89fvx/o6upy9OhRTp8+zZEjR1i+fDlBQUFERUUVON+E6rWP8KpRZuLEiWr7LazR19HRkZMnT5KdnV1kR8Sb5om28uZJce6Bb3oP10T8yhMEQXhP+X7Tv8jKuI6uDv1nafd8oiC8Dy5evEhubi4hISE0a9YMR0dHrWapdnNzQ6lU8vDhQ2rWrKm2FFYBz/ujVEU1CVVRvLy8sLKyYv78+RrXqyZrq1GjBmZmZsTGxhYZ59uSnJyslm9nz55FR0eHWrVqAXDq1CkCAwPp2LEjderUwcDAgEePHmkd/9y5c/Hz86N9+/bycVWqVInKlStz8+bNfOdA9SPe2dmZ33//nYyMDLW0aXL16lW5Z7Yw69atK3Joea9evdDX1y/yXJ06dYpu3brxySefUK9ePapXr672Wj+VqKgotf/Pnj2Lg4PDP2qgUKlQoQJ+fn5s3LiRJUuWsHr16gLDWlhYqOWvkZERFStWVPusoGdlLSws8Pb25rvvvtM4iZgqL+DVdaVtuX348CGbN2+me/fu6OjoqE0c+Xp5UM2D4erqSmRkpNpEaq+zsrLixIkTJCQk0KdPn3xhExMTycjIKLSsvEl+qaga2ezs7NTCanvPsrW1ZdiwYezatYtx48axZs0aeV1B575BgwbEx8fnS2vNmjXz9eAWpFatWuTk5KhNApmQkKDWINOgQQMePHiAnp5evv2oGh4LolAoaNmyJTNnzuTy5cvo6+uze/fuAsPnjbtKlSr59lmxYsUCt+3fvz9paWn5JrxUyVt234SzszN37tzh/v378mcF3afy0uYeWJJExV0QBOE9pKOrQ+2WtRn6bQBmVppbp82szBj6bQC1WzqL3nbhX6NmzZpkZ2ezfPlybt68yYYNGwocNpmXo6Mjvr6+DBw4kF27dnHr1i3OnTvHnDlz2L9/f4mk1cTEhLCwMPbv30/Xrl05duwYSUlJXLhwgQkTJjBs2DDg1cRVHh4enDx5Ml8cmZmZPHjwQG0pTgW6IIaGhvj5+REdHU1kZCSBgYH06dNHbpBwcHBgw4YNxMXFERUVha+vr1a9n3ktXLgQX19f2rVrJ/fezpw5kzlz5rBs2TJu3LhBTEwM4eHh8jvZ+/fvj0KhICAggNjYWA4cOKCxVzkpKYl79+7h4eHxhjnxiuqZ9KVLlzJ48GB+/fVXbt++zalTp/jss8/k9zk7ODjIPYhxcXF89tln/PXXX/niS05OZuzYsVy/fp0tW7awfPlyjZPsaWvatGn8/PPPJCQk8Mcff7Bv3z6cnZ3/cXxF+e6771AqlTRp0oSdO3cSHx9PXFwcy5YtUxs94u3trbHcSpLEgwcPuH//PnFxcXz//fe0aNECc3Nz5s6dC4CpqSnjx49nzJgx/PDDDyQmJnLp0iWWL1/ODz/8AMCoUaNITU3l448/5sKFC8THx7NhwwauX7+utr+KFSty4sQJrl27Rr9+/dQmr4uMjKR69eoFjrYoadrcs0aPHs3hw4e5desWly5d4pdffpHPb2Hn3tfXFysrK7p160ZkZCS3bt0iIiKCwMBA7t69q1X6nJyc8PDwYOjQoZw7d47Lly8zdOhQtVFLHh4eNG/enO7du3PkyBGSkpI4ffo0QUFBXLhwocC4o6KiCA4O5sKFCyQnJ7Nr1y7+/vvvEiu7TZs2ZcKECYwbN44JEyZw5swZbt++zfHjx+ndu7dcrt6Uh4cHjo6OavfQoKAgrbYt6h5YksQvPUEQhPeUQkeBcwtnZh3/mkEhg3DzdqNW81q4ebsxKGQQs45/jXMLZxQ6RT/XKQjvi3r16rFo0SLmzZtH3bp12bRpE3PmzNFq2/DwcAYOHMi4ceOoVasW3bt35/z584U+t/qmunXrxunTpylTpgz9+/fHycmJfv36kZKSwjfffCOHGzJkCFu3bs03FPLQoUPY2NioLR9++OEbp6tmzZr07NmTjh074uXlhaurq1ov1dq1a3n69CkNGjRgwIABBAYGFtrTVZDFixfTp08f2rVrx40bNxgyZAhhYWGEh4fj4uJCmzZtWLdundzbVLZsWfbu3UtMTAxubm4EBQWpvdpLZcuWLXh5eeV77/2bGDFiBEeOHOHevXv06NEDJycnhgwZgpmZmTwh3ZQpU2jQoAHe3t64u7tjbW2t8bnqgQMH8vLlS5o0acLIkSP54osv1GY6Ly59fX0mTZqEq6srrVu3RldXl61bt/7j+IpSvXp1Ll26RNu2bRk3bhx169bF09OT48ePq8254Ovryx9//JGvIp2amoqNjQ1VqlShefPm8sRbly9fVnsUYdasWUydOpU5c+bg7OyMj48P+/fvl8uDpaUlJ06cIC0tjTZt2tCwYUPWrFmjcRi0tbU1J06cICYmBl9fX5RKJfCqrBQ2KWBJ0+aepVQqGTlypJwHjo6O8vVY2Lk3Njbmt99+w87Ojp49e8qv3svIyCjWBN7r16+nUqVKtG7dmh49ehAQEICpqak8f4FCoeDAgQO0bt2aQYMG4ejoyMcff8zt27fleTU0MTMz47fffqNjx444OjoyZcoUQkJCNE7i9rbMmzePzZs3ExUVhbe3t/waQ1dX13yvg/undHR02L17t3yNDxkyRKtJ7YAi74ElSSGV1Hz+hUhNTcXc3JyUlBQxq7wgCIIWlDlKdPV0C/xfEN4F8f1dMEmSaNq0KWPGjJFnFhc0y8rKkmerb9myZWkn53/el19+SWpqKqGhoaWdlHz++OMPudFI26HjAty9exdbW1uOHTum8XWLwr+T6HEXBEH4F3i9ki4q7YLwflEoFKxevVrj+6kFdcnJyUyePFlU2t8TQUFBVK1atUQm9npT9+/fZ/369aLSXoQTJ06wZ88ebt26xenTp/n444+xt7endevWpZ004S0SPe6CIAiCIGhFfH8LgiC8fw4fPsy4ceO4efMmpqamtGjRgiVLlrzVR1GE0icq7oIgCIIgaEV8fwuCIAhC6RBD5QVBEARBEARBEAThPSYq7oIgCIJQTMocZaH/C4IgCIIgvE16pZ0AQRAEQfi3yFW+mrzpj+O/E3PkCi9TX2JkZoSLV33qetQDQEdXtIkLgiAIgvB2iV8XgiAIgqAFKVfixulrBHtMZ/OXPxBzNJqEqBvEHI1m85c/EOwxnRunryHlvvOpY4T3xNSpU9/oXd/amjFjBvXr1y+RuCMiIlAoFDx79uytxZmUlIRCoeDKlSsAxMbG8sEHH/DixYu3tg/h3+n69etYW1vz/Pnzd77voq6jQ4cOUb9+/fdytn1NtLl2161bR7ly5d5ZmkqKNvdAd3d3Ro8eLf9vb2/PkiVLCt1GoVDw008/vXH6SoqouAuCIAhCEXKVuVw/Fcf6wDDSHmv+gZn2+DnrA8O4fipO7pn/X+bv749Coci3JCQklHbSiu3y5cv07t2bSpUqYWhoiIODAwEBAdy4cUMO8+DBA5YuXUpQUJD8WUF54OPjUxqH8d6oXbs2zZo1Y9GiRVqF9/f3JykpqchwefPY3Nycli1bcuLEiTdMrfBPpKamEhQUhJOTE4aGhlhbW+Ph4cGuXbvIOy/2pEmT+PzzzzE1NQX+r/KpUCjQ0dHB3NwcNzc3JkyYwP3799/pMfj4+FCmTBk2bdpU4vvSNr/eVN++fdXuW++rnTt34u7ujrm5OWXLlsXV1ZWvv/6aJ0+eaB3Hrl27mDVrVgmm8t0TFXdBEARB0MKOaVuKrJDnKnPZOX3rO0rR+8/Hx4f79++rLdWqVSt2PEqlstR6vfbt20ezZs3IzMxk06ZNxMXFsXHjRszNzZk6daocLiwsjBYtWuR7/ZKmPNiyZcu7PgytZGdnv7N9DRo0iJUrVxb43vsnT57w3XffqVVaEhMTi6xEhYeHc//+fU6dOoWVlRWdO3fm5s2bbzXt/yZZWVlvHIe/vz8zZszQOvyzZ89o0aIF69evZ9KkSVy6dInffvuNvn37MmHCBFJSUgBITk5m3759+Pv754vj+vXr/Pnnn5w/f56JEydy7Ngx6tatS0xMzBsfT3H4+/uzbNmyYm9TEvn1NhgZGVGxYsW3Fl9RIiIisLe3L9Y2QUFB9O3bl8aNG3Pw4EGuXr1KSEgI0dHRbNiwQet4LCws5Aaht+Vd3iM1ERV3QRAEQSiEMkfJ1WPRBfa0v+75o1SuHo8WE9YBBgYGWFtbqy26urosWrQIFxcXTExMsLW1ZcSIEaSlpcnbqYZz7tmzh9q1a2NgYEBycjKZmZmMHz+eKlWqYGJiQtOmTYmIiCix9KenpzNo0CA6duzInj178PDwoFq1ajRt2pSFCxcSGhoqh926dStdunTRKg/Kly8vr1coFISGhtK5c2eMjY1xdnbmzJkzJCQk4O7ujomJCS1atCAxMTFf3KGhodja2mJsbEyfPn3UfuCfP38eT09PrKysMDc3p02bNly6dElte4VCwcqVK+natSsmJibMnj1bYx506NCBli1bykNww8LCcHZ2xtDQECcnJ1asWKG2zblz53Bzc8PQ0JBGjRpx+fLlfPF6enry5MkTfv31V415b2hoyL179/Dx8eHu3busWrUKf3//Iht+ypUrh7W1NXXr1mXlypW8fPmSo0eP8vjxY/r160eVKlUwNjbGxcUlXwPKjh07cHFxwcjICEtLSzw8POTh/BERETRp0gQTExPKlStHy5YtuX37trztzz//TIMGDTA0NKR69erMnDlTrVFCoVAQFhZGjx49MDY2xsHBgT179qjtf8+ePTg4OGBoaEjbtm354Ycf8g19PnnyJK1atcLIyAhbW1sCAwPVHjmwt7dn1qxZDBw4EDMzM4YOHUpWVhajRo3CxsYGQ0NDqlatypw5cwrNxzcxefJkkpKSiIqKws/Pj9q1a+Po6EhAQABXrlyhbNmyAGzfvp169epRpUqVfHFUrFgRa2trHB0d+fjjjzl16hQVKlRg+PDhauGKKot3796lX79+WFhYYGJiQqNGjYiKitKY7sTERKpXr86oUaPkBqMuXbpw4cIFjdff26Jtfm3YsIFGjRphamqKtbU1/fv35+HDh/niO3XqFK6urhgaGtKsWTOuXr0qr3t9qLxqyPmGDRuwt7fH3Nycjz/+WO3RhcKui7ft3LlzBAcHExISwoIFC2jRogX29vZ4enqyc+dO/Pz81MIXlu7Xh8q/Lj4+ntatW2NoaEjt2rU5evSo2nrVIz7btm2jTZs2GBoayg2HhZU71Xa7du2ibdu2GBsbU69ePc6cOfPG+VOqk9NlZ2eXesuFIAiCIBSmTJkyxBy5UqxtYo5E4+rl9p/7jlMdT2pqqtrnBgYGGBgYaB2Pjo4Oy5Yto1q1aty8eZMRI0YwYcIEtR8/6enpzJs3j7CwMCwtLalYsSKjRo0iNjaWrVu3UrlyZXbv3o2Pjw8xMTE4ODj8o2PKzc1FR0dzP8bhw4d59OgREyZM0Lhe9QP4yZMnxMbG0qhRo3+UhlmzZrFo0SIWLVrExIkT6d+/P9WrV2fSpEnY2dnx6aefMmrUKA4ePChvk5CQwPbt29m7dy+pqakMHjyYESNGyD8snz9/jp+fH8uXL0eSJEJCQujYsSPx8fFqvVAzZsxg7ty5LFmyBD09PbXe6WfPntGpUyfKli3L0aNHMTY2ZtOmTUybNo1vv/0WNzc3Ll++TEBAACYmJvj5+ZGWlkbnzp3x9PRk48aN3Lp1iy+++CLfMevr61O/fn0iIyNp3759vvXGxsYEBwdz4MABunbtSk5ODidOnKBMmTJa56uRkRHwqtc5IyODhg0bMnHiRMzMzNi/fz8DBgygRo0aNGnShPv379OvXz/mz59Pjx49eP78OZGRkUiSRE5ODt27dycgIIAtW7aQlZXFuXPnUCgUAERGRjJw4ECWLVtGq1atSExMlOc6mD59upyemTNnMn/+fBYsWMDy5cvx9fXl9u3bWFhYcOvWLXr16sUXX3zBkCFDuHz5MuPHj1c7nsTERHx8fPjmm2/4/vvv+fvvvxk1ahSjRo0iPDxcDrdw4UKmTZsm73vZsmXs2bOH7du3Y2dnx507d7hz547W+Vgcubm5bN26FV9fXypXrpxvvaoSCq/yTdtrxsjIiGHDhjFmzBgePnxIxYoVtSqLbdq0oUqVKuzZswdra2suXbqkcfTO77//jre3N4MHD+abb76RP7ezs6NSpUpERkZSo0aNf5AjhStOfmVnZzNr1ixq1arFw4cPGTt2LP7+/hw4cEBtmy+//JKlS5dibW3N5MmT6dKlCzdu3Cjw2klMTOSnn35i3759PH36lD59+jB37lxmz55d6HVREjZt2kTZsmUZMWKExvV5Gx0KS3dRcnNz6dmzJ5UqVSIqKoqUlJQCK/lfffUVISEhcmNkUeVOJSgoiIULF+Lg4EBQUBD9+vUjISEBPb1/Xv0u1Yr7kSNHMDY2Ls0kCIIgCEKBjIyM8PLy4mXqy2Jt9zI1HYBffvmFly+Lt+37LD391XHZ2tqqfT59+nSNQ0P37dun9sOzQ4cO/Pjjj/kmDPrmm28YNmyYWsU9OzubFStWUK/eq9n6k5OTCQ8PJzk5Wf6BO378eA4dOkR4eDjBwcEa05yYmMjUqVM5duwY5cuXp0ePHgwYMIA6deoQExPD5MmT2bt3r8Zt4+PjAXBycio0X5KTk5EkSeMP79fzAF71sE2ePFn+f9CgQfTp0weAiRMn0rx5c6ZOnYq3tzcAX3zxBYMGDVKLIyMjg/Xr18u9lcuXL6dTp06EhIRgbW1Nu3bt1MKvXr2acuXK8euvv9K5c2f58/79+6vFraq4P3jwgL59++Lg4MDmzZvR19cHXp3rkJAQevbsCUC1atWIjY0lNDQUPz8/Nm/eTG5uLmvXrsXQ0JA6depw9+7dfD2lAJUrV1brtX79+IKDg4mKisLd3Z1GjRrh4eHBggULaNKkicZt8kpPT2fKlCno6urKlbe8FeHPP/+cw4cPs337drninpOTQ8+ePeXHHVxcXIBXDTMpKSl07txZrrw5OzvLcc2cOZOvvvpK/tFevXp1Zs2axYQJE9Qq7v7+/vTr1w+A4OBgli1bxrlz5/Dx8SE0NJRatWqxYMECAGrVqsXVq1fVKiFz5szB19dXvn4cHBxYtmwZbdq0YeXKlRgaGgLQrl07xo0bJ2+XnJyMg4MDH374IQqFIt/jHG/To0ePePr0aZHXDMDt27eL1dilijMpKYmKFStqVRb//vtvzp8/j4WFBQA1a9bMF+/p06fp3LkzQUFBavmmUlg5fVPFya9PP/1U/rt69eosW7aMxo0bk5aWpnaPmT59Op6engD88MMPfPDBB+zevVu+x7wuNzeXdevWyQ16AwYM4Pjx43LFvaDroiTEx8dTvXp1rRroCkt3UY4dO8a1a9c4fPiwfN8ODg6mQ4cO+cKOHj1aLmNQ9D1QZfz48XTq1Al4dY+oU6cOCQkJWp3rgpRqxd3LywszM7PSTIIgCIIgFMnIzKiY4V81Srdt27YkklNqVD3td+7cUfv+Lqi3vW3btqxcuVL+38TEBHj1o2nOnDlcu3aN1NRUcnJyyMjIID09XW7Q19fXx9XVVd42JiYGpVKJo6Oj2j4yMzOxtLQsMM1jxoyhZcuWTJo0iZs3b7JlyxYaN25MdnY2VlZWzJw5s8Btte1VUjXOqCpOeb2eB4BciVDJe5yVKlUC1H8cV6pUiYyMDFJTU+V8t7OzUxti3Lx5c3Jzc+VZuv/66y+mTJlCREQEDx8+RKlUkp6eTnJystq+C6o4eXp60qRJE7Zt24auri4AL168IDExkcGDBxMQECCHzcnJwdzcHIC4uDh5mG7etGliZGQkNwa9Lj09nUqVKnHo0CEGDRrEsGHDCAgI4MyZM4VW3Pv164euri4vX76kQoUKrF27FldXV5RKJcHBwWzfvp179+6RlZVFZmamXN7q1atH+/btcXFxwdvbGy8vL3r16kX58uWxsLDA398fb29vPD098fDwoE+fPtjY2AAQHR3NqVOn1CoMSqUyX5nOe55NTEwwMzOThzpfv36dxo0bqx3L68cZHR3N77//rvacvyRJ5ObmcuvWLbkx4fVz6u/vj6enJ7Vq1cLHx4fOnTvj5eVVYB5u2rSJzz77TP4/MzMThULBwoUL5c8OHjxIq1at8m1bnJ7Yly9farxmCqKKW6FQaFUWr1y5gpubW77rLa/k5GQ8PT2ZPXt2gT2uhZVTeHf5dfHiRWbMmEF0dDRPnz6VRw4kJydTu3ZtOVze683CwoJatWoRFxdXYLz29vZqo3BsbGzkclnYdVGQvI0ISqWSzMxMtc8++eQTVq1apXHb4uRHYekuSlxcHLa2tmqNrQXdp/JeT9qUO5W817vqXvHw4cN/b8W9TJkyxRryJAiCIAjvmjJHiYtXfWKORmu9jYtXPZQ5yv/cd5zqeMzMzLRqeDcxMcnXw5WUlETnzp0ZPnw4s2fPxsLCgpMnTzJ48GCysrLkSo6RkZE8FBkgLS0NXV1dLl68KFckVV7v0c5r/fr18vBKFxcXunXrRmZmJk+fPsXa2rrQ9KsaCa5du1bgjzoAKysrAJ4+fUqFChWKzIPX5S0nqmPW9FlxJujz8/Pj8ePHLF26lKpVq2JgYEDz5s3zTVamakx5XadOndi5cyexsbFyI4JqHoI1a9bQtGlTtfCvnxNtPHnypMDhxxYWFowcOVLtsxo1ahQ5XHnx4sV4eHhgbm6udi4WLFjA0qVLWbJkiTy/wujRo+X80NXV5ejRo5w+fZojR46wfPlygoKCiIqKolq1aoSHhxMYGMihQ4fYtm0bU6ZM4ejRozRr1oy0tDRmzpyp1iunkrdi+vr9QKFQFOucpqWl8dlnnxEYGJhvnZ2dnfz36+e0QYMG3Lp1i4MHD3Ls2DH69OmDh4cHO3bs0Lifrl27qp3fiRMnUqVKFbX9anouHaBChQqUK1eOa9euFXk8VlZWPH36tMhwKqrKp729vVZlUfWoRGEqVKhA5cqV2bJlC59++qnG+9qTJ0/yXdd5vYv8evHiBd7e3nh7e7Np0yYqVKhAcnIy3t7ebzwBYWHlsqjrQhPVax8BoqKimDhxotpcJIV9dzg6OnLy5Emys7OL/P580+tJW3mvp+LcA9/0Hq5JqVbcBUEQBOF9p6unS12PepS1NNVqgjpTKzPqtq+Hjq6Y/1WTixcvkpubS0hIiPxs+fbt24vczs3NDaVSycOHDzX2XBVE0zuLVRPGFcXLywsrKyvmz5/P7t27861/9uwZ5cqVo0aNGpiZmREbG5tvREBJSU5O5s8//5R7jM6ePYuOjg61atUCXk1QtWLFCjp27Ai8GiXx6NEjreOfO3cuZcuWpX379kRERFC7dm0qVapE5cqVuXnzJr6+vhq3c3Z2ZsOGDWRkZMiV1rNnz2oMe/XqVXr16lVkWtatW6d1uq2trTU2lJw6dYpu3brxySefAK9+QN+4cUOtp1KhUNCyZUtatmzJtGnTqFq1Krt372bs2LHAqzLo5ubGpEmTaN68OZs3b6ZZs2Y0aNCA69evF9lAU5hatWrle1b5/Pnzav83aNCA2NjYf7QfMzMz+vbtS9++fenVqxc+Pj48efJEY2+0qampWk+mqakpFhYWWu1XR0eHjz/+mA0bNjB9+vR8j4+kpaVhaGiInp4ebm5uxMbGapX+ly9fsnr1alq3bi1Xoosqi66uroSFhRV4nPCqcr9v3z46duyIt7c3R44cUTv2jIwMEhMTcXNzKzBt7yK/rl27xuPHj5k7d678qNKFCxc0xnn27Fm5Iefp06fcuHFD7dGO4irqunhd3uO+e/cuenp6WpfZ/v37s2zZMlasWKFxbgzVPfdNOTs7c+fOHe7fvy/3hhd0n8pLm3tgSRK/KgRBEARBC72+7ldkZVxHV4ePZn78jlL071SzZk2ys7NZvnw5N2/eZMOGDQUOm8zL0dERX19fBg4cyK5du7h16xbnzp1jzpw57N+/v0TSamJiQlhYGPv376dr164cO3aMpKQkLly4wIQJExg2bBjw6se3h4cHJ0+ezBdHZmYmDx48UFuKU4EuiKGhIX5+fkRHRxMZGUlgYCB9+vSRGyQcHBzYsGEDcXFxREVF4evrq1UPZF4LFy7E19eXdu3ayT2CM2fOZM6cOSxbtowbN24QExNDeHi4/E72/v37o1AoCAgIIDY2lgMHDqgNGVZJSkri3r17eHh4vGFOaMfBwUHuOYyLi+Ozzz7jr7/+ktdHRUURHBzMhQsXSE5OZteuXfz99984Oztz69YtJk2axJkzZ7h9+zZHjhwhPj5ergxNmzaN9evXM3PmTP744w/i4uLYunUrU6ZM0Tp9n332GdeuXWPixIncuHGD7du3yw0Wqt66iRMncvr0aUaNGsWVK1eIj4/n559/ZtSoUYXGvWjRIrZs2cK1a9e4ceMGP/74I9bW1m+lAqTJ7NmzsbW1pWnTpqxfv57Y2Fji4+P5/vvvcXNzk3stvb29OXPmDEpl/jdwPHz4kAcPHhAfH8/WrVtp2bIljx49UnvspKiy2K9fP6ytrenevTunTp3i5s2b7Ny5M9/s3iYmJuzfvx89PT06dOig9oaLs2fPyqNVSoo2+WVnZ4e+vr5839yzZ0+B7yj/+uuvOX78OFevXsXf3x8rKyu6d+/+j9JW2HVREpo2bcqECRMYN24cEyZMkK+548eP07t3b3744Ye3sh8PDw8cHR3V7qFBQUFabVtUuStJouIuCIIgCEXQ0dWhVktnBi4bgqmV5mF+plZmDFw2hFotnUVveyHq1avHokWLmDdvHnXr1mXTpk1av5oqPDycgQMHMm7cOGrVqkX37t05f/682jDht61bt26cPn2aMmXK0L9/f5ycnOjXrx8pKSlqs08PGTKErVu35hsKeejQIWxsbNSWDz/88I3TVbNmTXr27EnHjh3x8vLC1dVVbXK/tWvX8vTpUxo0aMCAAQMIDAz8R+9vXrx4MX369KFdu3bcuHGDIUOGEBYWRnh4OC4uLrRp04Z169bJw2bLli3L3r17iYmJwc3NjaCgIObNm5cv3i1btuDl5VWiE6XlNWXKFBo0aIC3tzfu7u5yhU7FzMyM3377jY4dO+Lo6MiUKVMICQmhQ4cOGBsbc+3aNT766CMcHR0ZOnQoI0eOlJ9r9vb2Zt++fRw5coTGjRvTrFkzFi9eXKxjq1atGjt27GDXrl24urqycuVKuSKhmkPC1dWVX3/9lRs3btCqVSvc3NyYNm2axkkR8zI1NWX+/Pk0atSIxo0bk5SUxIEDBwp8m8KbsrCw4OzZs3zyySd88803uLm50apVK7Zs2cKCBQvkZ4E7dOiAnp4ex44dyxdHrVq1qFy5Mg0bNmTu3Ll4eHhw9epVtRESRZVFfX19jhw5QsWKFenYsSMuLi7MnTtX42MdZcuW5eDBg0iSRKdOneTXnW3ZsgVfX98SnUxbm/yqUKEC69at48cff6R27drMnTtXY4MYvBot88UXX9CwYUMePHjA3r175ckli6uw66KkzJs3j82bNxMVFYW3tzd16tRh7NixuLq65nsd3D+lo6PD7t27efnyJU2aNGHIkCFaTWoHRZe7kqSQSmo+/0KkpqZibm5OSkqKmJxOEARB+NfIVb6qlF09Hk3MkWhepqZjZGaMi1c96rZ/Nfv5f7nSLr6/CyZJEk2bNmXMmDHyzOGCZllZWfJs9S1btizt5Ly3Zs+ezapVq0rs1W3vg++++449e/Zw+PDh0k5KPo8ePaJWrVpcuHDhnVTKBKEo4hl3QRAEQdCSqlJep50rrl7/98yjMkf5n66wC0VTKBSsXr2amJiY0k7Key85OZnJkyeLSvtrVqxYQePGjbG0tOTUqVMsWLCgyGHw/3afffYZz5494/nz52rPib8PkpKSWLFihai0C+8N0eMuCIIgCIJWxPe3IJScMWPGsG3bNp48eYKdnR0DBgxg0qRJ6OmJfjZBEETFXRAEQRAELYnvb0EQBEEoHWJcnyAIgiAIgiAIgiC8x0TFXRAEQRD+RZQ5ykL/FwRBEAThv0c8NCMIgiAI/wKqGe2vn/iduKPRZDx/iaGpEc6e9XD6H5jRXhAEQRD+l4mKuyAIgiC856RciZtnrrF3xlZePH6uti7uWDQmlqZ0mfExNVo4o9BRlFIqBUEQBEEoKaJpXhAEQRDeY7nKXBJPx7Ft9Np8lXaVF4+fs230WhJPx8k988K7N3XqVIYOHVri+5kxYwb169cvkbgjIiJQKBQ8e/bsrcWZlJSEQqHgypUrAMTGxvLBBx/w4sWLt7YPba1bt45y5coVGsbf35/u3bvL/7u7uzN69OhCt7G3t2fJkiVvnL733dq1a/Hy8iqVfRd1Hr766is+//zzd5egN6TNdfx6WRT+t4mKuyAIgiC85/bO2IpURIVcUuayb+a2d5Siovn7+6NQKPItCQkJpZ20Yrt8+TK9e/emUqVKGBoa4uDgQEBAADdu3JDDPHjwgKVLlxIUFCR/VlAe+Pj4lMZhvDdq165Ns2bNWLRokVbh/f39SUpK0irsL7/8QseOHbG0tMTY2JjatWszbtw47t27p3X6li5dyrp167QO/1+QkJDAoEGD+OCDDzAwMKBatWr069ePCxcuyGEyMjKYOnUq06dPlz+bMWOGXK719PSwsrKidevWLFmyhMzMzHd6DOPHj+eHH37g5s2bJb4vbfLrbXjfy+K6devk86+jo8MHH3zAoEGDePjwYWkn7T9JVNwFQRAE4T2lzFESdzy6wJ7216U9SuXaid/fmwnrfHx8uH//vtpSrVq1YsejVCrJzS2dkQT79u2jWbNmZGZmsmnTJuLi4ti4cSPm5uZMnTpVDhcWFkaLFi2oWrWq2vaa8mDLli3v+jC0kp2d/c72NWjQIFauXElOTo7G9U+ePOG7774j71uLExMT2bRpU4FxhoaG4uHhgbW1NTt37iQ2NpZVq1aRkpJCSEiI1mkzNzcvsle+uN5l3trb2xMREaF1+AsXLtCwYUNu3LhBaGgosbGx7N69GycnJ8aNGyeH27FjB2ZmZrRs2VJt+zp16nD//n2Sk5P55Zdf6N27N3PmzKFFixY8f67dvettsLKywtvbm5UrVxZru5LKr7ehJMpiYWbMmIG/v3+xtjEzM+P+/fvcvXuXNWvWcPDgQQYMGFAyCfwXkCSpwPvamyrVint2drZYxCIWsYhFLGIpYNHV0+Xa0ehifbfGHY1GV0+3xNIEr97nnncpqGfNwMAAa2trtUVXV5dFixbh4uKCiYkJtra2jBgxgrS0NHk71XDmPXv2ULt2bQwMDEhOTiYzM5Px48dTpUoVTExMaNq0abF+cBdXeno6gwYNomPHjuzZswcPDw+qVatG06ZNWbhwIaGhoXLYrVu30qVLF63yoHz58vJ6hUJBaGgonTt3xtjYGGdnZ86cOUNCQgLu7u6YmJjQokULEhMT88UdGhqKra0txsbG9OnTh5SUFHnd+fPn8fT0xMrKCnNzc9q0acOlS5fUtlcoFKxcuZKuXbtiYmLC7NmzNeZBhw4daNmypTx8PiwsDGdnZwwNDXFycmLFihVq25w7dw43NzcMDQ1p1KgRly9fzhevp6cnT5484ddff9WY94aGhty7dw8fHx/u3r3LqlWr8Pf3L7Dh5+7duwQGBhIYGMj333+Pu7s79vb2tG7dmrCwMKZNm6YW/vDhwzg7O1O2bFm5cUWlqOHJDx8+pEuXLhgZGVGtWjWNjQkF5e3PP/9MgwYNMDQ0pHr16sycOVPtR75CoSAsLIwePXpgbGyMg4MDe/bsKTAtb0qSJPz9/XFwcCAyMpJOnTpRo0YN6tevz/Tp0/n555/lsAWVcT09PaytralcuTIuLi58/vnn/Prrr1y9epV58+bJ4bS5fk+dOoW7uzvGxsaUL18eb29vnj59qjHt+/fvx9zcXC3/u3TpwtatW98wVwpWnPyaOHEijo6OGBsbU716daZOnSrfQ/Mq7DrW9NhGYGAgEyZMwMLCAmtra2bMmKGWvhkzZmBnZ4eBgQGVK1cmMDCwRPJCRaFQyOe/Q4cOBAYGcuzYMV6+fMmhQ4f48MMPKVeuHJaWlnTu3FntXpaVlcWoUaOwsbHB0NCQqlWrMmfOHK2OpajypPoeKexaz8nJITAwUE7fxIkT8fPzU8vz3Nxc5syZQ7Vq1TAyMqJevXrs2LFDXq96vOjgwYM0bNgQAwMDTp48SXR0NG3btsXU1BQzMzMaNmz4xiMySnVyuiNHjmBsbFyaSRAEQRCE95KRkRFeXl5kPH9ZrO0yUtOBV0OGX74s3rZFSU9/Fbetra3a59OnT1f78VgUHR0dli1bRrVq1bh58yYjRoxgwoQJahXA9PR05s2bR1hYGJaWllSsWJFRo0YRGxvL1q1bqVy5Mrt378bHx4eYmBgcHBz+0THl5uaio6O5H+Pw4cM8evSICRMmaFyv6gl78uQJsbGxNGrU6B+lYdasWSxatIhFixYxceJE+vfvT/Xq1Zk0aRJ2dnZ8+umnjBo1ioMHD8rbJCQksH37dvbu3UtqaiqDBw9mxIgRciXm+fPn+Pn5sXz5ciRJIiQkhI4dOxIfH4+pqakcz4wZM5g7dy5LlixBT09PbZjxs2fP6NSpE2XLluXo0aMYGxuzadMmpk2bxrfffoubmxuXL18mICAAExMT/Pz8SEtLo3Pnznh6erJx40Zu3brFF198ke+Y9fX1qV+/PpGRkbRv3z7femNjY4KDgzlw4ABdu3YlJyeHEydOUKZMGY15+OOPP5KVlVXkuYJXZWvhwoVs2LABHR0dPvnkE8aPH19ob35e/v7+/Pnnn/zyyy+UKVOGwMBAjUODX8/byMhIBg4cyLJly2jVqhWJiYnynAh5h5/PnDmT+fPns2DBApYvX46vry+3b9/GwsJCq/QVx5UrV/jjjz/YvHmzxusgb76dPHlS655UJycnOnTowK5du/jmm28Airx+r1y5Qvv27fn0009ZunQpenp6/PLLLyiV+UcQbd68mWHDhrF582Y6d+4sf96kSRPu3r1LUlIS9vb2xcsMLRQnv0xNTVm3bh2VK1cmJiaGgIAATE1N1cpoUdexJj/88ANjx44lKiqKM2fO4O/vT8uWLfH09GTnzp0sXryYrVu3UqdOHR48eEB0dPEaf9+UkZERubm55OTk8OLFC8aOHYurqytpaWlMmzaNHj16cOXKFfl7YM+ePWzfvh07Ozvu3LnDnTt3AIo8Fm2+D4q61ufNm8emTZsIDw/H2dmZpUuX8tNPP9G2bVt5P3PmzGHjxo2sWrUKBwcHfvvtNz755BMqVKhAmzZt5HBfffUVCxcupHr16pQvX57WrVvj5ubGypUr0dXV5cqVKwXev7RVqhV3Ly8vzMzMSjMJgiAIgvBeMzQ1Kl54s1cN4nl/eLwtqampANy5c0ft+9vAwEBj+H379lG2bFn5/w4dOvDjjz+qTTBlb2/PN998w7Bhw9Qq7tnZ2axYsYJ69V696i45OZnw8HCSk5OpXLky8OqZ1kOHDhEeHk5wcLDGNCQmJjJ16lSOHTtG+fLl6dGjBwMGDKBOnTrExMQwefJk9u7dq3Hb+Ph44FUlpDDJyclIkiSnq7A8AJg8eTKTJ0+W/x80aBB9+vQBXvXSNW/enKlTp+Lt7Q3AF198waBBg9TiyMjIYP369VSpUgWA5cuX06lTJ0JCQrC2tqZdu3Zq4VevXk25cuX49ddf1So6/fv3V4tbVXF/8OABffv2xcHBgc2bN6Ovrw+8qmCGhITQs2dPAKpVq0ZsbCyhoaH4+fmxefNmcnNzWbt2LYaGhtSpU4e7d+8yfPjwfHlTuXJlbt++rTFPMzIyCA4OJioqCnd3dxo1aoSHhwcLFiygSZMm+cLHx8djZmaGjY2Nxvjyys7OZtWqVdSoUQN4VQH4+uuvi9wO4MaNGxw8eJBz587RuHFj4NWEbc7OzvnCvp63n376KV999RV+fn4AVK9enVmzZjFhwgS1iru/vz/9+vUDIDg4mGXLlnHu3LkSmRtB2zL+7NkzUlJSNJbxgjg5OXHkyBFAu+t3/vz5NGrUSO0+UKdOnXzxfvfddwQFBbF37161ihMgx3379u0Sqbhrm18AU6ZMkf+2t7dn/PjxbN26Va3iXtR1rImrq6tcXhwcHPj22285fvw4np6eJCcnY21tjYeHB2XKlMHOzk7j9VJS4uPjWbVqFY0aNcLU1JSPPvpIbf33339PhQoViI2NpW7duiQnJ+Pg4MCHH36IQqFQe9SosGPR9vugqGt9+fLlTJo0iR49egDw7bffcuDAAXl9ZmYmwcHBHDt2jObNmwOvrtuTJ08SGhqqVv6+/vprPD091dL/5ZdfymXlnzYu51WqFfcyZcq8ccuDIAiCIPxXKXOUOHvWI+6Y9j0mzp71UOYoS+T7VRWnmZmZVg3vbdu2VXve1MTEBIBjx44xZ84crl27RmpqKjk5OWRkZJCeni6PxNPX18fV1VXeNiYmBqVSiaOjo9o+MjMzsbS0LDANY8aMoWXLlkyaNImbN2+yZcsWGjduTHZ2NlZWVsycObPAbfM+X10Y1cgGQ0PDfOtezwMgX89p3uOsVKkSAC4uLmqfZWRkkJqaKue7nZ2d/GMfoHnz5uTm5nL9+nWsra3566+/mDJlChERETx8+BClUkl6ejrJyclq+y5olICnpydNmjRh27Zt6OrqAvDixQsSExMZPHgwAQEBcticnBzMzc0BiIuLw9XVVS0vVD94X2dkZCSP4nhdeno6lSpV4tChQwwaNIhhw4YREBDAmTNnNFZEJElCodDuVYjGxsbyD3kAGxsbrSfTiouLQ09Pj4YNG8qfOTk5aXwO+fW8jY6O5tSpU2qPJCiVynxlP295MDExwczMrND0DRs2jI0bN8r/qx5vUJ03QO1RlLzeRhkvSN5zos31e+XKFXr37l1onDt27ODhw4ecOnVKbjjJy8joVUNnQeUK3k1+AWzbto1ly5aRmJhIWloaOTk5+e6bRV3HmuQtH6Befnv37s2SJUuoXr06Pj4+dOzYkS5duqCnp7nKFxkZSYcOHeT/s7KykCRJbSh4aGgovr6+BR5nSkoKZcuWJTc3l4yMDD788EPCwsKAVxX5adOmERUVxaNHj+S5SpKTk6lbty7+/v54enpSq1YtfHx86Ny5s/zWgsKORdvvg8Ku9ZSUFP766y+1+4muri4NGzaU05mQkEB6erpahVyVT25ubmqfvX69jx07liFDhrBhwwY8PDzo3bu3Wlr+CfEed0EQBEF4T+nq6eLUvh4mlqZaTVBX1soMp3au6Oi+H3PPmpiYULNmTbXPkpKS6Ny5M8OHD2f27NlYWFhw8uRJBg8eTFZWllx5MTIyUquIpaWloaury8WLF9V+YAP5erTzWr9+vVypcnFxoVu3bmRmZvL06dMCfxirqH4UXrt2rcDKJ7yaFAvg6dOnVKhQocg8eF3eRhbVMWv6rDgT9Pn5+fH48WOWLl1K1apVMTAwoHnz5mRlZeVLnyadOnWSJ3hTNSKoKjNr1qyhadOmauFfPyfaePLkSYE/ZC0sLBg5cqTaZzVq1CgwvKOjIykpKdy/f7/IXvfXG7UUCkWxKmTaej1v09LSmDlzpjxaIa+8FWJN6Svs3H/99deMHz9e/t/d3Z158+blO0ea5C3jr1dE8rK0tEShUBT4vLkmcXFx8pwE2ly/qkp3Ydzc3Lh06RLff/89jRo1ytdY8+TJE4B812Fe7yK/zpw5g6+vLzNnzsTb2xtzc3O2bt1arEkSC1JY+bC1teX69escO3aMo0ePMmLECBYsWMCvv/6qsTG3UaNG8msaAZYtW8a9e/fU5iZQNSYWxNTUlEuXLqGjo4ONjY3aeezSpQtVq1ZlzZo1VK5cmdzcXOrWrSvfhxo0aMCtW7c4ePAgx44do0+fPnh4eLBjx45Cj0Xb74M3vdZV97z9+/erNbBA/pFmr1/vM2bMoH///uzfv5+DBw8yffp0tm7dKvfu/xOi4i4IgiAI77kuMz5m2+i1hb4STqGrQ+fpH7/DVP0zFy9eJDc3l5CQEPkZ0e3btxe5nZubG0qlkocPH9KqVSut96epJ1Q1YVxRvLy8sLKyYv78+ezevTvf+mfPnlGuXDlq1KiBmZkZsbGx+XqASkpycjJ//vmnPEz07Nmz6OjoUKtWLeDVJF8rVqygY8eOwKvHGx49eqR1/HPnzqVs2bK0b9+eiIgIateuTaVKlahcuTI3b94ssAfO2dmZDRs2kJGRIVdGz549qzHs1atX6dWrV5Fp0eZ1WL169eKrr75i/vz5LF68ON961bl6U05OTuTk5HDx4kW5x/f69etavfe+QYMGXL9+vciGnOKqWLEiFStWlP/X09OjSpUqWu2nfv361K5dm5CQEPr27ZvvuW1Vvunr61O7dm1iY2O1eo/7tWvXOHToEJMmTQK0u35dXV05fvx4oaNgatSoQUhICO7u7ujq6vLtt9+qrb969SplypTROMRe5V3k1+nTp6latara6yE1PRZS1HX8TxgZGdGlSxe6dOnCyJEjcXJyIiYmhgYNGmgMm/e4LSwsSE1NLVYZ1dHR0Rj+8ePHXL9+nTVr1sjn/OTJk/nCmZmZ0bdvX/r27UuvXr3w8fHhyZMnWFhYFHgs//T7IC9zc3MqVarE+fPnad26NfBqBMylS5eoX78+gNrkqK8/lqENR0dHHB0dGTNmDP369SM8PFxU3AVBEAThv0pHV4caLZzpu2Qw+2ZuI+1Rar4wZa3M6Dy9LzVaOKHQ0W64cGmpWbMm2dnZLF++nC5dunDq1ClWrVpV5HaOjo74+voycOBAQkJCcHNz4++//+b48eO4urrSqVOnt55WExMTwsLC6N27N127diUwMJCaNWvy6NEjtm/fTnJyMlu3bkVHRwcPDw9OnjyZbzbyzMxMHjx4oPaZ6n3Xb8LQ0BA/Pz8WLlxIamoqgYGB9OnTR26QcHBwYMOGDTRq1IjU1FS+/PJLrXo081q4cCFKpZJ27doRERGBk5MTM2fOJDAwEHNzc3x8fMjMzOTChQs8ffqUsWPH0r9/f4KCgggICGDSpEkkJSWxcOHCfHEnJSVx7949PDw83igfVGxtbVm8eDGjRo0iNTWVgQMHYm9vz927d1m/fj1ly5Z9K72dqiG9n332GStXrkRPT4/Ro0drlbfTpk2jc+fO2NnZ0atXL3R0dIiOjubq1avyBG7vmkKhIDw8HA8PD1q1akVQUBBOTk6kpaWxd+9ejhw5Is/87+3tzcmTJ9XmqIBXj0o8ePCA3NxcHj9+TEREBN988w3169fnyy+/BLS7fidNmoSLiwsjRoxg2LBh6Ovry6+Xy3u9ODo68ssvv+Du7o6enh5LliyR10VGRtKqVatil/W3nV8ODg7y/aFx48bs379fY+NfUddxca1btw6lUknTpk0xNjZm48aNGBkZ5XtN5btQvnx5LC0tWb16NTY2NiQnJ/PVV1+phVm0aBE2Nja4ubmho6PDjz/+iLW1NeXKlSv0WCwtLd/K98Hnn3/OnDlzqFmzJk5OTixfvpynT5/KIzlMTU0ZP348Y8aMITc3lw8//JCUlBROnTqFmZmZPF/F616+fMmXX35Jr169qFatGnfv3uX8+fP5nvkvrvdjLJ0gCIIgCAVS6Cio3tyJwEPT6TnfD2fP+lRr6oizZ316zvcj8NB0qjd//yvtAPXq1WPRokXMmzePunXrsmnTJvn1P0UJDw9n4MCBjBs3jlq1atG9e3fOnz+PnZ1diaW3W7dunD59mjJlytC/f3+cnJzo168fKSkpapWtIUOGsHXr1nxDmg8dOoSNjY3a8uGHH75xumrWrEnPnj3p2LEjXl5euLq6qk3qtXbtWp4+fUqDBg0YMGAAgYGBar2M2lq8eDF9+vShXbt23LhxgyFDhhAWFkZ4eDguLi60adOGdevWyUOiy5Yty969e+VesaCgILVhtypbtmzBy8vrrVYoRowYwZEjR7h37x49evTAycmJIUOGYGZmpjY0+k2Fh4dTuXJl2rRpQ8+ePRk6dKhWeevt7c2+ffs4cuQIjRs3plmzZixevLhUKlV5NWnShAsXLlCzZk0CAgJwdnama9eu/PHHH2qV4sGDB3PgwAG115UB/PHHH9jY2GBnZ4e7uzvbt29n0qRJREZGqg1bLur6dXR05MiRI0RHR9OkSROaN2/Ozz//rPH57Fq1anHixAm2bNmi9u70rVu3qs2/UBK0ya+uXbsyZswYRo0aRf369Tl9+jRTp07NF1dR13FxlStXjjVr1tCyZUtcXV05duwYe/fuLXQekJKio6PD1q1buXjxInXr1mXMmDEsWLBALYypqak8KWHjxo1JSkriwIED6OjoFHksb+P7YOLEifTr14+BAwfSvHlzypYti7e3t9qjK7NmzWLq1KnMmTMHZ2dnfHx82L9/f4GvpoRXjw49fvyYgQMH4ujoSJ8+fejQoUOho0m0oZBK4qGeIqSmpmJubk5KSoqYVV4QBEEQikGZo0RXT7fA/0uS+P4umCRJNG3aVB4SKRQsKytLnq2+ZcuWpZ0coRh69+5NgwYN5CHw75ODBw8ybtw4fv/99wInYxOEwuTm5uLs7EyfPn2YNWtWaScnH9HjLgiCIAj/Iq9X0t9VpV0onEKhYPXq1eTk5JR2Ut57ycnJTJ48WVTa/4UWLFhQ6GSQpenFixeEh4eLSrugtdu3b7NmzRpu3LhBTEwMw4cP59atW/Tv37+0k6aR6HEXBEEQBEEr4vtbEARB+K+4c+cOH3/8MVevXkWSJOrWrcvcuXPlyereN6JJShAEQRAEQRAEQfifYmtry6lTp0o7GVoTQ+UFQRAEQRAEQRAE4T0mKu6CIAiC8C+Sm6Ms9H9BEARBEP57xFB5QRAEQfgXyFW+es1Ywi+/E3/sdzKep2NoaoyDhys129UDXr3zXRAEQRCE/x7xDS8IgiAI7zkpV+L22euEdZzJgUkbiD8ezZ1z8cQfj+bApA2EdZzJ7bPXkXLf+XyzQh5Tp05l6NChpbJve3t7tXduv+7jjz8mJCTk3SWoGCIiIlAoFDx79qzAMOvWraNcuXLvLE0lrXXr1mzevLnE9+Pu7s7o0aNLJO4ZM2ZQv379txrn6+d51apVdOnS5a3uQ/j306bsvV72i7pHwqu3g/z0009vnL6SIirugiAIgvAey1XmknTmGnvGriX98XONYdIfP2fP2LUknbkm98yXNn9/fxQKRb4lISGhtJNWbJcvX6Z3795UqlQJQ0NDHBwcCAgI4MaNG3KYBw8esHTpUoKCguTP8uZBmTJlqFSpEp6ennz//ffk5r7b8zRlyhRmz55NSkpKkWGTkpLw9/fXKt7U1FSCgoJwcnLC0NAQa2trPDw82LVrF2/zxUV9+/ZVy+/31c6dO3F3d8fc3JyyZcvi6urK119/zZMnT+Qwe/bs4a+//uLjjz+WP7O3t9d4vcydO7c0DuO98emnn3Lp0iUiIyNLdD95r1V9fX1q1qzJ1/+PvfOOquLqHvZzQaQXKVIMCCogoij2XhGwRbGjsb2W1xZ7R8XescdKxKgosceGBUtiidiJBhsoYozGqAgiSrvz/cF358eVdlEsyXuetWate+e0PafMzD5nnz0zZojPO34mNBlHBbF79+4v8lvsH4JQ3AUCgUAg+MI5NiMMqQCFXMpUcmxG2CeSSDN8fX15/Pix2uHk5FTofDIzMz+5oqviwIED1K5dm9TUVEJDQ7l58yZbtmzB1NSUKVOmyPGCg4OpW7cupUuXVkuvqoO4uDjCw8Np0qQJw4cPp3Xr1p9UKahYsSJly5Zly5YtecYJDQ0lNjZW/i9JEt999x0JCQm5xn/58iV169Zl06ZNTJw4kStXrvDLL7/QpUsXxo0bp9Ekgabo6+tTsmTJIsuvIE6dOoWjo2Oh0gQEBNClSxdq1KhBeHg4N27cICgoiKioKDZv3izHW758OX369EFLS/01fMaMGTnGy7ffflsUl1OkSJL0yfpu8eLF6datG8uXLy9UusaNG7Nx48ZCpVGN1bt37zJ69GimTZvGwoULC5XHv4m0tLQPzuNjjqOCMDc3x9jYuJAS5096enqR5ldYhOIuEAgEAsEXijIjk7snovJcaX+XlOeviDnx2xfjsE5XVxcbGxu1Q1tbm8WLF1OpUiUMDQ2xt7dn8ODBJCcny+lU5rL79u2jQoUK6OrqEh8fT2pqKmPGjKFUqVIYGhpSq1YtTp069dHkT0lJoU+fPrRs2ZJ9+/bh5eWFk5MTtWrVYtGiRaxdu1aOGxYWlqtJr6oOSpUqRdWqVZk0aRI//fQT4eHhaorFy5cv6devH1ZWVpiYmNC0aVOioqLU8tq/fz81atRAT08PS0tL/Pz88pQ9ODgYMzMzjh8/Lp9r06YNYWF5T+44OTnRq1cv1qxZwx9//IGvry+PHj1CV1c31/iTJk0iLi6OyMhIevXqRYUKFXBxcaF///5cu3YNIyMjADZv3kz16tUxNjbGxsaGbt268fTp0xz5nT17Fg8PD/T09KhduzY3btyQw941oVaZym7evBlHR0dMTU3p2rUrr17931jZuXMnlSpVQl9fHwsLC7y8vHj9+nWe1/8hXLhwgTlz5hAUFMTChQupW7cujo6ONG/enF27dtGrVy8A/v77b06cOJFrX1HVT/bD0NAQ+L/tBEeOHMHT0xN9fX2aNm3K06dPCQ8Px83NDRMTE7p160ZKSopavhkZGQwdOhRTU1MsLS2ZMmWKmjVEQe2jKjs8PJxq1aqhq6vLmTNncsgfGxtLmTJlGDp0KJIkaTReN27ciIODAwYGBvj5+fH8+fMc+bZp04Z9+/bx5s0bzRvkPVCN1dKlSzNo0CC8vLzYt28fQIH3rAcPHtCmTRtKlCiBoaEh7u7uHDp0CICEhAS6d++OlZUV+vr6ODs7ExISIqd9+PAhnTt3xszMDHNzc9q2bUtcXJwc3rt3b9q1a8eiRYuwtbXFwsKCIUOGqCmRjx8/plWrVujr6+Pk5MTWrVtzmIYXdI9Rjang4GCcnJzQ09MDvsxxpCK/8V/QNpG7d+/SsGFD9PT0qFChAseOHVMLj4uLQ6FQ8OOPP9KoUSP09PQIDQ0Fsu6vbm5u6OnpUb58eVatWpUj3e7du2nSpAkGBgZUrlyZX3/99YPr57M6p0tPT//sMxcCgUAgEHyp6OjoEBPxW6HS3D0ehUvzKh/l+arKMykpSe28rq5unspdbmhpabF8+XKcnJy4d+8egwcPZty4cWovPykpKcyfP5/g4GAsLCwoWbIkQ4cOJTo6mrCwMOzs7NizZw++vr5cv34dZ2fn97ompVKZY+VTxZEjR3j27Bnjxo3LNVylSL548YLo6GiqV6+uUZlNmzalcuXK7N69m379+gHQqVMn9PX1CQ8Px9TUlLVr19KsWTPu3LmDubk5Bw8exM/Pj4CAADZt2kRaWpqsGLzLggULWLBgAUePHqVmzZry+Zo1azJ79mxSU1Nzba+6dety8uRJvLy8OHv2LPv376dFixa5lqFUKgkLC6N79+7Y2dnlCFcp7ZDVb2bOnImrqytPnz5l1KhR9O7dO4f8Y8eOZdmyZdjY2DBp0iTatGnDnTt30NHRyVWG2NhY9u7dy4EDB0hISKBz587MmzeP2bNn8/jxY/z9/VmwYAF+fn68evWK06dPF6n5fnZCQ0MxMjJi8ODBuYar+sqZM2cwMDDAzc3tvcqZNm0aK1euxMDAgM6dO9O5c2d0dXXZunUrycnJ+Pn5sWLFCsaPHy+n+eGHH+jbty8XLlzg0qVLDBgwAAcHB/r37w9o3j4TJkxg0aJFlClThhIlSqgp4b/99hs+Pj707duXWbNmARQ4XiMjI+nbty9z586lXbt2HD58mMDAwBzXXL16dTIyMoiMjKRx48bvVW/vg76+vjyRUNA9a8iQIaSlpfHLL79gaGhIdHS0PAamTJlCdHQ04eHhWFpaEhMTI09CpKen4+PjQ506dTh9+jTFihVj1qxZ+Pr68ttvv1G8eHEATp48ia2tLSdPniQmJoYuXbpQpUoVuQ179uzJs2fPOHXqFDo6OowaNSrH5FhB9xiAmJgYdu3axe7du9HW1v5ixxHkP/4LQqlU0r59e6ytrYmMjCQxMTFPJX/ChAkEBQXh6ekpK+9Tp05l5cqVeHp6cvXqVfr374+hoaHaxEJAQACLFi3C2dmZgIAA/P39iYmJoVix91e/P6vifvToUQwMDD6nCAKBQCAQfJHo6+vj7e3N21cpBUfORuqrrBfCkydPFvkKlWolz97eXu18YGAg06ZNyxH/wIEDagpcixYt2LFjRw6HQbNmzWLgwIFqint6ejqrVq2icuUsj/nx8fGEhIQQHx8vK4pjxozh8OHDhISEMGfOnFxljo2NZcqUKURERFCiRAn8/Pzo0aMH7u7uXL9+nUmTJrF///5c0969exeA8uXL51sv8fHxSJKUqwKbF+XLl+e337ImZc6cOcOFCxd4+vSprFAvWrSIvXv3snPnTgYMGMDs2bPp2rUr06dPl/NQ1U12xo8fz+bNm/n5559xd3dXC7OzsyMtLY0nT57kMOkHiIyMZOzYsdStWxcdHR2WLl3Kr7/+yqRJk+TVNxXPnj0jISGhwLqBrH3KKsqUKcPy5cupUaMGycnJav0jMDCQ5s2bA1nK5ldffcWePXvo3LlzrvkqlUo2btwom8P26NGD48ePy4p7RkYG7du3l6+1UqVKBcr6vty9e5cyZcrkOcmg4sGDB1hbW+c6WTR+/HgmT56sdi48PJwGDRrI/2fNmkW9evUA6Nu3LxMnTpRXugE6duzIyZMn1RR3e3t7lixZgkKhwNXVlevXr7NkyRJZ6dO0fWbMmCG3T3bOnTtH69atCQgIYPTo0YBm43XZsmX4+vrKE2MuLi6cO3eOw4cPq+VvYGCAqakpDx48yLduiwpJkjh+/DhHjhyRtyoUdM+Kj4+nQ4cOch9TtYcqzNPTU57Yy246/uOPP6JUKgkODkahUAAQEhKCmZkZp06dwtvbG4ASJUqwcuVKtLW1KV++PK1ateL48eP079+fW7duERERwcWLF+UygoOD1SYzNbnHQJZ5/KZNm7CysgLgypUrX+Q4gvzHf0FERERw69Ytjhw5IvfPOXPm5DpROWLECNq3by//DwwMJCgoSD7n5OREdHQ0a9euVVPcx4wZQ6tWrQCYPn067u7uxMTEaHTPzIvPqrh7e3tjYmLyOUUQCAQCgeCLRs+4cBPcusb6ADRp0qTIZVGttD98+FDt+Z3XanuTJk1YvXq1/F9l9hsREcHcuXO5desWSUlJZGRk8PbtW1JSUuQJ/eLFi+Ph4SGnvX79OpmZmbi4uKiVkZqaioWFRZ4yjxw5knr16jFx4kTu3bvHtm3bqFGjBunp6VhaWqopwu+i6aqSaoLkXeU2PyRJkl/Uo6KiSE5OznEdb968kfecX7t2TVa08iIoKIjXr19z6dIlNcVBhb5+Vt9415Raxd27dwkJCUFbW5tp06YREhLCqlWrSElJyXFthVlxu3z5MtOmTSMqKoqEhATZX0F8fDwVKlSQ49WpU0f+bW5ujqurKzdv3swzX0dHR7U9rLa2tvIqY+XKlWnWrBmVKlXCx8cHb29vOnbsSIkSJfLML7uSmpmZSWpqqtq5b775hjVr1uSatjB9Ja9+Mnbs2BxOAUuVKqX2P/uYsLa2xsDAQK2tra2tuXDhglqa2rVry30Nsuo5KCiIzMxMtLW1NW6f3CxK4uPjad68ObNnz1ZTbjUZrzdv3syx3aNOnTo5FHfI6rt59VvIUrqyT969efOG8+fPM3ToUPlcdHQ0Dg4OeeahmmhMT09HqVTSrVs3eUKyoHvWsGHDGDRoEEePHsXLy4sOHTrIbTVo0CA6dOjAlStX8Pb2pl27dtStWxfIGvsxMTE59mK/fftWzd+Eu7s72tra8n9bW1uuX78OwO3btylWrBhVq1aVw8uVK6fW1zW5xwCULl1aVtrhyx1HkP/4L4ibN29ib2+vNtma/f6Tnez9/vXr18TGxtK3b1+1+3FGRgampqZq6bKPVVtbWwCePn36z1XcdXR0NJpREQgEAoHgfxFlRibOXh7cPR5VcOT/j3OzyigzMj/K81WVp4mJiUYT74aGhpQrV07tXFxcHK1bt2bQoEHMnj0bc3Nzzpw5Q9++fUlLS5MVd319fTVlIzk5WVYysr/AgvqL4rts2rRJNq+sVKkSbdu2JTU1lYSEBGxsbPKVX6V03Lp1K8+XOgBLS0sgay9r9pfe/Lh586bsqC85ORlbW9tc9+urZFcp3fnRoEEDDh48yPbt25kwYUKOcJVH5rxk/OabbwDk/bUKhYIhQ4bkGtfKygozMzNu3bqVr0yvX7/Gx8cHHx8fQkNDsbKyIj4+Hh8fnw92fvVuH1coFLLSqa2tzbFjxzh37hxHjx5lxYoVBAQEEBkZmaeDxGvXrsm/IyMjGT9+vFqb5NfnXVxcOHPmDOnp6fmOPUtLyzyd/VlaWuYYL++SPW/V1wqyk70ONKEw7aOaeMuOlZUVdnZ2bNu2jf/85z9yHb3veM2LFy9e5Du2Bg4cqGaZ0b17dzp06KC2UlqQRYxqorF48eLY2dnJJs2a3LP69euHj48PBw8e5OjRo8ydO5egoCC+/fZbWrRowYMHDzh06BDHjh2jWbNmDBkyhEWLFpGcnEy1atXkvdPZyX69H9rOmtxjIGcbf6njCD68TjQle52o/BqsX7+eWrVqqcV7t5+/O1aBD5ZPOKcTCAQCgeALRauYNuWaVsbAQjPPuAYWxpRr6oFWMe2CI38mLl++jFKpJCgoiNq1a+Pi4sKff/5ZYDpPT08yMzN5+vQp5cqVUzvyU8Bz+/a3yglVQXh7e2NpacmCBQtyDVd9d7xs2bKYmJgQHR1dYJ4AJ06c4Pr163To0AGAqlWr8uTJE4oVK5bj2lSTAh4eHmqO5nKjZs2ahIeHM2fOHBYtWpQj/MaNG3z11Vdynnnh6OhYoEduLS0tunbtSmhoaK7tl5ycTEZGBrdu3eL58+fMmzePBg0aUL58+TxXxc6fPy//TkhI4M6dO++9FxyyXpbr1avH9OnTuXr1KsWLF2fPnj15xs9e76VKlcrRHvl5te/WrRvJyclq2z2yo+ornp6ePHnyJE/l/WMQGRmp9v/8+fM4Ozujra1dqPbJDX19fQ4cOICenh4+Pj6yczBNxqubm1uusr1LbGwsb9++xdPTM085zM3N1cpQfYUg+7mC9harJhodHBzU4mp6z7K3t2fgwIHs3r2b0aNHs379ejnMysqKXr16sWXLFpYuXcq6deuArLF/9+7dHLKWK1cuxwpuXri6upKRkcHVq1flczExMWp9TJN7TF58iePoQ3Fzc+Phw4c8fvxYPpdb33sXa2tr7OzsuHfvXo56fJ8vphQWobgLBAKBQPCF03xqVxTa+T+yFdpaNJ/aNd84XwLlypUjPT2dFStWcO/ePTZv3pyn2WR2XFxc6N69Oz179mT37t3cv3+fCxcuMHfuXA4ePPhRZDU0NCQ4OJiDBw/y9ddfExERQVxcHJcuXWLcuHEMHDgQyFJivby8cvW0nZqaypMnT3j06BFXrlxhzpw5tG3bltatW9OzZ08AvLy8qFOnDu3atePo0aPExcVx7tw5AgICuHTpEpC1r3Lbtm0EBgZy8+ZNrl+/zvz583OUV7duXQ4dOsT06dPVPEoDnD59Wt4zWxTMnj0be3t7atWqxaZNm4iOjubu3bts2LABT09PkpOTcXBwoHjx4nJ779u3L89vK8+YMYPjx49z48YNevfujaWlJe3atXsv2SIjI5kzZw6XLl0iPj6e3bt38/fff3/QREB+1KpVi3HjxjF69GjGjRvHr7/+yoMHDzh+/DidOnXihx9+ALIUWktLS86ePZsjj1evXvHkyRO1411HkO9DfHw8o0aN4vbt22zbto0VK1YwfPhwgEK1T14YGhpy8OBBihUrRosWLUhOTtZovA4bNozDhw+zaNEi7t69y8qVK3M1kz99+jRlypShbNmyH1wX74Mm96wRI0Zw5MgR7t+/z5UrVzh58qTc16ZOncpPP/1ETEwMv//+OwcOHJDDunfvjqWlJW3btuX06dPcv3+fU6dOMWzYMP744w+N5CtfvjxeXl4MGDCACxcucPXqVQYMGKBmtaTJPSY3vtRx9KF4eXnh4uJCr169iIqK4vTp0wQEBGiUdvr06cydO5fly5dz584drl+/TkhICIsXLy4S2fJDKO4CgUAgEHzBaGlr4VinPF8v7pvnyruBhTFfL+6LY53yaBWg4H9uKleuzOLFi5k/fz4VK1YkNDSUuXPnapQ2JCSEnj17Mnr0aFxdXWnXrh0XL17Md9/qh9K2bVvOnTuHjo4O3bp1o3z58vj7+5OYmCh7zwbo168fYWFhOUwhDx8+jK2tLY6Ojvj6+nLy5EmWL1/OTz/9JJtWKhQKDh06RMOGDenTpw8uLi507dpVdmQGWZ822rFjB/v27aNKlSo0bdo0x15mFfXr1+fgwYNMnjyZFStWAFl7Zvfu3VvgPvnCYG5uzvnz5/nmm2+YNWsWnp6eNGjQgG3btrFw4UJMTU2xsrJi48aN7NixgwoVKjBv3rxcrQEA5s2bx/Dhw6lWrRpPnjxh//79slftwmJiYsIvv/xCy5YtcXFxYfLkyQQFBeXpJb8omD9/Plu3biUyMhIfHx/c3d0ZNWoUHh4estMqbW1t+vTpk6tp9NSpU7G1tVU78vqiQWHo2bMnb968oWbNmgwZMoThw4fLzsgK0z75YWRkRHh4OJIk0apVK16/fl3geK1duzbr169n2bJlVK5cmaNHj+Zwzgewbdu2Iu23hUWTe1ZmZiZDhgzBzc0NX19fXFxc5FXj4sWLM3HiRDw8PGjYsCHa2tryZxkNDAz45ZdfcHBwoH379ri5udG3b1/evn1bKD9gmzZtwtramoYNG+Ln50f//v0xNjaW/Sloco/JjS91HH0oWlpa7NmzRx4X/fr108ipHWTd64ODgwkJCaFSpUo0atSIjRs3fpIVd4X0sfz550NSUhKmpqYkJiYK53QCgUAgEGiAMjNLIYw58Rt3j0eR+uoNusb6ODerTLmmWU5wPrbSLp7feSNJErVq1WLkyJH4+/t/bnFysHr1avbs2cPRo0c/tyj/8zx58gR3d3euXLmSq3d/wf/x+++/07RpU+7cuaOx6bgA/vjjD+zt7YmIiKBZs2afWxxBEfFZndMJBAKBQCDQDJVSXq5JJVyaV5HPKzMyv/hV9v8FFAoF69atkz09f2no6OjIq++Cz4uNjQ3ff/898fHxQnEvgMePH7Np0yahtBfAiRMnSE5OplKlSjx+/Jhx48bh6OhIw4YNP7dogiJErLgLBAKBQCDQCPH8FggEgi+PI0eOMHr0aO7du4exsTF169Zl6dKlYmLoX4ZQ3AUCgUAgEGiEeH4LBAKBQPB5ELZ1AoFAIBAIBAKBQCAQfMEIxV0gEAgEgn8wyozMfP8LBAKBQCD45yOc0wkEAoFA8A9E5WX+/qnfuJ/Ny7xTs8o4NakMfHwv8wKBQCAQCD4NQnEXCAQCgeAfhqSU+CPyFr/MDOPNi1dqYfdPRKFvbkzDKV2xr+2GQkvxmaQUCAQCgUBQVIipeIFAIBAI/kEoM5U8PH+To2O+z6G0q3jz4hVHx3zPw/M35ZV5wcdnypQpDBgw4LOU7ejoyNKlS/MM79q1K0FBQZ9OIMEXS48ePZgzZ85HL6d37960a9fuo+S9ceNGzMzMijTPU6dOoVAoePnyJQCHDx+mSpUqKJX/jHvou/Lnxseot8/BtGnTqFKlSr5xGjduzIgRI+T/Bd0jIeuznnv37v1g+T4WQnEXCAQCgeAfxi8zw5AKUMilTCW/zPrxE0mUk969e6NQKHIcMTExn02m9+Xq1at06tQJa2tr9PT0cHZ2pn///ty5c0eO8+TJE5YtW0ZAQIB8Lnsd6OjoYG1tTfPmzdmwYcMnVwYmT57M7NmzSUxMLDBuXFwcvXv3LjDetGnT5OsrVqwYjo6OjBw5kuTk5CKQWFBYTp48ScuWLbGwsMDAwIAKFSowevRoHj16JMeJiori0KFDDBs2TD7XuHHjXMfqwIEDP8dlfDH4+vqio6NDaGjoRy8rKSmJgIAAypcvj56eHjY2Nnh5ebF7926K8gNgXbp0Ubtvfans2rWLxo0bY2pqipGRER4eHsyYMYMXL15onMfu3buZOXPmR5Ty0yMUd4FAIBAI/iEoMzK5fzIqz5X2d3nzPIm4k799Nod1vr6+PH78WO1wcnIqdD6ZmZmfbdXrwIED1K5dm9TUVEJDQ7l58yZbtmzB1NSUKVOmyPGCg4OpW7duju8mq+ogLi6O8PBwmjRpwvDhw2ndujUZGRmf7DoqVqxI2bJl2bJlS55xQkNDiY2Nlf9LksR3331HQkJCnmnc3d3l65s/fz7r1q1j9OjRRSr7P4mi6KtxcXEoFIXb4rJ27Vq8vLywsbFh165dREdHs2bNGhITE9UsLVasWEGnTp0wMjJSS9+/f/8cY3XBggUfdB0fi/T09E9WVu/evVm+fHmh00ybNk3j+C9fvqRu3bps2rSJiRMncuXKFX755Re6dOnCuHHjNJps0xR9fX1KlixZZPkVxKlTp3B0dCxUmoCAALp06UKNGjUIDw/nxo0bBAUFERUVxebNmzXOx9zcHGNj40JKnD+fsu/lxmdV3NPT08UhDnGIQxziEIeGh1Yxbe4fjyrUs/b+iSi0imkXmQyQtTqU/UhNTc21bF1dXWxsbNQObW1tFi9eTKVKlTA0NMTe3p7BgwerrdKqzDn37dtHhQoV0NXVJT4+ntTUVMaMGUOpUqUwNDSkVq1anDp16r3fQwoiJSWFPn360LJlS/bt24eXlxdOTk7UqlWLRYsWsXbtWjluWFgYbdq0ybMOSpUqRdWqVZk0aRI//fQT4eHhbNy4UY738uVL+vXrh5WVFSYmJjRt2pSoKPW23r9/PzVq1EBPTw9LS0v8/PzylD04OBgzMzOOHz8un2vTpg1hYWF5pnFycqJXr16sWbOGP/74A19fXx49eoSurm6eaYoVK4aNjQ1fffUVXbp0oXv37uzbtw+AzZs3U716dYyNjbGxsaFbt248ffpUTpuQkED37t2xsrJCX18fZ2dnQkJCAEhLS2Po0KHY2tqip6dH6dKlmTt3rsb1pTKl3bx5M46OjpiamtK1a1devfq/Sa9Xr17RvXt3DA0NsbW1ZcmSJTnMawvqc3n11VOnTlGzZk0MDQ0xMzOjXr16PHjwIM96/BD++OMPhg0bxrBhw9iwYQONGzfG0dGRhg0bEhwczNSpU4GsSYWdO3fm2k8NDAxyjFUTExPg/yYStm/fToMGDdDX16dGjRrcuXOHixcvUr16dYyMjGjRogV///13jrynT58ut9PAgQNJS0uTww4fPkz9+vUxMzPDwsKC1q1bq00eqcr+8ccfadSoEXp6ermugP/9999Ur14dPz8/UlNTUSqVzJ07FycnJ/T19alcuTI7d+5US3Po0CFcXFzQ19enSZMmxMXF5ci3TZs2XLp0SU2mombSpEnExcURGRlJr169qFChAi4uLvTv359r167JkywFjScVZ8+excPDAz09PWrXrs2NGzfksHdN5TUZJzt37qRSpUro6+tjYWGBl5cXr1+//ih1ceHCBebMmUNQUBALFy6kbt26ODo60rx5c3bt2kWvXr3U4ucn97tj+V3u3r1Lw4YN0dPTo0KFChw7dkwtPL++FxwcjJubG3p6epQvX55Vq1blSLd7926aNGmCgYEBlStX5tdff/3g+vmszumOHj2KgYHB5xRBIBAIBIJ/BPr6+nh7e5P66k2h0qW+SgGyzGjfvClc2ndJScnKy97eXu18YGBgoVaYtLS0WL58OU5OTty7d4/Bgwczbtw4tZeflJQU5s+fT3BwMBYWFpQsWZKhQ4cSHR1NWFgYdnZ27NmzB19fX65fv46zs/N7XZNSqURLK/d1jCNHjvDs2TPGjRuXa7jqBfjFixdER0dTvXp1jcps2rQplStXZvfu3fTr1w+ATp06oa+vT3h4OKampqxdu5ZmzZpx584dzM3NOXjwIH5+fgQEBLBp0ybS0tI4dOhQrvkvWLCABQsWcPToUWrWrCmfr1mzJrNnzyY1NTVXZbxu3bqcPHkSLy8vzp49y/79+2nRooVG16RCX19fVszS09OZOXMmrq6uPH36lFGjRtG7d29Z7ilTphAdHU14eDiWlpbExMTIfXT58uXs27eP7du34+DgwMOHD3n48KFcTkH1BRAbG8vevXs5cOAACQkJdO7cmXnz5jF79mwARo0axdmzZ9m3bx/W1tZMnTqVK1euqO2d1aTPvdtXzc3NqVKlCv3792fbtm2kpaVx4cKFQq+ia8qOHTtIS0srsJ/+9ttvJCYmatxP3yUwMJClS5fi4ODAf/7zH7p164axsTHLli3DwMCAzp07M3XqVFavXi2nOX78OHp6epw6dYq4uDj69OmDhYWF3AavX79m1KhReHh4kJyczNSpU/Hz8+PatWtq43LChAkEBQXh6emJnp4eR44ckcMePnxI8+bNqV27Nt9//z3a2trMnj2bLVu2sGbNGpydnfnll1/45ptvsLKyolGjRjx8+JD27dszZMgQBgwYwKVLl3K1FHFwcMDa2prTp09TtmzZ96q3/FAqlYSFhdG9e3fs7OxyhGe3jChoPKkYO3Ysy5Ytw8bGhkmTJtGmTRvu3LmDjo5OrjLkN04eP36Mv78/CxYswM/Pj1evXnH69OkiNd/PTmhoKEZGRgwePDjX8OyTDgWN7/xQKpW0b98ea2trIiMjSUxMzFPJf7fvhYaGMnXqVFauXImnpydXr16lf//+GBoaqk0sBAQEsGjRIpydnQkICMDf35+YmBiKFXt/9fuzKu7e3t7ybJ5AIBAIBIKC0TXWL2T8rAnyJk2afHDZSUlJQNaLcvbnd14rsgcOHFB78WzRogU7duzI4TBo1qxZDBw4UE1xT09PZ9WqVVSunPVpu/j4eEJCQoiPj5dfcMeMGcPhw4cJCQnJ09lWbGwsU6ZMISIighIlSuDn50ePHj1wd3fn+vXrTJo0if379+ea9u7duwCUL18+33qJj49HkqRcX7zzonz58vz2228AnDlzhgsXLvD06VO5LhctWsTevXvZuXMnAwYMYPbs2XTt2pXp06fLeajqJjvjx49n8+bN/Pzzz7i7u6uF2dnZkZaWxpMnT3KY9ANERkYyduxY6tati46ODkuXLuXXX39l0qRJ6OnpFXhNly9fZuvWrTRt2hSA//znP3JYmTJlWL58OTVq1CA5ORkjIyPi4+Px9PSUFcnsJrXx8fE4OztTv359FAqFmrya1BdkvZxv3LhRNpft0aMHx48fZ/bs2bx69YoffviBrVu30qxZMwBCQkLU2lDTPvduX33x4gWJiYm0bt1aVvbc3NwKrL/35e7du5iYmGBra5tvvAcPHqCtrZ2rqfSqVasIDg5WO7d27Vq6d+8u/x8zZgw+Pj4ADB8+HH9/f44fP069evUA6Nu3r5oVCUDx4sXZsGEDBgYGuLu7M2PGDMaOHcvMmTPR0tKiQ4cOavE3bNiAlZUV0dHRVKxYUT4/YsQI2rdvn0Pu27dv07x5c/z8/Fi6dCkKhYLU1FTmzJlDREQEderUAbL635kzZ1i7di2NGjVi9erVlC1bVt5G4OrqyvXr15k/f36OMuzs7D6atcSzZ89ISEgo8B4DBY8nFYGBgTRv3hyAH374ga+++oo9e/bQuXPnXPPNb5w8fvyYjIwM2rdvL4/BSpUqvff1FsTdu3cpU6ZMnpMMmspdEBEREdy6dYsjR47IY3vOnDm5TlS+2/cCAwMJCgqSzzk5OREdHc3atWvVFPcxY8bQqlUrIMvqxN3dnZiYGI3aOi8+q+Kuo6OjUcMIBAKBQCDI2uPu1Kwy909obi7v1LQyyozMInneqvIwMTHRaOK9SZMmaqtvhoaGQNZL09y5c7l16xZJSUlkZGTw9u1bUlJSZEu84sWL4+HhIae9fv06mZmZuLi4qJWRmpqKhYVFnjKMHDmSevXqMXHiRO7du8e2bduoUaMG6enpWFpaqinC76LpqpJqlVgT5TZ73qoV2KioKJKTk3Ncx5s3b2QT3WvXrtG/f/988wwKCuL169dcunSJMmXK5AjX18+a9FFZTrzL3bt3CQkJQVtbm2nTphESEsKqVatISUnJ89quX7+OkZERmZmZpKWl0apVK1auXAlkKfLTpk0jKiqKhIQEee93fHw8FSpUYNCgQXTo0IErV67g7e1Nu3btqFu3LpC1T7h58+a4urri6+tL69at8fb21ri+IGsiIPseV1tbW9m0+N69e6Snp6tZJJiamuLq6qp2bZr0uXf7qrm5Ob1798bHx4fmzZvj5eVF586d81Ws3d3dZeVQ1e+yK2MNGjQgPDw817TZ+1J+vHnzBl1d3Vzjdu/eXc2xIoC1tbXa/+zXqArLrsRZW1vnMN2uXLmymnVtnTp1SE5O5uHDh5QuXZq7d+8ydepUIiMjefbsmVofya6452Yl8ObNGxo0aEC3bt3UvIXHxMSQkpIiK68q0tLS8PT0BODmzZvUqlVLLVyl5L+Lvr5+nmMGslaJ//vf/8r/U1NTUSgULFq0SD4XHh5OgwYNcqQtzMp1QeMpt+swNzfH1dWVmzdv5plvfuOkcuXKNGvWjEqVKuHj44O3tzcdO3akRIkSeeaXvd9mZmaSmpqqdu6bb75hzZo1uaYtTH3kJ3dB3Lx5E3t7e7WJurzaP3vfe/36NbGxsfTt21ftfpyRkYGpqalauuzjRTX2nz59+s9V3AUCgUAgEGiOVjFtnJpURt/cWCMHdfoWJjg28UBL+/O4tDE0NKRcuXJq5+Li4mjdujWDBg1i9uzZmJubc+bMGfr27UtaWpr8kq+vr6+mYCQnJ6Otrc3ly5fR1tZWy/NdR1vZ2bRpk2xeWalSJdq2bUtqaioJCQnY2NjkK79KYbt161aeL3UAlpaWQNaebSsrq3zzVHHz5k3ZUV9ycjK2tra57tdXya5SuvOjQYMGHDx4kO3btzNhwoQc4SqPzHnJ+M033wDIe30VCgVDhgzJt0xXV1f27dtHsWLFsLOzo3jx4kDWC66Pjw8+Pj6EhoZiZWVFfHw8Pj4+sil9ixYtePDgAYcOHeLYsWM0a9aMIUOGsGjRIqpWrcr9+/cJDw8nIiKCzp074+Xlxc6dOzWqLyDHZJVCoSiU4zhN+9y7fRWyVu+HDRvG4cOH+fHHH5k8eTLHjh2jdu3auZZ16NAh2YfEo0ePaNy4MdeuXVMrIy9cXFxITEzk8ePH+U4OWFpakpKSQlpamtxOKkxNTXOM1XfJXp+q6333XGEd87Vp04bSpUuzfv167OzsUCqVVKxYUW0fPPzfpF92dHV18fLy4sCBA4wdO5ZSpUoByP4yDh48KJ/LnqawvHjxIt9x/fXXX6tNAowfP55SpUqpee5/Vw4VVlZWmJmZcevWrXxl0GQ8vS/5jRNtbW2OHTvGuXPnOHr0KCtWrCAgIIDIyMg8HY1m77eRkZGMHz9ebazmN+nr4uLCmTNnSE9PL3Cy+UPHt6Zk73uqvrV+/focEz/v3iNyGy8fKp/wKi8QCAQCwT+MhlO6oihAGVdoa9FwctdPJJHmXL58GaVSSVBQELVr18bFxYU///yzwHSenp5kZmby9OlTypUrp3bkp4Dn9s1ilcO4gvD29sbS0jJP79qq7yWXLVsWExMToqOjC8wT4MSJE1y/fl02E65atSpPnjyhWLFiOa5NNSng4eGh5mguN2rWrEl4eDhz5sxRW+1TcePGDb766is5z7xwdHTMYfKcF8WLF6dcuXI4OjqqKYO3bt3i+fPnzJs3jwYNGlC+fPlcV8OsrKzo1asXW7ZsYenSpaxbt04OMzExoUuXLqxfv54ff/yRXbt28eLFC43qqyBU5rgXL16UzyUmJqp9Kut9+1z29BMnTuTcuXNUrFiRrVu35hm3dOnSct4qk+Ts5eWl+AF07NiR4sWLF9hPVXv3Ne2nRUFUVJSab43z589jZGSEvb09z58/5/bt20yePJlmzZrh5uaW7xcM3kVLS4vNmzdTrVo1mjRpIt9HsjsJfLfdVP453NzcuHDhglp+58+fz1HG27dviY2NlVfqc8PY2FitDGNjY8zNzdXO5TXxoqWlRdeuXQkNDc31PpicnExGRobG4+nd60hISODOnTsftFVDoVBQr149pk+fztWrVylevDh79uzJM/67/fbdcZqfV/tu3bqRnJystm0qO/l9o74wuLm58fDhQx4/fiyfy63938Xa2ho7Ozvu3buXo2+9zxdTCotYcRcIBAKB4B+ElrYW9rXd8F7Ul19m/cib50k54uhbmNBwchfsa5dHofVxHGK9L+XKlSM9PZ0VK1bQpk0bzp49m6fZZHZcXFzo3r07PXv2lB0F/f333xw/fhwPDw95L2FRYmhoSHBwMJ06deLrr79m2LBhlCtXjmfPnrF9+3bi4+MJCwtDS0sLLy8vzpw5Q7t27dTySE1N5cmTJ2RmZvLXX39x+PBh5s6dS+vWrenZsycAXl5e1KlTh3bt2rFgwQJ5MkPlkK569eoEBgbSrFkzypYtS9euXcnIyODQoUOMHz9erby6dety6NAhWrRoQbFixdT8CZw+fVo2N//YODg4ULx4cVasWMHAgQO5ceNGjm8qT506lWrVquHu7k5qaioHDhyQFYzFixdja2uLp6cnWlpa7NixAxsbG8zMzDSqr4IwNjamV69ejB07FnNzc0qWLElgYCBaWlry6tj79rn79++zbt06vv76a+zs7Lh9+zZ3796V27uosbe3Z8mSJQwdOpSkpCR69uyJo6Mjf/zxB5s2bcLIyIigoCCsrKyoWrUqZ86cUXPAB1nbJ548eaJ2TldXN1+TaE1IS0ujb9++TJ48mbi4OAIDAxk6dChaWlqUKFECCwsL1q1bh62tLfHx8blaiuSHtrY2oaGh+Pv707RpU06dOoWNjQ1jxoxh5MiRKJVK6tevT2JiImfPnsXExIRevXoxcOBAgoKCGDt2LP369ePy5cu5TladP38eXV3dfC1uPpTZs2dz6tQpatWqxezZs6levTo6OjqcPn2auXPncvHiRY3Gk4oZM2ZgYWGBtbU1AQEBWFpa5rgvaUpkZCTHjx/H29ubkiVLEhkZyd9///3RfDbUqlWLcePGMXr0aB49eoSfnx92dnbExMSwZs0a6tevz/Dhwz+4HC8vL1xcXOjVqxcLFy4kKSkpx1aRvJg+fTrDhg3D1NQUX19fUlNTuXTpEgkJCYwaNeqDZcsX6TOQmJgoAVJiYuLnKF4gEAgEgn88mRmZUmZGphR77KoUMXGjdHDoKili4kYp9thVOayoKczzu1evXlLbtm1zDVu8eLFka2sr6evrSz4+PtKmTZskQEpISJAkSZJCQkIkU1PTHOnS0tKkqVOnSo6OjpKOjo5ka2sr+fn5Sb/99tsHXFXBXLx4UWrfvr1kZWUl6erqSuXKlZMGDBgg3b17V45z6NAhqVSpUlJm5v/Ve69evSRAAqRixYpJVlZWkpeXl7Rhwwa1eJIkSUlJSdK3334r2dnZSTo6OpK9vb3UvXt3KT4+Xo6za9cuqUqVKlLx4sUlS0tLqX379nJY6dKlpSVLlsj/f/75Z8nQ0FBavny5JEmS9ObNG8nU1FT69ddfi6xeAgMDpcqVK+cZvnXrVsnR0VHS1dWV6tSpI+3bt08CpKtXr0qSJEkzZ86U3NzcJH19fcnc3Fxq27atdO/ePUmSJGndunVSlSpVJENDQ8nExERq1qyZdOXKFTnvguorN9mWLFkilS5dWi2Pbt26SQYGBpKNjY20ePFiqWbNmtKECRPkOAX1udz66pMnT6R27dpJtra2UvHixaXSpUtLU6dOzdHmeXH//n3pfV7Rjx07Jvn4+EglSpSQ9PT0pPLly0tjxoyR/vzzTznOqlWrpNq1a6ula9SokdxPsx8+Pj5q8qjaTZIk6eTJk2pjVpJy1oXqHjB16lTJwsJCMjIykvr37y+9fftWTWY3NzdJV1dX8vDwkE6dOiUB0p49e/IsO7ey0tPTpfbt20tubm7SX3/9JSmVSmnp0qWSq6urpKOjI1lZWUk+Pj7Szz//LKfZv3+/VK5cOUlXV1dq0KCBtGHDhhzXNGDAAOm///2vhi3wf9cdGBhYqDQvX76UJkyYIDk7O0vFixeXrK2tJS8vL2nPnj2SUqmUJKng8aRqk/3790vu7u5S8eLFpZo1a0pRUVF51ltB4yQ6Olry8fGR730uLi7SihUrNL6ukydPqo05Tfnxxx+lhg0bSsbGxpKhoaHk4eEhzZgxQ24bTcZ3o0aNpOHDh8v/371H3r59W6pfv75UvHhxycXFRTp8+LBGfU+SJCk0NFS+F5coUUJq2LChtHv37jzTJSQkSIB08uTJQtdFdhSS9JH8+edDUlISpqamJCYmCq/yAoFAIBB8AMqMTLSKaef5vygRz++8kSSJWrVqMXLkSPz9/T+3ODlYvXo1e/bs4ejRo59blC+W169fU6pUKYKCgujbt+/nFuej8ObNG1xdXfnxxx8/6iryv4Fnz57h6urKpUuXPokZtEBQEGKPu0AgEAgE/2DeVdI/ltIuyB+FQsG6devIyMj43KLkio6ODitWrPjcYnxRXL16lW3bthEbG8uVK1fkT5+1bdv2M0v28dDX12fTpk08e/bsc4vyxRMXF8eqVauE0i74YhAr7gKBQCAQCDRCPL8F/yauXr1Kv379uH37NsWLF6datWosXrz4o36nWiAQCN4X4ZxOIBAIBAKBQPA/h6enJ5cvX/7cYggEAoFGCFN5gUAgEAgEAoFAIBAIvmCE4i4QCAQCwT8IZUZmvv8FAoFAIBD8+xCm8gKBQCAQ/ANQZioBePhzFPEnokh7lUJxYwMcmlbGvnEVIOsb7wKBQCAQCP59iCe8QCAQCARfOJJS4vGFW+xtO5WzU37g4clr/HXpDg9PXuPslB/Y23Yqjy/cQlJ+cn+zgmxMmTKFAQMGfJayHR0dWbp0aZ7hXbt2JSgo6NMJlI2CZIuLi0OhUHDt2jUATp06hUKh4OXLl3mm2bhxI2ZmZkUq55dIWloa5cqV49y5cx+9rILa6UPo3bs37dq1K9I8p02bRpUqVeT/EyZM4Ntvvy3SMj4m78qfGx+j3gT/XITiLhAIBALBF4wyU8mfkTf5Zdx63r54lWucty9e8cu49fwZeVNemf/c9O7dG4VCkeOIiYn53KIVmqtXr9KpUyesra3R09PD2dmZ/v37c+fOHTnOkydPWLZsGQEBAfK57HWgo6ODtbU1zZs3Z8OGDSiVn7adJk+ezOzZs0lMTCwwblxcHL1799Yo36SkJAICAihfvjx6enrY2Njg5eXF7t270fTDRfb29jx+/JiKFStqFP/fgCRJrFu3jlq1amFkZISZmRnVq1dn6dKlpKSkyPHWrFmDk5MTdevWlc/lNq4UCgVhYWGf41K+GMaMGcMPP/zAvXv3PnpZMTEx9OnTh6+++gpdXV2cnJzw9/fn0qVLRVrOsmXL2LhxY5HmWZRs3LhR7n9aWlp89dVX9OnTh6dPn35u0f6VCMVdIBAIBIIvnMjZW5EKUMilTCWRc7Z9Iok0w9fXl8ePH6sd7/NN5MzMzE+u6Ko4cOAAtWvXJjU1ldDQUG7evMmWLVswNTVlypQpcrzg4GDq1q1L6dKl1dKr6iAuLo7w8HCaNGnC8OHDad269Sf95nvFihUpW7YsW7ZsyTNOaGgosbGx8n9Jkvjuu+9ISEjINf7Lly+pW7cumzZtYuLEiVy5coVffvmFLl26MG7cOI0mCQC0tbWxsbGhWLGi28GZlpZWZHkVxMaNG2ncuHGh0vTo0YMRI0bQtm1bTp48ybVr15gyZQo//fQTR48eBbLqf+XKlfTt2zdH+pCQkBxj60tcmf2UY9fS0hIfHx9Wr15dqHSOjo6cOnVK4/iXLl2iWrVq3Llzh7Vr1xIdHc2ePXsoX748o0ePLqTU+WNqavpJLUumTZum8aSdChMTEx4/fswff/zB+vXrCQ8Pp0ePHh9HwH8AkiR9tHu7UNwFAoFAIPhCUWZk8vDUtTxX2t/l7fMkHp6K+mIc1unq6mJjY6N2aGtry9/KNjQ0xN7ensGDB5OcnCynU5lB79u3jwoVKqCrq0t8fDypqamMGTOGUqVKYWhoSK1atQr1wl1YUlJS6NOnDy1btmTfvn14eXnh5ORErVq1WLRoEWvXrpXjhoWF0aZNmzzroFSpUlStWpVJkybx008/ER4erraS9vLlS/r164eVlRUmJiY0bdqUqKgotbz2799PjRo10NPTw9LSEj8/vzxlDw4OxszMjOPHj8vn2rRpk++qrJOTE7169WLNmjX88ccf+Pr68ujRI3R1dXONP2nSJOLi4oiMjKRXr15UqFABFxcX+vfvz7Vr1zAyMlKry//85z8YGxvj4ODAunXr5LB3TeVzY+PGjTg4OGBgYICfnx/Pnz9XC1eZHQcHB+Pk5ISenh5QcL2q0m3evBlHR0dMTU3p2rUrr15pNubeh+3btxMaGsq2bduYNGkSNWrUwNHRkbZt23LixAmaNGkCwOXLl4mNjaVVq1Y58jAzM8sxtlTXrBo/Bw4cwNXVFQMDAzp27EhKSgo//PADjo6OlChRgmHDhpGZqX6vePXqFf7+/hgaGlKqVCm+++47tfD3HbvvcvHiRaysrJg/fz6gWf+fN28e1tbWGBsb07dvX96+fZsj34L6+IciSRK9e/fG2dmZ06dP06pVK8qWLUuVKlUIDAzkp59+kuOOHz8eFxcXDAwMKFOmDFOmTCE9PT1HnmvXrsXe3h4DAwM6d+6sNuH1rql848aNGTZsGOPGjcPc3BwbGxumTZumJt+0adNwcHBAV1cXOzs7hg0b9lHqQoVCocDGxgY7OztatGjBsGHDiIiI4M2bNxw+fJj69etjZmaGhYUFrVu3VpscTEtLY+jQodja2qKnp0fp0qWZO3euRtdS0PNA1RePHDmCm5sbRkZG8kSqioyMDIYNGybLN378eHr16qVW50qlkrlz5+Lk5IS+vj6VK1dm586dcrhqa094eDjVqlVDV1eXM2fOEBUVRZMmTTA2NsbExIRq1ap9sEXGZ3VOl56enmsHFggEAoFAADo6OsSfiCo4YjYenrxG6WaeH+X5qsozKSlJ7byurm6eyl1uaGlpsXz5cpycnLh37x6DBw9m3LhxrFq1So6TkpLC/PnzCQ4OxsLCgpIlSzJ06FCio6MJCwvDzs6OPXv24Ovry/Xr13F2dn6va1IqlWhp5b6OceTIEZ49e8a4ceNyDVethL148YLo6GiqV6+uUZlNmzalcuXK7N69m379+gHQqVMn9PX1CQ8Px9TUlLVr19KsWTPu3LmDubk5Bw8exM/Pj4CAADZt2kRaWhqHDh3KNf8FCxawYMECjh49Ss2aNeXzNWvWZPbs2aSmpubaXnXr1uXkyZN4eXlx9uxZ9u/fT4sWLXItQ6lUEhYWRvfu3bGzs8sRnl1pBwgKCmLmzJlMmjSJnTt3MmjQIBo1aoSrq2uB9RUZGUnfvn2ZO3cu7dq14/DhwwQGBuaIFxMTw65du9i9ezfa2tpAwfUKEBsby969ezlw4AAJCQl07tyZefPmMXv27AJlex9CQ0NxdXWlbdu2OcIUCgWmpqYAnD59GhcXF4yNjQtdRkpKCsuXLycsLIxXr17Rvn17/Pz8MDMz49ChQ9y7d48OHTpQr149unTpIqdbuHAhkyZNYvr06Rw5coThw4fj4uJC8+bNgfcfu9k5ceIE7du3Z8GCBbJPiILaafv27UybNo3vvvuO+vXrs3nzZpYvX06ZMmXU8q5ZsyZ//PEHcXFxODo6FrreCuLatWv8/vvvbN26Ndf7RvbVcWNjYzZu3IidnR3Xr1+nf//+GBsbq91PYmJi2L59O/v37ycpKYm+ffsyePBgQkND85Thhx9+YNSoUURGRvLrr7/Su3dv6tWrR/Pmzdm1axdLliwhLCwMd3d3njx5kmMC5GOjr6+PUqkkIyOD169fM2rUKDw8PEhOTmbq1Kn4+flx7do1uS/t27eP7du34+DgwMOHD3n48CFAgdeiyfMgJSWFRYsWsXnzZrS0tPjmm28YM2aMXL/z588nNDSUkJAQ3NzcWLZsGXv37pUnzwDmzp3Lli1bWLNmDc7Ozvzyyy988803WFlZ0ahRIznehAkTWLRoEWXKlKFEiRI0bNgQT09PVq9ejba2NteuXUNHR+eD6vazKu5Hjx7FwMDgc4ogEAgEAsEXib6+Pt7e3qS9Sik4cjbSXr0B4OTJk7x586ZIZVLtvbW3t1c7HxgYqLbqo+LAgQNqClyLFi3YsWMHI0aMkM85Ojoya9YsBg4cqPbyn56ezqpVq6hcuTIA8fHxhISEEB8fLyuKY8aM4fDhw4SEhDBnzpxcZY6NjWXKlClERERQokQJ/Pz86NGjB+7u7ly/fp1Jkyaxf//+XNPevXsXgPLly+dbL/Hx8UiSlKsCmxfly5fnt99+A+DMmTNcuHCBp0+fygr1okWL2Lt3Lzt37mTAgAHMnj2brl27Mn36dDkPVd1kZ/z48WzevJmff/4Zd3d3tTA7OzvS0tJ48uRJDpN+yFKQx44dS926ddHR0WHp0qX8+uuvTJo0SV7NVfHs2TMSEhIKrBsVLVu2ZPDgwbKMS5Ys4eTJkxop7suWLcPX11dWeFxcXDh37hyHDx9Wi5eWlsamTZuwsrICNKtXyJqE2Lhxo6wg9+jRg+PHj380xf3u3bsaXfeDBw/y7FP+/v7y5ISK6OhoHBwcgKzxs3r1asqWLQtAx44d2bx5M3/99RdGRkZUqFCBJk2acPLkSTXFvV69ekyYMAHIquezZ8+yZMkSWXF/n7GbnT179tCzZ0+Cg4PlcjVpp6VLl9K3b19528CsWbOIiIjIsequqq8HDx58FMVd03sCZPmVUOHo6MiYMWMICwtTU9zfvn3Lpk2bKFWqFAArVqygVatWBAUFYWNjk2u+Hh4e8sSVs7MzK1eu5Pjx4zRv3pz4+HjZz4SOjg4ODg5qk3cfm7t377JmzRqqV6+OsbExHTp0UAvfsGEDVlZWREdHU7FiReLj43F2dqZ+/fooFAq1+1J+16Lp8yA9PZ01a9bI42Do0KHMmDFDLmPFihVMnDhRtl5auXKl2oRoamoqc+bMISIigjp16gBQpkwZzpw5w9q1a9UU9xkzZsjjRCXj2LFj5b7yvpPL2fmsiru3tzcmJiafUwSBQCAQCL5oihsXboK7uLE+gNqKQVGhWml/+PCh2vM7r9X2Jk2aqO03NTQ0BCAiIoK5c+dy69YtkpKSyMjI4O3bt6SkpMgT+sWLF8fDw0NOe/36dTIzM3FxcVErIzU1FQsLizxlHjlyJPXq1WPixIncu3ePbdu2UaNGDdLT07G0tFRThN9FU+dqqgmSd5Xb/JAkCYVCAUBUVBTJyck5ruPNmzeyWem1a9fo379/vnkGBQXx+vVrLl26lGMlErImgwA152fZuXv3LiEhIWhrazNt2jRCQkJYtWoVKSkpOa5N07pRkb0tVaa1mjqwunnzZo5tAXXq1MmhuJcuXVpW2kGzeoUspSr7qratrW2+ssXHx1OhQgX5f0ZGBunp6WqTVJMmTWLSpEm5pi9Mv8qrTy1ZsgQvLy+1c9mVfAMDA1lZAbC2tsbR0VFNRmtr6xzXqVJOsv/P7mn+fcauisjISA4cOMDOnTvVTJE1aaebN28ycODAHLKdPHlS7VxBfRxg4MCBar4eUlJSaNGihdpESHbz/+wUpt//+OOPLF++nNjYWJKTk8nIyMih9zg4OMhKu+qalEolt2/fzldxz072/tqpUyeWLl1KmTJl8PX1pWXLlrRp0yZP/xGnT59Ws6pJS0tDkiQ1U/C1a9fSvXv3PK8zMTERIyMjlEolb9++pX79+gQHBwNZ95SpU6cSGRnJs2fPZH8H8fHxVKxYkd69e9O8eXNcXV3x9fWldevWeHt7F3gtmj4P3h0H2esqMTGRv/76S21iQ1tbm2rVqslyxsTEkJKSoqaQq+rJ09NT7dy7FlejRo2iX79+bN68GS8vLzp16qQmy/vwWRV3HR2dDzYZEAgEAoHg34oyIxOHppV5ePKaxmnsm1RBmZH5UZ6vqjxNTEw0mng3NDSkXLlyaufi4uJo3bo1gwYNYvbs2Zibm3PmzBn69u1LWlqa/PKvr68vK7aQ9SKtra3N5cuXc6w0vmuWnZ1NmzbJ5quVKlWibdu2pKamkpCQkOeLsQrVS+GtW7dyKDTZsbS0BCAhIUFNccyPmzdvyo76kpOTsbW1zXW/vkp2lUKSHw0aNODgwYNs375dXjXNzosXLwDylPGbb74BstoIshTsIUOG5BrXysoKMzMzbt26VaBcQI7+qFAoitxpmWpiSIUm9fo+stnZ2antx9+9eze7du1SM29WmeHnhouLi0b1ZmlpyfXr13MNs7GxyTG2spPbNX1oG7zv2FVRtmxZLCws2LBhA61atZLl0bSdNKGgPg5ZK6NjxoyR/zdu3Jj58+dTq1atAvPPfk94V3HLzq+//kr37t2ZPn06Pj4+mJqaEhYWViSfZMyvHe3t7bl9+zYREREcO3aMwYMHs3DhQn7++edcnwnVq1dX68vLly/n0aNHsu8ByJrgyQ9jY2OuXLmClpYWtra2aveqNm3aULp0adavX4+dnR1KpZKKFSvKziOrVq3K/fv3CQ8PJyIigs6dO+Pl5cXOnTvzvRZNnwe51VVhJl9UEzgHDx5Um2CBnBPW795/pk2bRrdu3Th48CDh4eEEBgYSFhaWr2+SgvisirtAIBAIBIK80SqmjX3jKuiZ79bIQZ2ehQn2jSujpf3l+p69fPkySqWSoKAgeY/o9u3bC0zn6elJZmYmT58+pUGDBhqXl9uLv8phXEF4e3tjaWnJggUL2LNnT47wly9fYmZmRtmyZTExMSE6OjrHClBunDhxguvXrzNy5Egg6+X1yZMnFCtWLE/zXg8PD44fP06fPn3yzLdmzZoMHToUX19fihUrpqacANy4cYOvvvpKnmjIC0dHxwI/QaWlpUXXrl3ZvHkzgYGBOUy6k5OT0dPTKxJP8W5ubkRGRqqdO3/+fIHpNKnX96FYsWJqSnPJkiXR19fPV5HOTrdu3ejatSs//fRTjn3ukiSRlJSEqampvD82u3XGx+bdej1//jxubm7A+49dFZaWluzevZvGjRvTuXNntm/fjo6OjkbtpOoDPXv2zFNWyOrjOjo6ObaJZKdkyZJq++6LFStGqVKlNGq/KlWqUKFCBYKCgujSpUuOfe6qe8K5c+coXbq02uchHzx4kCO/+Ph4/vzzT3n8nD9/Hi0tLY22UuSFvr4+bdq0oU2bNgwZMoTy5ctz/fp1qlatmmvc7Ndtbm5OUlKSxn0Zsu4FucV//vw5t2/fZv369fI9+8yZMznimZiY0KVLF7p06ULHjh3x9fXlxYsXmJub53kt7/s8yI6pqSnW1tZcvHiRhg0bAllfQbhy5QpVqlQBUHOwmN0sXlNcXFxwcXFh5MiR+Pv7ExIS8kGK+5f7ZBcIBAKBQABArYBuKApQxhXaWtSe5P+JJHp/ypUrR3p6OitWrODevXts3ryZNWvWFJjOxcWF7t2707NnT3bv3s39+/e5cOECc+fO5eDBgx9FVkNDQ4KDgzl48CBff/01ERERxMXFcenSJcaNGyeb7mppaeHl5ZXrS2lqaipPnjzh0aNHXLlyhTlz5tC2bVtat24tKyFeXl7UqVOHdu3acfToUeLi4jh37hwBAQGyF+LAwEC2bdtGYGAgN2/e5Pr162qrYirq1q3LoUOHmD59upqJM2SZxarMUIuC2bNnY29vT61atdi0aRPR0dHcvXuXDRs24Onpmae5cWEZNmwYhw8fZtGiRdy9e5eVK1fmMJPPDU3q9XPQuXNnunTpgr+/P3PmzOHSpUs8ePCAAwcO4OXlJZt/N2nShOTkZH7//fccebx8+ZInT56oHa9fv/5g2c6ePcuCBQu4c+cO3333HTt27GD48OHA+4/d7JQsWZITJ05w69Yt/P39ycjI0Kidhg8fzoYNGwgJCeHOnTsEBgbmWi+nT5+mQYMGGlmovA8KhUKWoUGDBrKjv99++43Zs2fLEzHOzs7Ex8cTFhZGbGwsy5cvz3XyT09Pj169ehEVFcXp06cZNmwYnTt31mhiMTc2btzI999/z40bN7h37x5btmxBX18/V58WH5sSJUpgYWHBunXriImJ4cSJE4waNUotzuLFi9m2bRu3bt3izp077NixAxsbG8zMzPK9lqJ6Hnz77bfMnTuXn376idu3bzN8+HASEhLkiTJjY2PGjBnDyJEj+eGHH4iNjeXKlSusWLGCH374Ic9837x5w9ChQzl16hQPHjzg7NmzXLx4UZ4Ee1+E4i4QCAQCwReMlrYWdrXcaLigP3oWuZun61mY0HBBf2xruX3Rq+2Q5VBt8eLFzJ8/n4oVKxIaGip//qcgQkJC6NmzJ6NHj8bV1ZV27dpx8eJF2SHXx6Bt27acO3cOHR0dunXrRvny5fH39ycxMZFZs2bJ8fr160dYWFgO0+PDhw9ja2uLo6Mjvr6+nDx5kuXLl/PTTz/JJp4KhYJDhw7RsGFD+vTpg4uLC127duXBgweymWrjxo3ZsWMH+/bto0qVKjRt2pQLFy7kKnP9+vU5ePAgkydPZsWKFUCWE6y9e/cWuE++MJibm3P+/Hm++eYbZs2ahaenJw0aNGDbtm0sXLhQ9o7+odSuXZv169ezbNkyKleuzNGjR9Ucf+WFJvX6OVAoFGzdupXFixezd+9eGjVqhIeHB9OmTaNt27b4+PgAYGFhgZ+fX64exvv06YOtra3aoWrrD2H06NFcunQJT09PZs2axeLFi2V5PmTsZsfGxka2OunevTtKpbLAdurSpQtTpkxh3LhxVKtWjQcPHjBo0KAceYeFhRVpH8+NmjVrcunSJcqVK0f//v1xc3Pj66+/5vfff5cny77++mtGjhzJ0KFDqVKlCufOnWPKlCk58ipXrhzt27enZcuWeHt74+Hhoebor7CYmZmxfv166tWrh4eHBxEREezfvz9fPyAfCy0tLcLCwrh8+TIVK1Zk5MiRLFy4UC2OsbExCxYsoHr16tSoUYO4uDgOHTqElpZWgddSFM+D8ePH4+/vT8+ePalTpw5GRkb4+Pio+ZaYOXMmU6ZMYe7cubi5ueHr68vBgwflrU65oa2tzfPnz+nZsycuLi507tyZFi1a5OtTRRMUUmG9ixQBKhOgxMRE4ZxOIBAIBAINUGZmKYQPT0Xx8OQ10l69obixPvZNqmDfOMt788dW2sXzO28kSaJWrVqySeSXxurVq9mzZw9Hjx793KIICsFvv/1G8+bNiY2NzdeXgwDCw8MZPXo0v/32W5Fs0RD876FUKnFzc6Nz587MnDnzc4uTA9GrBQKBQCD4B6BSyu0beVC62f85RVJmZH7xq+z/CygUCtatW5enM7HPjY6OTpGsyAo+LR4eHsyfP5/79+9TqVKlzy3OF83r168JCQkRSrtAYx48eMDRo0dp1KgRqamprFy5kvv379OtW7fPLVquiBV3gUAgEAgEGiGe3wKBQCD4t/Dw4UO6du3KjRs3kCSJihUrMm/ePNlZ3ZeGmJISCAQCgUAgEAgEAsH/FPb29pw9e/Zzi6ExwrZOIBAIBAKBQCAQCASCLxihuAsEgv8JlBmZ+f4XCAQCgUAgEAi+VISpvEAg+FcjZSqRgMe/RPHnyaukv3qDjrE+dk08sW1UBQUU+H1sgUAgEAgEAoHgcyIUd4FA8K9FUko8vXiTa3NDSX3xSi3s8alr6JobU2Vid0rWrIBCS/GZpBQIBAKBQCAQCPJHLDMJBIJ/JVKmkqcXorkwYV0OpV1F6otXXJiwjqcXopH+/zeyBQKB4GPQu3dv2rVrl2+cxo0bM2LEiE8ij+DfyfHjx3FzcyMz8+NuBzt16hQKhYKXL19+lPwVCgV79+4t0jwdHR1ZunQpAGlpaTg6OnLp0qUiLeNDadiwIVu3bv3k5cbFxaFQKLh27Vqu4V9afU2bNo0qVarkG+fd+2n29s+Lj9HvihKhuAsEgn8lEnBtbmiBCrmUqeTavK188u9iCgT/cnr37o1CochxxMTEfG7RCs3Vq1fp1KkT1tbW6Onp4ezsTP/+/blz506RlrN7925mzpxZpHnmRuPGjQuMo3qRVx0WFhZ4e3tz9erVjy6fICdPnjzh22+/pUyZMujq6mJvb0+bNm04fvy4Wrxx48YxefJktLW1Adi4cWOu41BPT+9zXMYXQ/HixRkzZgzjx4//JOXt2rWLxo0bY2pqipGRER4eHsyYMYMXL17Icfbt28dff/1F165d5XOOjo5ym+nr6+Po6Ejnzp05ceLEJ5FbxZdYXwXxqe6nnxKhuAsEgn8dyoxMHv98Lc+V9ndJfZ7E45+vCYd1AkER4+vry+PHj9UOJyenQueTmZmJUvl5rGIOHDhA7dq1SU1NJTQ0lJs3b7JlyxZMTU2ZMmVKkZZlbm6OsbFxkeap4s6dO4SFhamdu3LlCgcOHMg3XUREBI8fP+bIkSMkJyfTokWLj7bK+k8gPT39g/No3LgxGzdu1Dh+XFwc1apV48SJEyxcuJDr169z+PBhmjRpwpAhQ+R4Z86cITY2lg4dOqilNzExyTEOHzx48MHX8TFIS0v7ZGV1796dM2fO8Pvvv2uc5tSpUzg6OhaqnICAALp06UKNGjUIDw/nxo0bBAUFERUVxebNm+V4y5cvp0+fPmhpqatnM2bM4PHjx9y+fZtNmzZhZmaGl5cXs2fPLpQcH8qXVl8F8THup0Ux/j+Ez6q4p6eni0Mc4hBHkR9axbT582ThVoUen7qGVjHtzy67OMTxpR8ASUlJakdqamqu40pXVxcbGxu1Q1tbm8WLF1OpUiUMDQ2xt7dn8ODBJCcny+k2btyImZkZ+/bto0KFCujq6hIfH09qaipjxoyhVKlSGBoaUqtWLU6dOvXe7yEFkZKSQp8+fWjZsiX79u3Dy8sLJycnatWqxaJFi1i7di2QNbHQt29fnJyc0NfXx9XVlWXLluWa5/Tp07GyssLExISBAweqKSq5mXbOmTOH//znPxgbG+Pg4MC6devk8LS0NIYOHYqtrS16enqULl2auXPn5lqupaUlJ0+epHPnzrx8+ZKpU6cyceJEypQpk28dWFhYYGNjQ/Xq1Vm0aBF//fUXkZGRxMbG0rZtW6ytrTEyMqJGjRpERESopV21ahXOzs7o6elhbW1Nx44d5bCdO3dSqVIl9PX1sbCwwMvLi9evX8vhwcHBuLm5oaenR/ny5Vm1apUcprIG2L17N02aNMHAwIDKlSvz66+/qpW/fv167O3tMTAwwM/Pj8WLF2NmZqYW56effqJq1aro6elRpkwZpk+fTkZGhhyuUChYvXo1X3/9NYaGhsyePZuEhAS6d++OlZUV+vr6ODs7ExISkm89fgiDBw9GoVBw4cIFOnTogIuLC+7u7owaNYrz58/L8cLCwmjevHmO1XSFQpFjHFpbW8vhjRs35ttvv2XEiBGUKFECa2tr1q9fz+vXr+nTpw/GxsaUK1eO8PDwHLKdPXsWDw8P9PT0qF27Njdu3JDDnj9/jr+/P6VKlcLAwIBKlSqxbds2tfSNGzdm6NChjBgxAktLS3x8fHKtg8DAQGxtbfntt9+ArEmKBg0aoK+vj729PcOGDVPrP0+fPqVNmzbo6+vj5OREaGhojjxLlChBvXr1ckxoFSUXLlxgzpw5BAUFsXDhQurWrYujoyPNmzdn165d9OrVC4C///6bEydO0KZNmxx5GBsbY2Njg4ODAw0bNmTdunVMmTKFqVOncvv2bTnejRs3aNGiBUZGRlhbW9OjRw+ePXsmhyuVShYsWEC5cuXQ1dXFwcEhT+U/MzOT//znP5QvX574+Hjgy6ovFZs3b8bR0RFTU1O6du3Kq1f/t2BT0Naju3fv0rBhQ/T09KhQoQLHjh1TC1fdZ3788UcaNWqEnp6e3I+K4v70PnxW53RHjx7FwMDgc4ogEAj+Zejr6+Pt7U36qzeFSpf+KgWAkydP8uZN4dIKBP8rpKRkjRN7e3u184GBgUybNk3jfLS0tFi+fDlOTk7cu3ePwYMHM27cOLWXn5SUFObPn09wcDAWFhaULFmSoUOHEh0dTVhYGHZ2duzZswdfX1+uX7+Os7Pze12TUqnMscKl4siRIzx79oxx48blGq5SApVKJV999RU7duzAwsKCc+fOMWDAAGxtbencubMc//jx4+jp6XHq1Cni4uLo06cPFhYW+a6cBQUFMXPmTCZNmsTOnTsZNGgQjRo1wtXVleXLl7Nv3z62b9+Og4MDDx8+5OHDh7nmY25uztq1a1m3bh07duzA3d2dI0eOaFhLWejr6wNZEwbJycm0bNmS2bNno6ury6ZNm2jTpg23b9/GwcGBS5cuMWzYMDZv3kzdunV58eIFp0+fBuDx48f4+/uzYMEC/Pz8ePXqFadPn0aSsjYthYaGMnXqVFauXImnpydXr16lf//+GBoaqr24BwQEsGjRIpydnQkICMDf35+YmBiKFSvG2bNnGThwIPPnz+frr78mIiIih4XE6dOn6dmzJ8uXL6dBgwbExsYyYMAAIKtPq5g2bRrz5s1j6dKlFCtWjClTphAdHU14eDiWlpbExMR8tOfGixcvOHz4MLNnz8bQ0DBHePaJiNOnT9OtW7f3KueHH35g3LhxXLhwgR9//JFBgwaxZ88e/Pz8mDRpEkuWLKFHjx7Ex8ervbuPHTuWZcuWYWNjw6RJk2jTpg137txBR0eHt2/fUq1aNcaPH4+JiQkHDx6kR48elC1blpo1a6qVPWjQIM6ePZtDLkmSGDZsGAcOHOD06dOUK1eO2NhYfH19mTVrFhs2bODvv/9m6NChDB06VJ5A6d27N3/++ScnT55ER0eHYcOG8fTp0xz516xZU+6XH4PQ0FCMjIwYPHhwruGq9jtz5gwGBga4ublplO/w4cOZOXMmP/30E+PGjePly5c0bdqUfv36sWTJEt68ecP48ePVzOonTpzI+vXrWbJkCfXr1+fx48fcunUrR96pqan4+/sTFxfH6dOnsbKyksO+lPoCiI2NZe/evRw4cICEhAQ6d+7MvHnzNLJEUCqVtG/fHmtrayIjI0lMTMxTyZ8wYQJBQUF4enrKyvuH3p/eG+kzkJiYKAHSs2fPpLS0NHGIQxziKNJDkiTpwuRg6af6QzU+Lk75XpIk6bPLLg5xfMnHs2fPJEB6+PChlJiYKB9v377N8azv1auXpK2tLRkaGspHx44dc30v2LFjh2RhYSH/DwkJkQDp2rVr8rkHDx5I2tra0qNHj9TSNmvWTJo4cWKe7xwxMTGSv7+/ZGVlJbm4uEjjx4+Xbty4IUmSJP32229S69at80w7f/58CZBevHiRZ5y8GDJkiNShQwf5f69evSRzc3Pp9evX8rnVq1dLRkZGUmZmpiRJktSoUSNp+PDhcnjp0qWlb775Rv6vVCqlkiVLSqtXr5YkSZK+/fZbqWnTppJSqSxQnhcvXkiDBg2SOnXqJFWuXFmaMmWK5OvrK926dSvX+Pfv35cA6erVq5IkSVJCQoLk5+cnGRkZSU+ePMk1jbu7u7RixQpJkiRp165dkomJiZSUlJQj3uXLlyVAiouLyzWfsmXLSlu3blU7N3PmTKlOnTpqsgUHB8vhv//+uwRIN2/elCRJkrp06SK1atVKLY/u3btLpqam8v9mzZpJc+bMUYuzefNmydbWVv4PSCNGjFCL06ZNG6lPnz65yq4JjRo1kkJCQjSKGxkZKQHS7t27C4xramoqbdq0Se2caixlH4eGhoaSr6+vmjz169eX/2dkZEiGhoZSjx495HOPHz+WAOnXX3+VJEmSTp48KQFSWFiYHOf58+eSvr6+9OOPP+YpY6tWraTRo0erle3p6ZkjHiDt2LFD6tatm+Tm5ib98ccfcljfvn2lAQMGqMU/ffq0pKWlJb1580a6ffu2BEgXLlyQw2/evCkB0pIlS9TSLVu2THJ0dMxT3nc5efKkVLp0aY3jt2jRQvLw8Cgw3pIlS6QyZcrkOF+6dOkcMquwtraWBg0aJElS1vjw9vZWC3/48KEESLdv35aSkpIkXV1daf369bnmpRpTp0+flpo1aybVr19fevnyZY54X0p9BQYGSgYGBmr3l7Fjx0q1atWS/+d2P1XV5ZEjR6RixYqpPU/Cw8MlQNqzZ48kSf9XJ0uXLlUruyjuT+/LZ11x19HRQUdH53OKIBAI/oUoMzKxa+LJ41PXNE5j27gKyoxMcU8SCPJBNT5MTEwwMTEpMH6TJk1YvXq1/F+1YhgREcHcuXO5desWSUlJZGRk8PbtW1JSUuTVvOLFi+Ph4SGnvX79OpmZmbi4uKiVkZqaioWFRZ4yjBw5knr16jFx4kTu3bvHtm3bqFGjBunp6VhaWjJ9+vQ800qS5m4rv/vuOzZs2EB8fDxv3rwhLS0th9fjypUrq61W1qlTh+TkZB4+fEjp0qVzzTd7HahMnlUrh71796Z58+a4urri6+tL69at8fb2zjWfp0+f0qBBA/z9/WncuDEzZszgypUr3LlzB1dX1zyvq27dumhpafH69WvKlCnDjz/+iLW1NcnJyUybNo2DBw/y+PFjMjIyePPmjWxW27x5c0qXLk2ZMmXw9fXF19cXPz8/2Wy0WbNmVKpUCR8fH7y9venYsSMlSpTg9evXxMbG0rdvX/r37y/LkZGRgampaZ51Y2trK19n+fLluX37Nn5+fmrxa9asqbanPyoqirNnz6qt0GVmZuboi9WrV1fLZ9CgQXTo0IErV67g7e1Nu3btqFu3bp51OGfOHObMmSP/f/PmDefPn2fo0KHyuejoaBwcHHKkLUwffPPmTa5O54yNjbly5YraOZX1hIrsdamtrY2FhQWVKlWSz6lM699dta5Tp47829zcHFdXV27evAlk1eWcOXPYvn07jx49Ii0tjdTU1BzWttWqVcv1ekaOHImuri7nz5/H0tJSPh8VFcVvv/2mZv4uSRJKpZL79+9z584dihUrppZv+fLlc2yTUNWDypIoL4yMjOTfmZmZpKamqp375ptvWLNmTa5pNW2/vNouPyRJQqHI+pRtVFQUJ0+eVJNLRWxsLC9fviQ1NZVmzZrlm6e/vz9fffUVJ06cyNFH4MupL8jaSpR9D7utrW2uVhW5cfPmTezt7bGzs5PPZe/L2ck+/ovq/vS+iO+4CwSCfx1axbSxbVQFXXNjjRzU6VqYYNuoClrawl+nQFCUGBoaUq5cObVzcXFxtG7dmkGDBjF79mzMzc05c+YMffv2JS0tTX6p19fXl19KAZKTk9HW1uby5cuyx2wVub2sqlA5cwKoVKkSbdu2JTU1lYSEBGxsbPKVXzVJcOvWrTxf6iBrb/GYMWMICgqiTp06GBsbs3DhQiIjI/PNXxPenUxUKBSyo76qVaty//59wsPDiYiIoHPnznh5ebFz584c+bi6uuZQ0KtWrUrVqlXzLf/HH3+kQoUKWFhYqCk+Y8aM4dixYyxatIhy5cqhr69Px44d5T37KmXx1KlTHD16lKlTpzJt2jQuXryImZkZx44d49y5cxw9epQVK1YQEBBAZGSk3P7r16+nVq1aarK82+7Z60bVVwrjxDA5OZnp06fTvn37HGHZlah3TdRbtGjBgwcPOHToEMeOHaNZs2YMGTKERYsW5VrOwIED1bZMdO/enQ4dOqiVm12ByI6zszMKhSJXk+Z3sbS0JCEhIcd5LS2tHOPwXXLrZx9avwsXLmTZsmUsXbpU9mkxYsSIHA7octsCAFmTP9u2bePIkSN0795dPp+cnMx///tfhg0bliONg4NDob728OLFCzVT8NzI/om0yMhIxo8fr+ZbI79JTBcXF86cOUN6enq+CwN5tV1ePH/+nL///lt29pmcnEybNm2YP39+jri2trbcu3dPo3xbtmzJli1b+PXXX2natGmO8C+lviD/e2NRkr1/qnyxfIr7U24IxV0gEPwrUQBVJnbnwoR1+X4STqGtRZUJ3VDkGUMgEBQlly9fRqlUEhQUJO8t3759e4HpPD09yczMlFeONSW3VTaV07yC8Pb2xtLSkgULFrBnz54c4S9fvsTMzIyzZ89St25dtX2ZsbGxOeJHRUXx5s0beSXr/PnzGBkZ5fAZUBhMTEzo0qULXbp0oWPHjvj6+vLixQvMzc3zTFMYh3729vaULVs2x/mzZ8/Su3dveVU7OTmZuLg4tTjFihXDy8sLLy8vAgMDMTMz48SJE7Rv3x6FQkG9evWoV68eU6dOpXTp0uzZs4dRo0ZhZ2fHvXv31JS1wuLq6srFixfVzr37v2rVqty+fbtApTY3rKys6NWrF7169aJBgwaMHTs2T8Xd3NxcrT309fUpWbKkRuWam5vj4+PDd999x7Bhw3Iouao+CFljJDo6utDX8iGcP39ethRISEjgzp078j7ts2fP0rZtW7755hsgS2m5c+cOFSpU0Cjvr7/+mjZt2tCtWze0tbXlz6RVrVqV6OjoPOuvfPnyZGRkcPnyZWrUqAHA7du3c/0awo0bN/D09MxXjuzl/PHHHxQrVkzjPtOtWzeWL1/OqlWrGD58eI5wVft5enry5MkTEhISKFGiRIH5Llu2DC0tLdq1awdk1cmuXbtwdHTMdQ+1s7Mz+vr6HD9+nH79+uWZ76BBg6hYsSJff/01Bw8epFGjRmrhX0p9fShubm48fPiQx48fy6vh2R095oW1tXWR3J/eF6G4CwSCfyUKbS1K1qxAzXkDuDZvK6nPk3LE0bUwocqEbpSsWQGFllDdBYJPQbly5UhPT2fFihW0adOGs2fP5mk2mR0XFxe6d+9Oz549ZUdBf//9N8ePH8fDw4NWrVoVuayGhoYEBwfTqVMnvv76a4YNG0a5cuV49uwZ27dvJz4+nrCwMJydndm0aRNHjhzBycmJzZs3c/HixRyfvktLS6Nv375MnjyZuLg4AgMDGTp0aJ7O8Qpi8eLF2Nra4unpiZaWFjt27MDGxqZIXmwLwtnZmd27d9OmTRsUCgVTpkxRW006cOAA9+7do2HDhpQoUYJDhw6hVCpxdXUlMjKS48eP4+3tTcmSJYmMjOTvv/+WFb7p06czbNgwTE1N8fX1JTU1lUuXLpGQkMCoUaM0ku/bb7+lYcOGLF68mDZt2nDixAnCw8PVrDimTp1K69atcXBwoGPHjmhpaREVFcWNGzeYNWtWnnlPnTqVatWq4e7uTmpqKgcOHNDYqdj78N1331GvXj1q1qzJjBkz8PDwICMjg2PHjrF69WrZNN3Hx4cffvghR3pJknjy5EmO8yVLlnzvvqdixowZWFhYYG1tTUBAAJaWlrIy6ezszM6dOzl37hwlSpRg8eLF/PXXXxor7gB+fn5s3ryZHj16UKxYMTp27Mj48eOpXbs2Q4cOpV+/fhgaGhIdHc2xY8dYuXKlvHXkv//9L6tXr6ZYsWKMGDEiV9Pv06dPf9RvfdeqVYtx48YxevRoHj16hJ+fH3Z2dsTExLBmzRrq16/P8OHD8fT0xNLSkrNnz9K6dWu1PF69esWTJ09IT0/n/v37bNmyheDgYObOnSsrxEOGDGH9+vX4+/szbtw4zM3NiYmJISwsjODgYPT09Bg/fjzjxo2jePHi1KtXj7///pvff/+dvn37qpX37bffkpmZSevWrQkPD6d+/fpfXH19KF5eXri4uNCrVy8WLlxIUlISAQEBGqUtivvT+yLsQgUCwb8WhZaCkjXcaL5rJtWm98GuiSdW1V2xa+JJtel9aL5rJiVruAmlXSD4hFSuXJnFixczf/58KlasSGhoaJ6fMHuXkJAQevbsyejRo3F1daVdu3ZcvHgx173BRUXbtm05d+4cOjo6dOvWjfLly+Pv709iYqKs3P33v/+lffv2dOnShVq1avH8+fNcvSI3a9YMZ2dnGjZsSJcuXfj6668L5Y3/XYyNjVmwYAHVq1enRo0axMXFcejQoQ9WxjRh8eLFlChRgrp169KmTRt8fHzUzO7NzMzYvXs3TZs2xc3NjTVr1rBt2zbc3d0xMTHhl19+oWXLlri4uDB58mSCgoJo0aIFAP369SM4OJiQkBAqVapEo0aN2LhxY46JkPyoV68ea9asYfHixVSuXJnDhw8zcuRINRN4Hx8fDhw4wNGjR6lRowa1a9dmyZIlefobUFG8eHEmTpyIh4cHDRs2RFtb+6N+IqtMmTJcuXKFJk2aMHr0aCpWrEjz5s05fvy4mg+J7t278/vvv6t9IgyyPt9oa2ub49B0P3B+zJs3j+HDh1OtWjWePHnC/v37KV68OACTJ0+matWq+Pj40LhxY2xsbGSlvjB07NiRH374gR49erB79248PDz4+eefuXPnDg0aNMDT05OpU6eqbTcICQnBzs6ORo0a0b59ewYMGEDJkiXV8v31119JTExU+0zhx2D+/Pls3bqVyMhIfHx85E/5eXh4yF7ItbW16dOnT66frZs6dSq2traUK1eOHj16kJiYyPHjxxk/frwcx87OjrNnz5KZmYm3tzeVKlVixIgRmJmZyfeDKVOmMHr0aKZOnYqbmxtdunTJsw+MGDGC6dOn07JlS86dOwd8WfX1oWhpabFnzx7evHlDzZo16devn0be6KFo7k/vi0IqjBeA/8+VK1fQ0dGRnVb89NNPhISEUKFCBaZNmyYP2LxISkrC1NSUxMREjZzbCAQCwYeizMhEq5h2nv8FAkHBiOe34J9M//79uXXr1kf9nNXnZuzYsSQlJbF27drPLcoXT5cuXahcuTKTJk363KIA8OTJE9zd3bly5UqBk0efgy+tvv4Xea8p2f/+97+y44d79+7RtWtXDAwM2LFjR57fOhUIBILPybtKulDaBQKB4N/NokWLiIqKIiYmhhUrVvDDDz8U2Yrdl0pAQAClS5f+KE66/k2kpaVRqVIlRo4c+blFkbGxseH777+Xv8zwJfEl1tf/Iu+14m5qasqVK1coW7Ys8+fP58SJExw5coSzZ8/StWtXHj58mG96MWMvEAgEAsE/D/H8FvyT6Ny5M6dOneLVq1eUKVOGb7/9loEDB35usQQCgeC9eC/ndKpvJULWt1hVThTs7e159uxZ0UknEAgEAoFAIBC8B5p8rUAgEAj+KbyXqXz16tWZNWsWmzdv5ueff5Y9ud6/fx9ra+siFVAgEAgEAoFAIBAIBIL/Zd5LcV+6dClXrlxh6NChBAQEyJ8i2LlzJ3Xr1i1SAQUCgUAgEGShzMjM979AIBAIBIJ/J++1xz0v3r59i7a2Njo6OvnGE3vkBAKBQCDQHClTCUg8/eUaT09dIT05BR0jA0o2rkrJhlUABQrtj/8JMPH8FggEAoHg81CkT3k9Pb0ClXaBQCAQCASaIyklnl+M5kynAG5M/56nP18l4fJtnv58lRvTv+dMpwCeX4xGUhbZPLzgI9C7d+8Cv2HduHFjRowY8UnkKSqmTZtGlSpV8o3z7nU5OjqydOnSfNMoFAr27t37wfJ96UyZMoUBAwZ89HI0aaf35dSpUygUCl6+fFlkecbFxaFQKLh27RoA0dHRfPXVV7x+/brIyvhQnj9/TsmSJYmLi/vkZW/cuBEzM7M8w7/E+hJ8OBor7iVKlMDc3FyjQyAQCAQCwYcjZSp5fuF3fgtYQ9qLpFzjpL1I4reANTy/8Pv/X5n/MujduzcKhSLHERMT87lFKzRXr16lU6dOWFtbo6enh7OzM/3795c/jVtU7N69m5kzZxZpnrnRuHFjjePu2rWLxo0bY2pqipGRER4eHsyYMYMXL15onMenuq4vCU36zJMnT1i2bBkBAQHyubzGja+v7+e4jC+GChUqULt2bRYvXvzRy0pLS2PBggVUrlwZAwMDLC0tqVevHiEhIaSnp8vxZs+eTdu2bXF0dAT+b7JBdRgbG+Pu7s6QIUO4e/fuR5c7O5+qvjZu3Chfr5aWFl999RV9+vTh6dOnH7Xc/1U09ipf0MyoQCAQCASCokbi5oItBSrkUqaSmwu2UH/HnE8kl2b4+voSEhKids7KyqrQ+WRmZsovhp+aAwcO0KFDB3x8fAgNDaVs2bI8ffqUHTt2MGXKFH788cciK+tjLn7cuXOHK1eu0LVrV/nclStX+PPPP+WvA71LQEAA8+fPZ+TIkcyZMwc7Ozvu3r3LmjVr2Lx5M8OHD9eo7I9xXenp6Z/MylOhUHD//n1ZQSsITftMcHAwdevWpXTp0mrpcxs3urq6RXItRU12RfZj06dPH/r378/EiRMpVkwzFWbatGnExcWxceNGjeKnpaXh4+NDVFQUM2fOpF69epiYmHD+/HkWLVqEp6cnVapUISUlhe+//54jR47kyCMiIgJ3d3dSUlK4fv06y5Yto3Llyuzfv59mzZoV5pI/iE9RXwAmJibcvn0bpVJJVFQUffr04c8//8y1bv4XkCSJzMxMjeu8MGj8BOzVq5fGh0AgEAgEgg9DmZHJ05+v5bnS/i5pL5J4+svVL8phna6uLjY2NmqHtrY2ixcvplKlShgaGmJvb8/gwYNJTk6W06nMQPft20eFChXQ1dUlPj6e1NRUxowZQ6lSpTA0NKRWrVqcOnXqo8mfkpJCnz59aNmyJfv27cPLywsnJydq1arFokWLWLt2LZA1sdC3b1+cnJzQ19fH1dWVZcuW5Zrn9OnTsbKywsTEhIEDB5KWliaH5WZSPmfOHP7zn/9gbGyMg4MD69atk8PT0tIYOnQotra26OnpUbp0aebOnZtruZaWlpw8eZLOnTvz8uVLpk6dysSJEylTpkyu8S9cuMCcOXMICgpi4cKF1K1bF0dHR5o3b86uXbtyvO9t3rwZR0dHTE1N6dq1K69evcrzut7l7t27NGzYED09PSpUqMCxY8fUwlUrmT/++CONGjVCT0+P0NBQIEv5dXNzQ09Pj/Lly7Nq1aoc6Xbv3k2TJk0wMDCgcuXK/Prrr3nK8qFo2mcAwsLCaNOmTY48chs3JUqUkMMVCgVr166ldevWGBgY4Obmxq+//kpMTAyNGzfG0NCQunXrEhsbmyPvtWvXYm9vj4GBAZ07dyYxMVEOu3jxIs2bN8fS0hJTU1MaNWrElStX1NIrFApWr17N119/jaGhIbNnz861Dlq0aEG9evVk8/n82gmy+punpyd6enpUr16dq1ev5si3efPmvHjxgp9//jmP2v9wli5dyi+//MLx48cZMmQIVapUoUyZMnTr1o3IyEicnZ0BOHToELq6utSuXTtHHhYWFtjY2FCmTBnatm1LREQEtWrVom/fvmRm/t/9+aeffqJq1aro6elRpkwZpk+fTkZGhhz+8uVL/vvf/8pWGxUrVuTAgQO5yv33339TvXp1/Pz8SE1NBT5NfUFWn7CxscHOzo4WLVowbNgwIiIiePPmDYcPH6Z+/fqYmZlhYWFB69at1fplfvcwSZKYNm0aDg4O6OrqYmdnx7Bhw+S0BT0PVM+RI0eO4ObmhpGREb6+vjx+/FiOk5GRwbBhw2T5xo8fT69evdS2NSmVSubOnSvf3ytXrszOnTvlcNVWkfDwcKpVq4auri5nzpwhKiqKJk2aYGxsjImJCdWqVePSpUsfVNfvPRUQGxtLSEgIsbGxLFu2jJIlSxIeHo6DgwPu7u4a5ZGenv5JZ+oEAoFAIPinoKOjw9OfrxQcMRtPf76KdZNqH+3Zqso3KUl9MkFXV7dQK4JaWlosX74cJycn7t27x+DBgxk3bpzay3xKSgrz588nODgYCwsLSpYsydChQ4mOjiYsLAw7Ozv27NmDr68v169fl1+oC4tSqcxzJf/IkSM8e/aMcePG5Rqu2mOqVCr56quv2LFjBxYWFpw7d44BAwZga2tL586d5fjHjx9HT0+PU6dOERcXR58+fbCwsMhV+VERFBTEzJkzmTRpEjt37mTQoEE0atQIV1dXli9fzr59+9i+fTsODg48fPiQhw8f5pqPubk5a9euZd26dezYsQN3d/d8V8RCQ0MxMjJi8ODB+V47ZL0T7t27lwMHDpCQkEDnzp2ZN29evtelQqlU0r59e6ytrYmMjCQxMTFPJX/ChAkEBQXJCl5oaChTp05l5cqVeHp6cvXqVfr374+hoaHaxEJAQACLFi3C2dmZgIAA/P39iYmJ+SgrYpr2mRcvXhAdHU316tXfq5yZM2eyePFiFi9ezPjx4+nWrRtlypRh4sSJODg48J///IehQ4cSHh4up4mJiWH79u3s37+fpKQk+vbty+DBg+VJkFevXtGrVy9WrFiBJEkEBQXRsmVL7t69i7GxsZzPtGnTmDdvHkuXLqVYsWLcu3dPDnv58iWtWrXCyMiIY8eOYWBgUGA7JScn07p1a5o3b86WLVu4f/9+rtYcxYsXp0qVKpw+ffqjrVyHhobi5eWFp6dnjjAdHR3ZyuP06dNUq1ZNozy1tLQYPnw4fn5+XL58mZo1a3L69Gl69uzJ8uXLadCgAbGxsbKvg8DAQJRKJS1atODVq1ds2bKFsmXLEh0djba2do78Hz58SPPmzalduzbff/+9HOdT1Fdu6Ovro1QqycjI4PXr14waNQoPDw+Sk5OZOnUqfn5+XLt2TX4O5HUP27VrF0uWLCEsLAx3d3eePHlCVFSUXI4mz4OUlBQWLVrE5s2b0dLS4ptvvmHMmDFyn58/fz6hoaGEhITg5ubGsmXL2Lt3L02aNJHLmTt3Llu2bGHNmjU4Ozvzyy+/8M0332BlZUWjRo3keBMmTGDRokWUKVOGEiVK0LBhQzw9PVm9ejXa2tpcu3btg62E3uuO9fPPP8szab/88guzZ8+mZMmSREVF8f3336vNQuTH0aNHMTAweB8RBAKBQCD416Kvr4+3tzfpySmFSpfxKiv+yZMnefPmTZHLlZKSlb+9vb3a+cDAQKZNm5Yj/oEDBzAyMpL/t2jRgh07duRYVZ41axYDBw5UU9zT09NZtWoVlStXBiA+Pp6QkBDi4+Oxs7MDYMyYMRw+fJiQkBDmzMl9m0BsbCxTpkwhIiKCEiVK4OfnR48ePXB3d+f69etMmjSJ/fv355pWtS+1fPny+daLjo4O06dPl/87OTnx66+/sn37djXFvXjx4mzYsAEDAwPc3d2ZMWMGY8eOZebMmXlOHrRs2VJWnsePH8+SJUs4efIkrq6uxMfH4+zsTP369VEoFDlMrrOTkJBAQEAAz549o3LlypQtW5YWLVqwdOlSXF1dc732MmXKaPSiqVQq2bhxo6zc9ejRg+PHj2ukuEdERHDr1i2OHDkit+ucOXNo0aJFjrgjRoygffv28v/AwECCgoLkc05OTkRHR7N27Vo1xX3MmDG0atUKyLJ4cHd3JyYmpsB2fR807TPx8fFIkiRfc3beHTcAkyZNYtKkSfL/Pn36yH1r/Pjx1KlThylTpuDj4wPA8OHD6dOnj1oeb9++ZdOmTZQqVQqAFStW0KpVK4KCgrCxsaFp06Zq8detW4eZmRk///yz2naKbt26qeWtUtyfPHlCly5dcHZ2ZuvWrRQvXhwouJ22bt2KUqnk+++/R09PD3d3d/744w8GDRqUo27s7Ox48OBBflX7Qdy9e1cjHxAPHjzIte3yQtUf4uLiqFmzJtOnT2fChAlyPy1TpgwzZ85k3LhxBAYGEhERwYULF7h58yYuLi5ynHe5ffs2zZs3x8/Pj6VLl6JQKNTCP3Z9vYtqK0316tUxNjamQ4cOauEbNmzAysqK6OhoKlasmO89LD4+HhsbG7y8vNDR0cHBwYGaNWvKYZo8D9LT01mzZg1ly5YFspT9GTNmyGWsWLGCiRMn4ufnB8DKlSs5dOiQHJ6amsqcOXOIiIigTp06QFY7nDlzhrVr16op7jNmzKB58+Zq8o8dO1Zu+/edXM7OeynuEyZMYNasWYwaNUptBq5p06asXLlS43y8vb3F52QEAoFAIMgDHaPCTW4XM86Kn321oChRrbQ/fPhQ7fmd12p7kyZNWL16tfzf0NAQyFLW5s6dy61bt0hKSiIjI4O3b9+SkpIiT+gXL14cDw8POe3169fJzMyUX2JVpKamYmFhkafMI0eOpF69ekycOJF7yvg8uQABAABJREFU9+6xbds2atSoQXp6OpaWlmoK97sU5ou53333HRs2bCA+Pp43b96QlpaWw4u3ytmVijp16pCcnMzDhw/zVLqz14HKJFXl+Kl37940b94cV1dXfH19ad26Nd7e3rnm8/TpUxo0aIC/vz+NGzdmxowZXLlyhTt37uSquBfm2h0dHdXeB21tbTV2TnXz5k3s7e3VlCDVC/K7ZF+dfv36NbGxsfTt25f+/fvL5zMyMjA1NVVLl70ObW1tgaz6yEu5btGiBadPn1Y75+7uLitFpUuX5vfff881rab1pppY09PTyxH27riBnH4Csl+TtbU1AJUqVVI79/btW5KSkuSx6uDgICvtkFXPSqWS27dvY2Njw19//cXkyZM5deoUT58+JTMzk5SUFOLj49XKzstKoHnz5tSsWZMff/xRXvXVpJ1u3ryJh4eHWl3k1Qf09fXlCcTcOH36tNqkT1paGpIkqS0qrl27lu7du+eavjDtl1vb5YUqX1UfioqK4uzZs2qTW5mZmfJ98Nq1a3z11Vc57nfvytCgQQO6deuWpy+yj11fAImJiRgZGaFUKnn79i3169cnODgYyFLkp06dSmRkJM+ePUOpzPLXEh8fT8WKFfO9h3Xq1ImlS5dSpkwZfH19admyJW3atKFYsWIaPw8MDAxkpR3U702JiYn89ddf8mQAgLa2NtWqVZPljImJISUlRU0hV9XTu1YZ746LUaNG0a9fPzZv3oyXlxedOnVSk+V9eC/F/fr162zdujXH+ZIlS/Ls2TON88luciIQCAQCgeD/UGZkUrJxVZ7+nHOvZ16UbOSJMiPzoz1bVfmamJhoNPFuaGhIuXLl1M7FxcXRunVrBg0axOzZszE3N+fMmTP07duXtLQ0WbHV19dXWz1KTk5GW1uby5cv5zAXfXd1MjubNm2SzZMrVapE27ZtSU1NJSEhARsbm3zlV70U3rp1K09FArL2Ko8ZM4agoCDq1KmDsbExCxcuJDIyMt/8NeHdtlQoFPJLZdWqVbl//z7h4eFERETQuXNnvLy8crV8dHV1zaGgV61alapVq+ZarouLC2fOnNHICVx+MhYlqokfQPaJsH79emrVqqUW793+kV0+VZ/KT77g4GA1ixVnZ2cOHTokK7351YemfcbS0hLIsoR412FjbuPmXXK7psJe57v06tWL58+fs2zZMkqXLo2uri516tRR88Ogki83WrVqxa5du4iOjpYnEQrTTprw4sWLfJWf6tWry5+QA1i+fDmPHj1i/vz58jnVREduuLi4cOvWrQLlsLS0JCEhQTOhyZqcgCxrA8iql+nTp6tZkKjQ09NDX1+/wDx1dXXx8vLiwIEDjB07Vm1SRsXHri8AY2Njrly5gpaWFra2tmqyt2nThtKlS7N+/Xrs7OxQKpVUrFhR7lP53cPs7e25ffs2ERERHDt2jMGDB7Nw4UJ+/vlnjZ8Hud2bCjMpqeq/Bw8ezFG/705Yvzsupk2bRrdu3Th48CDh4eEEBgYSFhYmr+6/D++luJuZmfH48WO586m4evVqrp1GIBAIBAJB4dAqpk3JhlUobm6ikYO64uYmlGzoiUL703teLwyXL19GqVQSFBQkm4dv3769wHSenp5kZmbKK8eaktu3jlXOvwrC29sbS0tLFixYwJ49e3KEv3z5EjMzM86ePUvdunXV9oPn5hgsKiqKN2/eyC+258+fx8jIKMfWg8JgYmJCly5d6NKlCx07dsTX15cXL17k68ldE4d+3bp1Y/ny5axatSrX/caqa/9Q3NzcePjwIY8fP5ZXw8+fP19gOmtra+zs7Lh3716+q4HvQ27vsqVLl9bIq7ymfaZs2bKYmJgQHR2d76pqURIfH8+ff/4pWzecP38eLS0teULn7NmzrFq1ipYtWwJZljWFWZCbN28eRkZGNGvWjFOnTlGhQgWN2snNzY3Nmzfz9u1beRU7rz5w48YNOnbsmKcM+vr6apMe5ubmJCUlFTgRoqJbt25MmjSJq1ev5lhRTU9PJy0tDUNDQzw9PdmyZYtGeSqVStmnhyrPqlWrcvv27Tzl8vDw4I8//uDOnTt59g8tLS02b95Mt27daNKkCadOncphvv+x60slR27xnz9/zu3bt1m/fr18zz5z5kyOePndw/T19WnTpg1t2rRhyJAhlC9fnuvXr7/38yA7pqamWFtbc/HiRRo2bAhkWT1cuXJFtpbK7hw1u1m8pri4uODi4sLIkSPx9/cnJCTk0yvuXbt2Zfz48ezYsUOeVT179ixjxoyhZ8+e7y2MQCAQCASC7ChwG/cNvwWsyfeTcAptLdzG9fiEcr0/5cqVIz09nRUrVtCmTRvOnj3LmjVrCkzn4uJC9+7d6dmzp+yg7O+//+b48eN4eHjIe5iLEkNDQ4KDg+nUqRNff/01w4YNo1y5cjx79ozt27cTHx9PWFgYzs7ObNq0iSNHjuDk5MTmzZu5ePFijgWOtLQ0+vbty+TJk4mLiyMwMJChQ4e+92fuFi9ejK2tLZ6enmhpabFjxw5sbGyKRKGuVasW48aNY/To0Tx69Ag/Pz/s7OyIiYlhzZo11K9fX+PPweWHl5cXLi4u9OrVi4ULF5KUlKT2XfP8mD59OsOGDcPU1BRfX19SU1O5dOkSCQkJjBo16oNlex807TNaWlp4eXlx5swZNQ/WkGXu++TJE7VzxYoVk1fp3xc9PT169erFokWLSEpKYtiwYXTu3FmexHJ2dmbz5s1Ur16dpKQkxo4dq9HKb3YWLVpEZmYmTZs25dSpU5QvX77AdurWrRsBAQHyp8vi4uJYtGhRjrzj4uJ49OgRXl5eH1QP+TFixAgOHjxIs2bNmDlzJvXr18fY2JhLly4xf/58vv/+e6pUqYKPjw8TJ04kISFBzeM/ZCmsT548ISUlhRs3brB06VIuXLjAwYMH5dXhqVOn0rp1axwcHOjYsSNaWlpERUVx48YNZs2aRaNGjWjYsCEdOnRg8eLFlCtXjlu3bqFQKPD19ZXL0tbWJjQ0FH9/f7nOVe35KeorP0qUKIGFhQXr1q3D1taW+Ph4JkyYoBYnv3vYxo0byczMpFatWhgYGLBlyxb09fUpXbo0FhYWRfI8+Pbbb5k7dy7lypWjfPnyrFixgoSEBNlixdjYmDFjxjBy5EiUSiX169cnMTGRs2fPYmJikufX1N68ecPYsWPp2LEjTk5O/PHHH1y8eDHHnv/C8l5Pijlz5lC+fHns7e1JTk6mQoUKNGzYkLp16zJ58uQPEkggEAgEAkEWCm0tLGq64zF7IMXNczdNL25ugsfsgVjUrPDFr7ZD1j7vxYsXM3/+fCpWrEhoaGienzB7l5CQEHr27Mno0aNxdXWlXbt2XLx4EQcHh48mb9u2bTl37hw6Ojp069aN8uXL4+/vT2JiIrNmzQLgv//9L+3bt6dLly7UqlWL58+f5+qNvVmzZjg7O9OwYUO6dOnC119/natTP00xNjZmwYIFVK9enRo1ahAXF8ehQ4eK7Hv38+fPZ+vWrURGRuLj44O7u7vsIbqoPv+rpaXFnj17ePPmDTVr1qRfv34aObUD6NevH8HBwYSEhFCpUiUaNWrExo0bc0yYfGo06TOQJX9YWFgOc/bDhw9ja2urdtSvX/+D5SpXrhzt27enZcuWeHt74+HhoeYQ8vvvvychIYGqVavSo0cPhg0bRsmSJQtdzpIlS+jcuTNNmzblzp07BbaTkZER+/fvl1dSAwIC1Ey1VWzbtg1vb+98nTB+KLq6uhw7doxx48axdu1aateuTY0aNVi+fDnDhg2jYsWKQNa2m6pVq+ZqLeTl5YWtrS2VKlViwoQJuLm58dtvv6n5HvHx8eHAgQMcPXqUGjVqULt2bZYsWaJ2bbt27aJGjRr4+/tToUIFxo0bp/Y5ORXFihVj27ZtuLu707RpU3kP96eor/zQ0tIiLCyMy5cvU7FiRUaOHMnChQvV4uR3DzMzM2P9+vXUq1cPDw8PIiIi2L9/v7yHvSieB+PHj8ff35+ePXtSp04djIyM8PHxUfNfMHPmTKZMmcLcuXNxc3PD19eXgwcP5nuf0dbW5vnz5/Ts2RMXFxc6d+5MixYt8vWpogkKqTCG/u8QHx/PjRs3SE5OxtPTU2NveUlJSZiampKYmCic0wkEAoFAUACq1fanv1zl6c9XyXiVQjFjA0o28qRkwyzTy0+htIvnt0BQdEiSRK1atWQzWkHepKWlyd7q69Wr97nFAbL2PY8dO5YbN24U2WRZUfEl1tc/AaVSiZubG507d2bmzJmfW5wcfNAHLB0cHD7qLLdAIBAIBIL/U8qtGlTBusn/fTtYmZH5j1hlFwgEOVEoFKxbt47r169/blG+eOLj45k0adIXpYS2atWKu3fv8ujRow/yU/Ex+BLr60vkwYMHHD16lEaNGpGamsrKlSu5f/8+3bp1+9yi5YrGK+6F2Su0ePHifMPFjL1AIBAIBP88xPNbIBAIBP8WHj58SNeuXblx4waSJFGxYkXmzZsnO6v70tB4xf3qVfXP0Vy5coWMjAzZE+WdO3fkb98JBAKBQCAQCAQCgUDwpWJvb8/Zs2c/txgao7HifvLkSfn34sWLMTY25ocffpA9KSYkJNCnT5/3dskvEAgEAoFAIBAIBAKBICfv5ZyuVKlSHD16FHd3d7XzN27cwNvbmz///DPf9MLUTiAQCASCgpEyMlEU087z/6dGPL8FAoFAIPg8vJdzuqSkJP7+++8c5//++29evXr1wUIJBAKBQPC/TJYXeYkXZ67w4pcrZCSnUMzIAPOGVTFvUBVQCKd0AoFAIBD8D/Feirufnx99+vQhKCiImjVrAhAZGcnYsWNp3759kQooEAgEAsH/EpJSIvHS79xb9APpCUlqYS9+uYxOCRPKjOmFaY2KKLQUn0lKgUAgEAgEn5L3mq5fs2YNLVq0oFu3bpQuXZrSpUvTrVs3fH19WbVqVVHLKBAIBALB/wRSppLEize4PeW7HEq7ivSEJG5P+Y7Eizfk77sLBP+LbNy4ETMzs3zj9O7dm3bt2sn/GzduzIgRI/JN4+joyNKlSz9Yvi+d77//Hm9v749ejibt9L7ExcWhUCi4du1akearUCjYu3cvAM+ePaNkyZL88ccfRVrGh5CWlka5cuU4d+7cJy/71KlTKBQKXr58mWv4l1hfBZG9vXPjY/WzwvJeiruBgQGrVq3i+fPnXL16latXr/LixQtWrVqFoaFhUcsoEAgEAsH/CBL3Fv0AygIUcqWSe0GbgEK7qflk9O7dG4VCkeOIiYn53KIVCkdHR1l2Q0NDqlatyo4dOz63WP9YevfuTVxcnEZxT548ScuWLbGwsMDAwIAKFSowevRoHj16pHF5y5YtY+PGje8n7D+UmJgY+vTpw1dffYWuri5OTk74+/tz6dIlOc7bt2+ZMmUKgYGB8rlp06blOmbLly//OS7ji8HS0pKePXuq1dXHQpIk1q1bR61atTAyMsLMzIzq1auzdOlSUlJS5Hhr1qzBycmJunXryueyt5mhoSHOzs707t2by5cvf3S5s/Ml1teHYm9vz+PHj6lYsWKR5fk+fNAGOUNDQ8zNzTE3NxcKu0AgEAgEH4CUkcmL01fyXGl/l/QXibw4cxUpI/MjS/b++Pr68vjxY7XDycmp0PlkZmaiLGgy4yMyY8YMHj9+zNWrV6lRowZdunT5LCtdXwppaWmFiv/ixQu+++47svtDjo2NJTQ0NM80a9euxcvLCxsbG3bt2kV0dDRr1qwhMTGRoKAgjcs2NTUt8tXe9PT0Is0vPxwdHTl16pTG8S9dukS1atW4c+cOa9euJTo6mj17/h975x1VxdU97OcKSpOiooCKYqFpQFHEQhJRaRbsJWgUjJJYsb52LIldsSaWhIhd7LErGiFBNPaCYgNFotH4GhElCMhlvj/47vy4cIGLYkne86w1a92ZU2bPnnPOnX3KPrtxcHBgzJgxcrwdO3ZgYmKCu7u7Wvr69esXqLMnTpworccpVUpaDt+E/v37s2nTJp4+fap1mrVr1+Lh4VGi+/Tt25eRI0fSqVMnoqKiuHTpEiEhIezZs4fIyEgg11j99ttvGTBgQIH04eHhPHz4kGvXrvHdd9+RlpZG06ZNWb9+fYnkeFM+JH2VBjo6OlhaWqKr+1qrzEuN1zLcc3Jy+PrrrzE1NZWnypuZmfHNN9+U6I/11atX4hCHOMQhDnGI49UrFLo6PP31Qon+j5/+eh6Frs47lRNyndTmPTIzMzXKp6enh6Wlpdqho6PDokWLcHJywsjICGtra4YMGUJaWpqcTjW1du/evdSrVw89PT2Sk5PJzMxk7NixVKtWDSMjI5o2bVoio+Z1MTY2xtLSEjs7O7777jsMDAzYt28fSqWSAQMGUKtWLQwMDLC3t2fp0qVqaaOjo3Fzc8PIyAgzMzPc3d25d+8eAJcvX6ZVq1YYGxtjYmJC48aN1UZFT5w4wSeffIKBgQHW1tYEBwfz999/y+E2NjbMnj2bL774AmNjY2rUqMH333+vdv+TJ0/SsGFD9PX1cXV15aeffiow5fPq1au0bduW8uXLY2FhQd++fXny5Ikc7uHhwbBhwxg5ciTm5ub4+PggSRLTp0+nRo0a6OnpUbVqVYKDgzXqT19fnwcPHuDr68v9+/dZtWoVgYGBhXbi3L9/n+DgYIKDg1mzZg0eHh7Y2Njw6aefEhYWxtSpU9XiHzlyBEdHR8qXLy93FqnIP1U+P48fP8bPzw8DAwNq1aqlsTNBoVCwcuVKOnbsiJGREbNmzQJgz549NGrUCH19fWrXrs2MGTPIzs5WSxcWFkaXLl0wNDTE1taWvXv3FirLmyJJEoGBgdja2hITE0P79u2pU6cODRs2ZNq0aezZs0eOGxERgZ+fX4E8dHV1C9RZc3NzOdzGxoaZM2fSr18/ypcvT82aNdm7dy///e9/6dSpE+XLl8fZ2VmtHKv46aefsLW1RV9fHx8fH37//Xc5LDExkU6dOmFhYUH58uVp0qQJx44dU0tvY2PDN998Q79+/TAxMeHLL78scA+lUskXX3yBg4MDycnJQPHv6fbt23z66afo6+tTr149jh49WiDf+vXrU7VqVXbv3l3UK3gjtm3bxqZNm9iyZQuTJk2iSZMm2NjY0KlTJ44fP06rVq0AOH/+PImJibRv375AHmZmZlhaWmJjY4O3tzc7duygT58+DBs2jJSUFDlecW1LZmYm48ePx9raGj09PerWrcuPP/6oUe709HTatm2Lu7u7PH3+Q9LX2bNn8fLywtzcHFNTU1q2bMmFCwX/ax8+fEjbtm0xMDCgdu3a7NixQw7LP1VetVzg559/xtXVFUNDQ1q0aMHNmzflNMW176/Da3UbTJ48mR9//JG5c+fKPXUnTpxg+vTpZGRkyA1acURGRmJoaPg6IggEAoFA8K/BwMAAb29vstNKNrUv+0Vu/KioKF6+fPk2RFNDNfXQ2tpa7fq0adOYPn261vmUKVOGZcuWUatWLe7cucOQIUMYN26cmp+c9PR05s2bR1hYGJUqVaJKlSoMGzaM+Ph4IiIi5I9CX19f4uLisLW1fa1nysnJoUwZ7ccxdHV1KVu2LFlZWeTk5FC9enW2b99OpUqVOHnyJF9++SVWVlb07NmT7OxsOnfuTFBQEFu2bCErK4szZ86gUOQ6FezTpw8uLi6sXLkSHR0dLl26RNmyZYFcQ8bX15eZM2eyZs0a/vvf/zJs2DCGDRtGeHi4LE9oaCjffPMNkyZNYseOHQwePJiWLVtib2/P8+fP8fPzo127dmzevJl79+4VWN/97NkzWrduzcCBA1m8eDEvX75k/Pjx9OzZk+PHj8vx1q1bx+DBg4mNjQVg586dLF68mIiICOrXr8+jR4+4fPmyRp0ZGhoye/ZsDh48SMeOHcnOzub48ePys+Zn+/btZGVlMW7cOI3heUfQ09PTWbhwIRs2bKBMmTJ8/vnnjB07tsjR/LwEBgbyxx9/EBUVRdmyZQkODubx48cF4k2fPp25c+eyZMkSdHV1iYmJoV+/fixbtoxPPvmExMRE2ZDMO0V4xowZzJ8/nwULFrB8+XL69OnDvXv3qFixolbylYRLly5x7do1Nm/erLFM59XbiRMn6Nu372vdZ/HixcyePZuQkBAWL15M3759adGiBV988QULFixg/Pjx9OvXj2vXrsllPT09nVmzZrF+/XrKlSvHkCFD+Oyzz+TylJaWRrt27Zg1axZ6enqsX78ePz8/bt68SY0aNeR7L1y4kKlTp2qchp2ZmYm/vz9JSUnExMRQuXLlYt9TTk4OXbt2xcLCgtOnT5OamlqoDwQ3NzdiYmI0jnSXBps2bcLe3p5OnToVCFMoFJiamgIQExODnZ0dxsbGWuU7atQo1q9fz9GjR+nZs6dWbUu/fv04deoUy5Yto0GDBty9e1etM0/Fs2fPaN++PeXLl+fo0aNqdt2Hoq8XL14QEBDA8uXLkSSJ0NBQ2rVrx+3bt9V0GBISwty5c1m6dCkbNmzgs88+Iy4uDkdHx0JlmDx5MqGhoVSuXJlBgwbxxRdfyGW6qPb9tZFeAysrK2nPnj0Frv/0009S1apVi02fmpoqAdKTJ0+krKwscYhDHOIQhzj+5w9JkqRbM1ZJv7UJ0vq49fUqSZKkdybjkydPJED6/fffpdTUVPnIyMgo8F8fEBAg6ejoSEZGRvLRvXt3jd8F27dvlypVqiSfh4eHS4B06dIl+dq9e/ckHR0d6cGDB2pp27RpI02cOLHQb46EhATJ399fqly5smRnZyeNHz9eunr1qiRJknTlyhWpQ4cORX6z1KxZU1q8eLEkSZKUmZkpzZ49WwKk/fv3a4w/dOhQqVu3bpIkSdJff/0lAVJ0dLTGuMbGxtLatWs1hg0YMED68ssv1a7FxMRIZcqUkV6+fCnL9vnnn8vhOTk5UpUqVaSVK1dKkiRJK1eulCpVqiTHlyRJ+uGHHyRAunjxoiRJkvTNN99I3t7eavf5/fffJUC6efOmJEmS1LJlS8nFxUUtTmhoqGRnZyeX3aJ4+fKlFBISInl7e0tt2rSRxo8fL3366afS6dOnNcYfPHiwZGJiUmy+qnKSkJAgX/vuu+8kCwsL+TwgIEDq1KmTfN6yZUtpxIgRkiRJ0s2bNyVAOnPmjBx+/fp1CZDfuSRJEiCNHDlS7d5t2rSRZs+erXZtw4YNkpWVlVq6KVOmyOdpaWkSIB06dKjYZ1NRs2ZNKSoqSqu4W7dulQDpwoULRcZLSUmRAOnXX39Vuz5t2jSpTJkyanXWyMhI+uqrr9TkyVvmHj58KAFSSEiIfO3UqVMSID18+FCSpP97T7/99pscR6XnwsqAJElS/fr1peXLl6vdu3Pnzmpx7t69KwFSTEyM1KZNG+njjz+Wnj17JocX956OHDki6erqqrUrhw4dkgBp9+7daulGjRoleXh4FCpvfsLDw6WWLVtqHd/R0VHq2LFjsfFGjBghtW7dusB1TTJLUm79A6R58+ZJklR826KqF0ePHtV4/6ioKAmQrl+/Ljk7O0vdunWTMjMzC8T7UPSVH6VSKRkbG0v79u2TrwHSoEGD1OI1bdpUGjx4sCRJ/1fOVO2mSgfHjh2T4x84cEAC5Pa2qPb9dXmtEfenT59qdFTh4OBQorUMZcuWffOeB4FAIBAI/gVI2UoqftqIp79q70io4qeNkbKV7+y/VHUfExMTTExMio3fqlUrVq5cKZ+r/OEcO3aMOXPmcOPGDZ4/f052djYZGRmkp6fLIzblypXD2dlZThsXF4dSqcTOzk7tHpmZmVSqVKlQGUaNGoW7uzsTJ07kzp07bNmyhSZNmvDq1SvMzc2ZMWNGsc8xfvx4pkyZQkZGBuXLl2fu3LnyNNXvvvuONWvWkJyczMuXL8nKyqJhw4YAVKxYkcDAQHx8fPDy8sLT05OePXtiZWUFwOjRoxk4cCAbNmzA09OTHj16UKdOHSB3muWVK1fURo4lSSInJ4e7d+/Ko0B5daRQKLC0tJRHjG/evImzszP6+vpyHNU2viouX75MVFQU5cuXL/DciYmJsr4bN26sFtajRw+WLFlC7dq18fX1pV27dvj5+WlcA5qeno6FhQWHDx+mf//+DBo0iKCgIE6dOlVAHtVzqkZqi8PQ0FDWGYCVlZXGEXNNXL9+HV1dXbVnc3Bw0Lgm3tXVVe388uXLxMbGqs0yVSqVBcpx3vdjZGSEiYlJkfINGjSIjRs3yueqacg6OjrytbzLSvIi5fEhUBSq2Tl5y4UKe3v7AtP589f1vM9kYWEBgJOTU4Frjx8/xtLSEsidqdKkSRM5jkrP169fx83NjbS0NKZPn86BAwd4+PAh2dnZvHz5Up7uriL/e1Dh7+9P9erVOX78OAYGBvL14t7T9evXsba2pmrVqnJ48+bNNd7DwMCgSIdnycnJ1KtXTz7Pzs7m1atXanVr0qRJTJo0SWP6krw/Te+uMFT5qupUcW1LXFwcOjo6tGzZssh8vby8cHNzY+vWrWrlU8WHoq8///yTKVOmEB0dzePHj1EqlaSnpxcoW/nfe/PmzYv1Ip+3Lqja9cePH1OjRo0i2/fX5bUM9wYNGvDtt9+ybNkytevffvstDRo0eCOBBAKBQCD4X0Shq0PFTxpRtoKJVg7qylY0peLHLig0fDB9KBgZGVG3bl21a0lJSXTo0IHBgwcza9YsKlasyIkTJxgwYABZWVmywWNgYKBmvKWlpaGjo8P58+cLfCRqMjpVrF+/XjbEnJyc6NSpE5mZmaSkpMhGRXH85z//ITAwUF4DrpIrIiKCsWPHEhoaSvPmzTE2NmbBggWcPn1aThseHk5wcDCHDx9m69atTJkyhaNHj9KsWTOmT59O7969OXDgAIcOHWLatGlERETQpUsX0tLS+OqrrzSuG887dTh/p41CoSiRv6G0tDT8/PyYN29egTDVhyhQwAmxtbU1N2/e5NixYxw9epQhQ4awYMECfvnllwIyVaxYkaFDh6pdq1OnTqEfsXZ2dqSmpvLw4UM1GTSh6fm1/aAvCfmfPy0tjRkzZtC1a9cCcfMaVSV9P19//TVjx46Vzz08PJg3bx5NmzYtVkZVJ8uNGzdwcXEpNF6lSpVQKBRqa55VlCtXrkCdzU/eZ1LVBU3XSlIOx44dy9GjR1m4cCF169bFwMCA7t27F3BAV5gz7Hbt2rFx40ZOnTpF69at5evavidtePr0KZUrVy40vGrVqmqG3q5du9i5c6eagVzUEgk7Oztu3LhRrBzm5ubExcVpJzS5HVSA7FOiuLZF250/2rdvLzuOzNtxo+JD0VdAQAB//fUXS5cupWbNmujp6dG8efNScW5YVLkvqn1/XV7LcJ8/fz7t27fn2LFjcu/EqVOnSE5O5tChQ68tjEAgEAgE/9soqD02gJsh3xW9JVyZMtQe2w/QblTyQ+L8+fPk5OQQGhoqr8Pdtm1bselcXFxQKpU8fvyYTz75ROv7aRo9VTnN0xZzc3ONxkxsbCwtWrRgyJAh8rXExMQC8VxcXHBxcWHixIk0b96czZs306xZMyD349POzo5Ro0bh7+9PeHg4Xbp0oVGjRsTHxxdrRBWFvb09GzduJDMzEz09PSDXUVNeGjVqxM6dO7GxsSmxx2QDAwP8/Pzw8/Nj6NChODg4EBcXR6NGjQpNo83WbN27d2fChAnMnz+fxYsXFwh/9uxZqXiKd3BwIDs7m/Pnz8ujwTdv3ix0f+q8NGrUiJs3b77R+9FElSpVqFKlinyuq6tLtWrVtLpPw4YNqVevHqGhofTq1avAOneV3sqVK0e9evWIj49/J/u4Q+5o6rlz5+QZFio9q2aOxMbGEhgYKBs1aWlpWm8bCDB48GA++ugjOnbsyIEDB+TR4uLek6OjI7///rtaJ9Fvv/2mMe7Vq1eL9Hquq6urdp8qVapgYGCgdRnp3bs3n332GXv27CmwbluSJJ4/f46pqam8blrbmSlLlizBxMQET09PgGLbFicnJ3Jycvjll1/kNJqYO3cu5cuXp02bNkRHR6uNnsOHo6/Y2FhWrFhBu3btAPj99981rtf/7bff6Nevn9p5UR1g2lBY+/66vJZX+ZYtW3Lz5k26du3Ks2fPePbsGV27duXWrVsl+jMVCAQCgUDwfyh0ymDa5CPsvxlK2YqmGuOUrWiK/TdDMXX9CIXOG+3q+l6oW7cur169Yvny5dy5c4cNGzawatWqYtPZ2dnRp08f+vXrx65du7h79y5nzpxhzpw5HDhw4B1IXhBbW1vOnTvHkSNHuHXrFiEhIWqG8d27d5k4cSKnTp3i3r17REZGcvv2bRwdHXn58iXDhg0jOjqae/fuERsby9mzZ2VDZvz48Zw8eZJhw4Zx6dIlbt++zZ49exg2bJjW8vXu3ZucnBy+/PJLrl+/zpEjR1i4cCHwf6NDQ4cO5enTp/j7+3P27FkSExM5cuQI/fv3R6ksfKvBtWvX8uOPP3L16lXu3LnDxo0bMTAwoGbNmq+jSjWsra1ZvHgxS5cuZcCAAfzyyy+yjr766iu++eabN74H5HZs+Pr68tVXX3H69GnOnz/PwIED1aZaF8bUqVNZv349M2bM4Nq1a1y/fp2IiAimTJlSKrK9DgqFgvDwcPl7/ODBg9y5c4crV64wa9YsNePGx8dH4zZv2dnZPHr0SO34888/31i2smXLMnz4cFnPgYGBNGvWTDbkbW1t2bVrF5cuXeLy5cty2S0Jw4cPZ+bMmXTo0EF+tuLek6enJ3Z2dgQEBHD58mViYmKYPHlygbzT09M5f/78W+3o6NmzJ7169cLf35/Zs2dz7tw57t27x/79+/H09CQqKgrIXYKUlpbGtWvXCuTx7NkzHj16xL179zh69Cjdu3dn8+bNrFy5Uu7sKq5tsbGxISAggC+++IKffvqJu3fvEh0drbGDdeHChfTp04fWrVurjX5/SPqytbVlw4YNXL9+ndOnT9OnTx+NdXz79u2sWbOGW7duMW3aNM6cOVOi9jYvxbXvr83rLo5/+fKldPr0aWnfvn3Snj171I7iUDmnS01Nfd3bCwQCgUDwryUnWynlZGdLT6LPSre+XiXF/2eRdOvrVdKT6LNSTna2lJOtfC9yleT/O79TsLwsWrRIsrKykgwMDCQfHx9p/fr1EiClpKRIkpTrpMjU1LRAuqysLGnq1KmSjY2NVLZsWcnKykrq0qWLdOXKlTd4qqLJ65wuPxkZGVJgYKBkamoqmZmZSYMHD5YmTJggNWjQQJIkSXr06JHUuXNnycrKSipXrpxUs2ZNaerUqZJSqZQyMzOlzz77TLK2tpbKlSsnVa1aVRo2bJiaI7kzZ85IXl5eUvny5SUjIyPJ2dlZmjVrVpGyNWjQQJo2bZp8HhsbKzk7O0vlypWTGjduLG3evFkCpBs3bshxbt26JXXp0kUyMzOTDAwMJAcHB2nkyJFSTk6OJEnqDt1U7N69W2ratKlkYmIiGRkZSc2aNVNz1FQaHD16VPLx8ZEqVKgg6evrSw4ODtLYsWOlP/74Q5IkzeVk9+7dUt7P26Kc00lSrnO19u3bS3p6elKNGjWk9evXF9ArhTj9Onz4sNSiRQvJwMBAMjExkdzc3KTvv/++yHSmpqZSeHi41jooiXM6FTdv3pT69esnVa1aVS53/v7+ak7rrl27JhkYGKg5cps2bZoEFDj09PTU5Mlf5vI/Z35HXqr3tHPnTql27dqSnp6e5OnpKd27d08tTatWrSQDAwPJ2tpa+vbbbwu8K033zn8vScp1nGhsbCzFxsZKklT8e7p586b08ccfS+XKlZPs7Oykw4cPF3imzZs3S/b29sWpXo2SOluTpFzHaStXrpSaNGkiGRoaSiYmJlLjxo2lpUuXSunp6XK8nj17ShMmTFBLm/ed6evrS3Xq1JECAgKk8+fPF7hPcW3Ly5cvpVGjRsltV926daU1a9ZIkvR/jtlU7bUkSdLw4cMlKysr2aHlh6SvCxcuSK6urpK+vr5ka2srbd++XWMd/+677yQvLy9JT09PsrGxkbZu3SqHF+acLq8OLl68KAHS3bt3tWrfXwfF/xe2RBw+fJh+/frx119/FVhHpFAoiuyhBeSpC6mpqVo5txEIBAKB4H8RKVuJQlen0PN3jfj//uezadMm+vfvT2pqqlYjy4J/Lz169KBRo0ZMnDjxfYvywdOsWTOCg4Pp3bv3+xYFgCtXruDl5UViYmKRPj7eFx+avv4tvNYcu+HDh9OjRw/++OMPcnJy1I7ijHaBQCAQCATakd9If59Gu+Cfyfr16zlx4gR3797lp59+kvdoF0a7YMGCBR+k0feh8eTJE7p27Yq/v//7FkXG2dmZefPmcffu3fctSgE+RH39W3itEXcTExMuXrz42i7tRY+9QCAQCAT/PMT/9z+P+fPns2LFCh49eoSVlRWdO3dm1qxZsvd+gUAgEPwzeC3D/YsvvsDd3Z0BAwa81k3FH79AIBAIBP88xP+3QCAQCATvh9cy3NPT0+nRoweVK1fGycmpwB6VmvYFzIv44xcIBAKB4J+H+P8WCAQCgeD98Fr7uG/ZsoXIyEj09fWJjo5W20NQoVAUa7gLBALBv4EPzXGYQCAQCAQCgeDfyWsZ7pMnT2bGjBlMmDCBMmX+eXvICgQCwZsgKXMAidST50mNPYcyLR2d8oaYurti6t4YUPwj99cWCAQCgUAgEHyYvNaXZVZWFr169RJGu0Ag+J9DypF4ceEq1wPGkDxvFaknzpF2KZ7UE+dInreK6wFjeHHhKlJOiVchCQSCfzghISF8+eWXb/0+06dPp2HDhm8lb9VMymfPnpVanklJSSgUCi5dugRAfHw81atX5++//y61e5SUvn37Mnv27Pdyb4VCwU8//VRoeLNmzdi5c+e7E+gN8fDwYOTIkUXGsbGxYcmSJe9EHsG/i/zthybWrl2LmZmZfK5NGxkYGEjnzp1LRcZ3xWtZ3gEBAWzdurW0ZREIBIIPGkmZw4vzcSR9vYzslOca42SnPCfp62W8OB/3/0fmBYL/TQIDA1EoFAWOhISE9y1aibl48SI9evTAwsICfX19bG1tCQoK4tatW3KcR48esXTpUiZPnixfK0wHvr6+7+MxPhjq1atHs2bNWLRokVbxAwMDSUpK0ipuVFQU7dq1o1KlShgaGlKvXj3GjBnDgwcP5DiXL1/m4MGDaks7PTw85Pejp6dHtWrV8PPzY9euXSV6ttJgypQpTJgwgZyct/8foo2+SoOzZ8++k04tDw+PYuOoOqdUh4WFBd26dePOnTtvXT5BQRISEujfvz/Vq1dHT0+PWrVq4e/vz7lz57TOo1evXmrt8b+V1zLclUol8+fPp2XLlgwfPpzRo0erHQKBQPDvROL+0jVQ3MdUTg73l4YDYtRd8L+Nr68vDx8+VDtq1apV4nyUSuU7MWI0sX//fpo1a0ZmZiabNm3i+vXrbNy4EVNTU0JCQuR4YWFhtGjRgpo1a6ql16SDLVu2vOvH0IpXr169s3v179+flStXkp2drTH86dOnfPfdd+T1oZyYmMimTZsKzXP16tV4enpiaWnJzp07iY+PZ9WqVaSmphIaGirHW758OT169Ciwh3lQUBAPHz4kMTGRnTt3Uq9ePT777LN3YnDmpW3btrx48YJDhw5pnUY1KlkStNVXaVC5cuW3tgVhbGwsx44dU7t27NgxTp48WWS6mzdv8scff7B9+3auXbuGn58fSqXyrcj4T6A06r+NjQ3R0dFaxz937hyNGzfm1q1brF69mvj4eHbv3o2DgwNjxozROh8DAwOqVKnyGhIXTlZWVqnmVxq8luEeFxeHi4sLZcqU4erVq1y8eFE+iprGIBAIBP9UpGwlqbHnCx1pz092SiqpJy8gZf/vfgQIBHp6elhaWqodOjo6LFq0CCcnJ4yMjLC2tmbIkCGkpaXJ6VTTHvfu3Uu9evXQ09MjOTmZzMxMxo4dS7Vq1TAyMqJp06Yl+kgsKenp6fTv35927dqxd+9ePD09qVWrFk2bNmXhwoWsXr1ajhsREYGfn59WOqhQoYIcrlAoWL16NR06dMDQ0BBHR0dOnTpFQkICHh4eGBkZ0aJFCxITEwvkvXr1aqytrTE0NKRnz56kpqbKYWfPnsXLywtzc3NMTU1p2bIlFy5cUEuvUChYuXIlHTt2xMjIiFmzZmnUQdu2bXF3d5enz4eFheHo6Ii+vj4ODg6sWLFCLc2ZM2dwcXFBX18fV1dXLl68WCBfLy8vnj59yi+//KJR9/r6+jx48ABfX1/u37/PqlWrCAwMLLTj5/79+wQHBxMcHMyaNWvw8PDAxsaGTz/9lLCwMKZOnQrkdgLt2LFD47syNDTE0tKS6tWr06xZM+bNm8fq1av54Ycf1AzD33//nZ49e2JmZkbFihXp1KlTgRkBa9asoX79+ujp6WFlZcWwYcM0yg0wbdo0rKysuHLlCgA6Ojq0a9eOiIiIQtO8Kdrq66+//sLf359q1aphaGiIk5OTxo6n7Oxshg0bhqmpKebm5oSEhKh1uuSfKq9QKAgLC6NLly4YGhpia2vL3r175fCUlBT69OlD5cqVMTAwwNbWlvDwcI3PUqNGDVavXs2QIUN48eIFQ4YM4fvvv8fa2rpIHVSpUgUrKys+/fRTpk6dSnx8PAkJCcXWHUmSmD59OjVq1EBPT4+qVauqzd5YsWIFtra26OvrY2FhQffu3eWwnJwc5syZQ61atTAwMKBBgwbs2LFDDlfNBvj5559xdXXF0NCQFi1acPPmTTXZZ86cSZUqVTA2NmbgwIFMmDChwNTwouqpqqNn69attGzZEn19fTZt2sS9e/fw8/OjQoUKGBkZUb9+fQ4ePFikHl8XSZIIDAzE1taWmJgY2rdvT506dWjYsCHTpk1jz549avHv3LlDq1atMDQ0pEGDBpw6dUoOyz9VPj9KpZLRo0djZmZGpUqVGDduHPk3VvPw8GDYsGGMHDkSc3NzfHx8ALh69Spt27alfPnyWFhY0LdvX548eaKWLjg4mHHjxlGxYkUsLS2ZPn36mytIA6/lnC4qKqpUbv7q1at32rsrEAgEr0vZsmVJjdV+2hZA6olzmH3SRLRzgn8NqrL8/Ll6B5aenh56enpa51OmTBmWLVtGrVq1uHPnDkOGDGHcuHFqH5bp6enMmzePsLAwKlWqRJUqVRg2bBjx8fFERERQtWpVdu/eja+vL3Fxcdja2r7WM+Xk5BTqs+fIkSM8efKEcePGaQxXfSg+ffqU+Ph4XF1dX0uGb775hkWLFrFo0SLGjx9P7969qV27NhMnTqRGjRp88cUXDBs2TG0ENiEhgW3btrFv3z6eP3/OgAEDGDJkiDwi/eLFCwICAli+fDmSJBEaGkq7du24ffs2xsbGcj7Tp09n7ty5LFmyBF1dXbXpws+ePaN9+/aUL1+eo0ePYmhoyKZNm5g6dSrffvstLi4uXLx4kaCgIIyMjAgICCAtLY0OHTrg5eXFxo0buXv3LiNGjCjwzOXKlaNhw4bExMTQpk2bAuGGhobMnj2bgwcP0rFjR7Kzszl+/HiBLYhVbN++naysrGLf1ZUrV0hNTdX6XQUEBDBmzBh27dqFp6cnr169wsfHh+bNmxMTE4Ouri4zZ87E19eXK1euUK5cOVauXMno0aOZO3cubdu2JTU1ldjY2AJ5S5JEcHAw+/fvJyYmhrp168phbm5uzJ07VysZXwdt9ZWRkUHjxo0ZP348JiYmHDhwgL59+1KnTh3c3Nzk+OvWrWPAgAGcOXOGc+fO8eWXX1KjRg2CgoIKlWHGjBnMnz+fBQsWsHz5cvr06cO9e/eoWLEiISEhxMfHc+jQIczNzUlISODly5ca87G2tmb79u1MnDiRCxcu4OPjU6AzqTgMDAyA3FHW4urOzp07Wbx4MREREdSvX59Hjx5x+fJlIHcEOTg4mA0bNtCiRQuePn1KTEyMfJ85c+awceNGVq1aha2tLb/++iuff/45lStXpmXLlnK8yZMnExoaSuXKlRk0aBBffPGFXIY2bdrErFmzWLFiBe7u7kRERBAaGqrWqVVcPVUxYcIEQkND5Y62oKAgsrKy+PXXXzEyMiI+Pr7AzJTS4tKlS1y7do3NmzdrbIPzG+KTJ09m4cKF2NraMnnyZPz9/UlISEBXt3hzNjQ0lLVr17JmzRocHR0JDQ1l9+7dtG7dWi3eunXrGDx4sKzrZ8+e0bp1awYOHMjixYt5+fIl48ePp2fPnhw/flwt3ejRozl9+jSnTp0iMDAQd3d3vLy8XkMzhfNahntpERkZ+damzQgEAkFpYWBggLe3N8q09BKlU6blOl6Kiooq9INDIPgnkZ6eWwfyj2RNmzZN4wjD/v371T762rZty/bt29UcWdnY2DBz5kwGDRqk9rH96tUrVqxYQYMGDQBITk4mPDyc5ORkqlatCsDYsWM5fPgw4eHhhToaS0xMJCQkhGPHjlGhQgW6dOlC3759qV+/PnFxcUyaNIl9+/ZpTHv79m0AHBwcitRLcnIykiTJchWlA4BJkyYxadIk+bx///707NkTgPHjx9O8eXNCQkLkEZ8RI0bQv39/tTwyMjJYv3491apVA3Knf7dv357Q0FAsLS0LfJB+//33mJmZ8csvv9ChQwf5eu/evdXyVhnujx49olevXtja2rJ582bKlSsH5L7r0NBQunbtCkCtWrWIj49n9erVBAQEsHnzZnJycvjxxx/R19enfv363L9/n8GDBxfQTdWqVbl3755GnWZkZDB79mxOnz6Nh4cHrq6ueHp6smDBAjWDUcXt27cxMTHByspKY34q7t27h46OjtbTasuUKYOdnZ08or5161ZycnIICwuTp6aHh4djZmZGdHQ03t7ezJw5kzFjxqh1WDRp0kQt3+zsbD7//HMuXrzIiRMn5PeoomrVqvz+++9Fdiy9Cdrqq1q1aowdO1Y+Hz58OEeOHGHbtm1q78Ha2prFixejUCiwt7cnLi6OxYsXF2m4BwYG4u/vD8Ds2bNZtmwZZ86cwdfXl+TkZFxcXOQOFhsbm0LzefDgAWPGjKFChQo0atSIlJQUPvvsM0JDQwvoVRMPHz5k4cKFVKtWDXt7e5ycnNTC89ed5ORkLC0t8fT0pGzZstSoUUPWRXJyMkZGRnTo0AFjY2Nq1qyJi4sLAJmZmcyePZtjx47RvHlzAGrXrs2JEydYvXq1muE+a9Ys+XzChAm0b9+ejIwM9PX1Wb58OQMGDJDr7dSpU4mMjFSbtVRcPVUxcuRIOY5K/m7dusk6qF27drH6e120bV9VjB07lvbt2wO5nT7169cnISFBq/RLlixh4sSJ8rOuWrWKI0eOFIhna2vL/Pnz5fOZM2fi4uKi9v+yZs0arK2tuXXrFnZ2dgA4Ozszbdo0OY9vv/2Wn3/++d9luHt7e2NiYvI+RRAIBAKt0Slfso5GnfJGALRq1eptiCMQvHNUI+2///672v93YaPtrVq1YuXKlfK5kVFunTh27Bhz5szhxo0bPH/+nOzsbDIyMkhPT5c79MuVK4ezs7OcNi4uDqVSKX8oqcjMzKRSpUqFyjxq1Cjc3d2ZOHEid+7cYcuWLTRpkjsTxtzcnBkzZhSaNv9UysJQdczp6+sXCMuvA4CKFSuqned9TgsLCwA148HCwoKMjAyeP38u671GjRpqRknz5s3Jycnh5s2bWFpa8ueffzJlyhSio6N5/PgxSqWS9PR0kpOT1e5d2Mizl5cXbm5ubN26FR0dHQD+/vtvEhMTGTBggJpBlp2djampKQDXr1/H2dlZTRcqIyU/BgYGcmdQftLT07GwsODw4cP079+fQYMGERQUxKlTpzQa7pIkabXG++XLl+jp6ZVoPXjevC9fvkxCQoLarAXI7WhITEzk8ePH/PHHHxpnEeRl1KhR6Onp8dtvv2Fubl4g3MDAgJycHDIzM+XR4PzUr19f7vhQldW8nUSffPJJoevktdWXUqlk9uzZbNu2jQcPHpCVlUVmZmaBgbdmzZqp5de8eXNCQ0NRKpVy+clP3nJvZGSEiYkJjx8/BmDw4MF069aNCxcu4O3tTefOnWnRooXGfJKSkhg4cCCenp54eHiwcuVKjh07RlJSUpGGe/Xq1ZEkifT0dBo0aMDOnTspV65csXWnR48eLFmyhNq1a+Pr60u7du3w8/NDV1cXLy8vatasKYf5+vrKywESEhJIT08vYMxlZWXJxr0m3ag6Vx4/fkyNGjW4efMmQ4YMUYvv5uYmjwBrU09V5K//wcHBDB48mMjISDw9PenWrZuaLPkZNGgQGzdulM9VS2vyvvO8HQp50bZ9VVGYTooz3FNTU3n48CFNmzaVr+nq6uLq6lpAhsaNG6udX758maioKI2zDhITE9UM97xYWVnJZbk0ea+Ge9myZQud8iQQCAQfElK2ElN3V1JPaD9d3vRjV6RspWjnBP8aVGXZxMREq453IyMjtem/kPuR3aFDBwYPHsysWbOoWLEiJ06cYMCAAWRlZckGgYGBgZohkJaWho6ODufPny9gCBQ1lXP9+vXylEsnJyc6depEZmYmKSkpWFpaFim/6qPsxo0bhRqfgGx4paSkULly5WJ1kJ+8bYTqmTVdK4mDvoCAAP766y+WLl1KzZo10dPTo3nz5gUcLqk6U/LTvn172WGZqhNB9QH+ww8/qH0EA4UaZ0Xx9OlT6tSpozGsYsWKDB06VO1anTp1Co1vZ2cnf6AXNYpsbm5Oeno6WVlZ8iyColAqldy+fVseMU9LS6Nx48YaneRVrlxZ69FxLy8vtmzZwpEjR+jTp0+B8KdPn2JkZFSo0Q5w8OBBefnKgwcP8PDwUPM1VVRabfW1YMECli5dypIlS2S/FCNHjiwVx135/xsVCoVcxtu2bcu9e/c4ePAgR48epU2bNgwdOpSFCxcWyMfd3b3ANU9Pz2LvHxMTg4mJibxWXEVxdcfa2pqbN29y7Ngxjh49ypAhQ1iwYAG//PILxsbGXLhwgejoaCIjI5k6dSrTp0/n7Nmzcv05cOBAgQ6F/J2fb1L/S1JP89f/gQMH4uPjw4EDB4iMjGTOnDmEhoYyfPhwjff6+uuv1WZkeHh4MG/evAL31UTe9jV/x4Um3rRN1Ib8+khLS8PPz4958+YViJu33hRVlksTsRG7QCAQaIFCVwdT98boVtBulpBuBVNMWzRCoVvyj1mB4N/M+fPnycnJITQ0lGbNmmFnZ8cff/xRbDoXFxeUSiWPHz+mbt26akdRBrgmh0Uqh3HF4e3tjbm5udrUybyonLXVqVMHExMT4uPji82ztEhOTlbT22+//UaZMmWwt7cHcj1tBwcH065dO9lJWl6HSsUxd+5cAgICaNOmjfxcFhYWVK1alTt37hR4B6r1tY6Ojly5coWMjAw12TRx9epVrT7Y165dW+RUaYDu3btTrly5Yt+VyoGXtu9q3bp1pKSk0K1bNwAaNWrE7du3qVKlSgEdmJqaYmxsjI2NDT///HOR+Xbs2JHNmzczcOBAjU7otNFNzZo15XurdjPIK09Ro83a6is2NpZOnTrx+eef06BBA2rXrq1x263Tp0+rnf/222/Y2tq+VoeOisqVKxMQEMDGjRtZsmQJ33//fbFpSuKsslatWtSpU6fA7Alt6o6BgQF+fn4sW7aM6OhoTp06RVxcHJA7muvp6cn8+fO5cuUKSUlJHD9+XM3RZv6yU5wjvbzY29tz9uxZtWt5z7Wpp0VhbW3NoEGD2LVrF2PGjOGHH34oNG7+eqCrq0u1atXUrhVGw4YNqVevHqGhoRqNXFUZfFNMTU2xsrJSK6PZ2dmcP3++2LSNGjXi2rVr2NjYFNBlYZ2eb5P3OuIuEAgE/ywUVB/xBUlfLyt6S7gyZag+sj9Qsq15BIL/BerWrcurV69Yvnw5fn5+xMbGsmrVqmLT2dnZ0adPH/r16yc7U/rvf//Lzz//jLOzs7z2sTQxMjIiLCyMHj160LFjR4KDg6lbty5Pnjxh27ZtJCcnExERQZkyZfD09OTEiRN07txZLY/MzEwePXqkdk1XV1fj9OiSoK+vT0BAAAsXLuT58+cEBwfTs2dPuUPC1taWDRs24OrqyvPnz/nPf/5T5AisJhYuXIhSqaR169ZER0fj4ODAjBkzCA4OxtTUFF9fXzIzMzl37hwpKSmMHj2a3r17M3nyZIKCgpg4cSJJSUkaR0mTkpJ48OCBViOj2qBaYz1s2DCeP39Ov379sLGx4f79+6xfv57y5cvLzr4aNWrEiRMnCnjhTk9P59GjR2RnZ3P//n12797N4sWLGTx4sLzkqU+fPixYsIBOnTrx9ddfU716de7du8euXbsYN24c1atXZ/r06QwaNIgqVarIW7vFxsYWGLXs0qULGzZsoG/fvujq6qp5H4+JicHb27tUdPMm+rK1tWXHjh2cPHmSChUqsGjRIv7880/q1aunll9ycjKjR4/mq6++4sKFCyxfvvyNtpSbOnUqjRs3pn79+mRmZrJ//34cHR3f9LG1ori6s3btWpRKJU2bNsXQ0JCNGzdiYGBAzZo12b9/P3fu3OHTTz+lQoUKHDx4kJycHOzt7TE2Nmbs2LGMGjWKnJwcPv74Y9lxoYmJidra86IYPnw4QUFBuLq60qJFC7Zu3cqVK1fU1qMXV08LY+TIkbRt2xY7OztSUlKIiop6a3pXKBSEh4fj6enJJ598wuTJk3FwcCAtLY19+/YRGRlZ6K4TJWXEiBHMnTsXW1tbHBwcWLRokVYdA0OHDuWHH37A399f9hqfkJBAREQEYWFhb9Qx9TqIEXeBQCDQEoVOGYwbO2EzNRjdCqYa4+hWMMVmajDGjZxQ6IgmViDIT4MGDVi0aBHz5s3jo48+YtOmTcyZM0ertOHh4fTr148xY8Zgb29P586dOXv2LDVq1Hhr8nbq1ImTJ09StmxZevfujYODA/7+/qSmpjJz5kw5nmrkNP/I0eHDh7GyslI7Pv744zeWq27dunTt2pV27drh7e2Ns7OzmnO/H3/8kZSUFBo1akTfvn0JDg5+rX2OFy9eTM+ePWndujW3bt1i4MCBhIWFER4ejpOTEy1btmTt2rXySF758uXZt2+fvHXw5MmTNU4z3bJlC97e3gX2vX8ThgwZQmRkJA8ePKBLly44ODgwcOBATExM1KbzDhw4UONU9x9++AErKyvq1KlD165diY+PZ+vWrWp6NTQ05Ndff6VGjRp07doVR0dHBgwYQEZGhrx8JCAggCVLlrBixQrq169Phw4dZEdc+enevTvr1q2jb9++7Nq1C8id9n7y5MkCDglLG230NWXKFBo1aoSPjw8eHh5YWloW6JwC6NevHy9fvsTNzY2hQ4cyYsQIvvzyy9eWrVy5ckycOBFnZ2c+/fRTdHR03ur2eHkpru6YmZnxww8/4O7ujrOzM8eOHWPfvn1UqlQJMzMzdu3aRevWrXF0dGTVqlVs2bKF+vXrA7k7SISEhDBnzhwcHR3x9fXlwIEDWo2Eq+jTpw8TJ05k7NixNGrUiLt37xIYGKjmV6K4eloYSqWSoUOHyrLZ2dmV2EN/SXBzc+PcuXPUrVuXoKAgHB0d6dixI9euXVPbPvBNGTNmDH379iUgIIDmzZtjbGxMly5dik1XtWpVYmNjUSqVeHt74+TkxMiRIzEzM3srTiOLQyGV1DNAKfD8+XNMTU1JTU0VzukEAsE/DkmZA0iknrxA6olzKNP+Rqe8EaYfu2LaohGgEEa74F+J+P8uHEmSaNq0KaNGjZI9ZQs0k5WVJXur17Q++W3z8uVL7O3t2bp1a5G+C94X48ePJyUlRaup4QIB5PpMsLS0ZMOGDe9bFMFbREyVFwgEghKiMspNmzfC7JP/2+JHylaieMfTpgQCwYeBQqHg+++/l9e5CgonOTmZSZMmvRejHXLXJ69fv75Ea/7fJVWqVClyOrPgf5v09HRWrVqFj48POjo6bNmyRXaUJ/h3I0bcBQKBQCAQaIX4/xYIBIL3y8uXL/Hz8+PixYtkZGRgb2/PlClT1PZjF/w7ESPuAoFAIBAIBAKBQPAPwMDAgGPHjr1vMQTvAbEIUyAQCAQCgUAgEAgEgg+Yf43hLimVRZ4LBAKBQCAQCAQCgUDwT+QfP1Ve5d35xW/nSDt1lpy//6aMkRHlmzfBuJkrwruzQCAQCAQCgUAgEAj+yfyjDXcpJ4f0y3E8+u5HlM9S1cLSTp3lv2amWA4dgGFDZxRlFO9JSoFAIBAIBAKBQCAQCF6ff+xQtKTMIf1SHA/mLClgtKtQPkvlwZwlpF+68v9H5gUCgUAgEAjeDiEhIXz55Zdv/T7Tp0+nYcOGbyXv6OhoFAoFz549K7U8k5KSUCgUXLp0CYD4+HiqV6/O33//XWr3KE08PDwYOXJkkXFsbGxYsmTJO5FH8O/kxx9/xNvb+73cu7gyPmHCBIYPH/7uBCqC/O2HJtauXYuZmZl8rk0bGRgYSOfOnUtFxnfFP9ZwB4lH3/0IOcUY5Dk5ufF457veCQQCgUDwP0tgYCAKhaLAkZCQ8L5FKzEXL16kR48eWFhYoK+vj62tLUFBQdy6dUuO8+jRI5YuXcrkyZPla4XpwNfX9308xgdDvXr1aNasGYsWLdIqfmBgIElJSVrFjYqKol27dlSqVAlDQ0Pq1avHmDFjePDgwRtIXJCzZ8++k04aDw+PYuOoOltUh4WFBd26dePOnTtvXT5BQRISEujfvz/Vq1dHT0+PWrVq4e/vz7lz5+Q4GRkZhISEMG3aNPna9OnT5Xeoq6uLubk5n376KUuWLCEzM/OdPsPYsWNZt27dOylD2uirOHr16qXWHv9b+Uca7pJSyYvfzhU60p4f5bNU0k6fFw7rBAKBQCB4h/j6+vLw4UO1o1atWiXOR6lUklNcR/1bYv/+/TRr1ozMzEw2bdrE9evX2bhxI6ampoSEhMjxwsLCaNGiBTVr1lRLr0kHW7ZsedePoRWvXr16Z/fq378/K1euJDs7W2P406dP+e6775Ck/xt4SUxMZNOmTYXmuXr1ajw9PbG0tGTnzp3Ex8ezatUqUlNTCQ0NLVX5K1eujKGhYanmqSI2NrbAdl/Hjh3j5MmTRaa7efMmf/zxB9u3b+fatWv4+fmh/B/+9i2N8mxjY0N0dLTW8c+dO0fjxo25desWq1evJj4+nt27d+Pg4MCYMWPkeDt27MDExAR3d3e19PXr1+fhw4ckJycTFRVFjx49mDNnDi1atODFixdv/DzaYm5ujo+PDytXrixRurelr+IwMDCgSpUqJZK1OLKysko1v9LgvRrur169eq1DoaND2qmzJbrXi1NnUejovPY9xSEOcYhDHOIQR+6H8PPnz9WOwkaD9PT0sLS0VDt0dHRYtGgRTk5OGBkZYW1tzZAhQ0hLS5PTqaY97t27l3r16qGnp0dycjKZmZmMHTuWatWqYWRkRNOmTUv0kVhS0tPT6d+/P+3atWPv3r14enpSq1YtmjZtysKFC1m9erUcNyIiAj8/P610UKFCBTlcoVCwevVqOnTogKGhIY6Ojpw6dYqEhAQ8PDwwMjKiRYsWJCYmFsh79erVWFtbY2hoSM+ePUlN/b8BjbNnz+Ll5YW5uTmmpqa0bNmSCxcuqKVXKBSsXLmSjh07YmRkxKxZszTqoG3btri7u8vT58PCwnB0dERfXx8HBwdWrFihlubMmTO4uLigr6+Pq6srFy9eLJCvl5cXT58+5ZdfftGoe319fR48eICvry/3799n1apVBAYGFtrxc//+fYKDgwkODmbNmjV4eHhgY2PDp59+SlhYGFOnTgXgr7/+wt/fn2rVqmFoaIiTk5PGjpTs7GyGDRuGqakp5ubmhISEqHUi5J8qr1AoCAsLo0uXLhgaGmJra8vevXvl8JSUFPr06UPlypUxMDDA1taW8PBwjc9So0YNVq9ezZAhQ3jx4gVDhgzh+++/x9raWmN8FVWqVMHKyopPP/2UqVOnEh8fT0JCQrFlQZIkpk+fTo0aNdDT06Nq1aoEBwfL4StWrMDW1hZ9fX0sLCzo3r27HJaTk8OcOXOoVasWBgYGNGjQgB07dsjhqtkAP//8M66urhgaGtKiRQtu3rypJvvMmTOpUqUKxsbGDBw4kAkTJhSY6lxUuVNNp966dSstW7ZEX1+fTZs2ce/ePfz8/KhQoQJGRkbUr1+fgwcPFqnH10WSJAIDA7G1tSUmJob27dtTp04dGjZsyLRp09izZ48ct7D2QldXF0tLS6pWrYqTkxPDhw/nl19+4erVq8ybN0+Op01bGBsbi4eHB4aGhlSoUAEfHx9SUlI0yn7gwAFMTU3VOsb8/PyIiIh4Q60UTkn0BXDnzh1atWqFoaEhDRo04NSpU3JY/qny+VEqlYwePRozMzMqVarEuHHj1Ooz5M5uGTZsGCNHjpQ7LgCuXr1K27ZtKV++PBYWFvTt25cnT56opQsODmbcuHFUrFgRS0tLpk+f/uYK0sB7dU4XGRlZ4t5KAwMDvL29ySnhuqictNz4UVFRvHz5skRpBQKBQCAQ5BpxQAEDYtq0aSX6UClTpgzLli2jVq1a3LlzhyFDhjBu3Di1D/H09HTmzZtHWFgYlSpVokqVKgwbNoz4+HgiIiKoWrUqu3fvxtfXl7i4OGxtbV/rmXJycihTRvM4xpEjR3jy5Anjxo3TGK76UHz69Cnx8fG4urq+lgzffPMNixYtYtGiRYwfP57evXtTu3ZtJk6cSI0aNfjiiy8YNmwYhw4dktMkJCSwbds29u3bx/PnzxkwYABDhgyRP7xfvHhBQEAAy5cvR5IkQkNDadeuHbdv38bY2FjOZ/r06cydO5clS5agq6urNjX22bNntG/fnvLly3P06FEMDQ3ZtGkTU6dO5dtvv8XFxYWLFy8SFBSEkZERAQEBpKWl0aFDB7y8vNi4cSN3795lxIgRBZ65XLlyNGzYkJiYGNq0aVMg3NDQkNmzZ3Pw4EE6duxIdnY2x48fp2zZshp1uH37drKysop9VxkZGTRu3Jjx48djYmLCgQMH6Nu3L3Xq1MHNzU2Ov27dOgYMGMCZM2c4d+4cX375JTVq1CAoKKjQ9zhjxgzmz5/PggULWL58OX369OHevXtUrFiRkJAQ4uPjOXToEObm5iQkJBT6PWptbc327duZOHEiFy5cwMfHp0DnSHEYGBgAuaOGxZWFnTt3snjxYiIiIqhfvz6PHj3i8uXLQO6IaHBwMBs2bKBFixY8ffqUmJgY+T5z5sxh48aNrFq1CltbW3799Vc+//xzKleuTMuWLeV4kydPJjQ0lMqVKzNo0CC++OILYmNjAdi0aROzZs1ixYoVuLu7ExERQWhoqFonTXHlTsWECRMIDQ2VO46CgoLIysri119/xcjIiPj4eMqXL18iXWrLpUuXuHbtGps3b9bYpuQ1LE+cOEHfvn21ytfBwYG2bduya9cuZs6cCVBsW3jp0iXatGnDF198wdKlS9HV1SUqKkrjDIzNmzczaNAgNm/eTIcOHeTrbm5u3L9/n6SkJGxsbEqmDC0oib4gtwwtXLgQW1tbJk+ejL+/PwkJCejqFm/OhoaGsnbtWtasWYOjoyOhoaHs3r2b1q1bq8Vbt24dgwcPlsvms2fPaN26NQMHDmTx4sW8fPmS8ePH07NnT44fP66WbvTo0Zw+fZpTp04RGBiIu7s7Xl5er6GZIpDeA6mpqRIgPXnyRMrKyirxIUmS9GDBculm135aHw8WfitJkvRa9xOHOMQhDnGIQxxZ0pMnTyRA+v3336XU1FT5yMjIKPBfHxAQIOno6EhGRkby0b17d43fBdu3b5cqVaokn4eHh0uAdOnSJfnavXv3JB0dHenBgwdqadu0aSNNnDix0G+OhIQEyd/fX6pcubJkZ2cnjR8/Xrp69aokSZJ05coVqUOHDoWmnTdvngRIT58+LTSOJEnSxYsXJUBKTk5Wu65JB0ZGRtKsWbPkOIA0ZcoU+fzUqVMSIP3444/ytS1btkj6+vry+bRp0yQdHR3p/v378rVDhw5JZcqUkR4+fKhRRqVSKRkbG0v79u1Tu/fIkSPV4kVFRUmAdP36dcnZ2Vnq1q2blJmZKYfXqVNH2rx5s1qab775RmrevLkkSZK0evVqqVKlStLLly/l8JUrV0qAdPHiRbV0Xbp0kQIDAzXK+/LlSykkJETy9vaW2rRpI40fP1769NNPpdOnT2uMP3jwYMnExERjWHG0b99eGjNmjHzesmVLydHRUcrJyZGvjR8/XnJ0dJTPa9asKS1evFg+z/8e09LSJEA6dOiQJEmS5OfnJ/Xv318ree7fvy/16tVLGjRokNSoUSNp0KBBUq9evdTed15U7ywlJUWSJEn6448/pBYtWkjVqlVTe3cq8peF0NBQyc7OTsrKyioQd+fOnZKJiYn0/PnzAmEZGRmSoaGhdPLkSbXrAwYMkPz9/dVkO3bsmBx+4MABCZDLSNOmTaWhQ4eq5eHu7i41aNBAPi+u3N29e1cCpCVLlqjFcXJykqZPn15Adm2pWbOmFBUVpVXcrVu3SoB04cKFIuOlpKRIgPTrr7+qXZ82bZraM+dl/PjxkoGBgSRJ2rWF/v7+kru7e6EytGzZUhoxYoT07bffSqamplJ0dHSBOCp7TVNYYbwNfanebVhYmHzt2rVrcjslSbn/GaampnJ4fl1aWVlJ8+fPl89fvXolVa9eXerUqZN8rWXLlpKLi4vavb/55hvJ29tb7drvv/8uAdLNmzfldB9//LFanCZNmkjjx48v8rleh/c64l62bNlCe06LQlIqKd+8SYmmyxs3b4KkVL7W/QQCgUAgECD/h5qYmGBiYlJs/FatWqmtkTQyMgJy1+vOmTOHGzdu8Pz5c7Kzs8nIyCA9PV2eiVeuXDmcnZ3ltHFxcSiVSuzs7NTukZmZSaVKlQqVYdSoUbi7uzNx4kTu3LnDli1baNKkCa9evcLc3JwZM2YUmlbKN5WyMFQjp/r6+gXC8usAoGLFimrneZ/TwsICACcnJ7VrGRkZPH/+XNZ7jRo1qFatmhynefPm5OTkcPPmTSwtLfnzzz+ZMmUK0dHRPH78GKVSSXp6OsnJyWr3LmyWgJeXF25ubmzduhUdHR0A/v77bxITExkwYIDayHN2djampqYAXL9+HWdnZzVdNG/eXOM9DAwM5Fkc+UlPT8fCwoLDhw/Tv39/Bg0aRFBQEKdOnVIbGVchSRIKRfFb/yqVSmbPns22bdt48OABWVlZZGZmFpgB2qxZM7X8mjdvTmhoKEqlUtZHfvK+RyMjI0xMTHj8+DEAgwcPplu3bly4cAFvb286d+5MixYtNOaTlJTEwIED8fT0xMPDg5UrV3Ls2DGSkpLU3nl+qlevjiRJpKen06BBA3bu3Em5cuWKLQs9evRgyZIl1K5dG19fX9q1a4efnx+6urp4eXlRs2ZNOczX11deDpCQkEB6enqBUcWsrCxcXFwK1Y2VlRUAjx8/pkaNGty8eZMhQ4aoxXdzc5NHNLUpdyryl+fg4GAGDx5MZGQknp6edOvWTU2W/AwaNIiNGzfK56qlInnfed5lPXkpjfaiMPKWb23awkuXLtGjR48i89yxYwePHz8mNjaWJk2aFAhXzdoorI7Cu9GXisLKkIODQ5HpUlNTefjwIU2bNpWv6erq4urqWkCGxo0bq51fvnyZqKgojbM0EhMT5XeQv0xZWVnJdb80+Ufu467Q0cG4mSv/NTPVykGdjpkp5Zs2RlFIQysQCAQCgaD0MTIyom7dumrXkpKS6NChA4MHD2bWrFlUrFiREydOMGDAALKysmQDysDAQM1wSktLQ0dHh/PnzxcwnIqa+rp+/Xp5yqWTkxOdOnUiMzOTlJQULC0ti5Rf9VF248aNQo1PyHXkBLnrmCtXrlysDvKTd1BB9cyarpXEQV9AQAB//fUXS5cupWbNmujp6dG8efMCDpdUnSn5ad++vezgTdWJoPoA/+GHH9Q+goFCjdmiePr0KXXq1NEYVrFiRYYOHap2rU6dOoXGt7Ozkz/QVR/1mliwYAFLly5lyZIlsp+FkSNHloojqvyDQwqFQn5nbdu25d69exw8eJCjR4/Spk0bhg4dysKFCwvkk99hGYCnp2ex94+JicHExEReK66iuLJgbW3NzZs3OXbsGEePHmXIkCEsWLCAX375BWNjYy5cuEB0dDSRkZFMnTqV6dOnc/bsWbk8HDhwoECHgp6eXqG6KWl5Lkm5y1+eBw4ciI+PDwcOHCAyMpI5c+YQGhpa6FZnX3/9NWPHjpXPPTw8mDdvXoH7aiJve5G/4yIvlSpVQqFQFLreXBPXr1+Xlw5o0xaqjO6icHFx4cKFC6xZswZXV9cCHV9Pnz4FKNCm5eVd6EvFm7aJ2pC//KSlpeHn56fmX0BF3namqLpfmvwjvcrnosBy6AAoZF2aTJkyufEovhdWIBAIBALB2+X8+fPk5OQQGhpKs2bNsLOz448//ig2nYuLC0qlksePH1O3bl21oygDXJPDIpXDuOLw9vbG3Nyc+fPnawxXOWurU6cOJiYmxMfHF5tnaZGcnKymt99++40yZcpgb28P5DqmCg4Opl27dtSvXx89PT01h0rFMXfuXAICAmjTpo38XBYWFlStWpU7d+4UeAcqo8LR0ZErV66QkZGhJpsmrl69qtUH+9q1a4tdY9u9e3fKlStX7LuKjY2lU6dOfP755zRo0IDatWtr3Ebq9OnTaue//fYbtra2r9VBoaJy5coEBASwceNGlixZwvfff19smpI4X6xVqxZ16tRRM9pBu7JgYGCAn58fy5YtIzo6mlOnThEXFwfkjk56enoyf/58rly5QlJSEsePH1dzHJm/PBTnSC8v9vb2nD2rPos277k25a4orK2tGTRoELt27WLMmDH88MMPhcatUqWKWv66urpUq1ZN7VphNGzYkHr16hEaGqrRaFOVwXLlylGvXj2t24sbN25w+PBhunXrBmjXFjo7O/Pzzz8XmW+dOnWIiopiz549Gjsyrl69StmyZalfv36hebwLfb0ppqamWFlZqdXp7Oxszp8/X2zaRo0ace3aNWxsbArourBOz7fJP3LEHUChUwbDhs5UmziSR9/9qHHkXcfMFMuhAzBs6IyijDDcBQKBQCB439StW5dXr16xfPly/Pz8iI2NZdWqVcWms7Ozo0+fPvTr1092PvXf//6Xn3/+GWdnZ9q3b1/qshoZGREWFkaPHj3o2LEjwcHB1K1blydPnrBt2zaSk5OJiIigTJkyeHp6cuLECTp37qyWR2ZmJo8ePVK7ptqj+U3Q19cnICCAhQsX8vz5c4KDg+nZs6f84W5ra8uGDRtwdXXl+fPn/Oc//9FqFC4vCxcuRKlU0rp1a6Kjo3FwcGDGjBkEBwdjamqKr68vmZmZnDt3jpSUFEaPHk3v3r2ZPHkyQUFBTJw4kaSkJI2jyklJSTx48ECrkWRtsLa2ZvHixQwbNoznz5/Tr18/bGxsuH//PuvXr6d8+fKEhoZia2vLjh07OHnyJBUqVGDRokX8+eef1KtXTy2/5ORkRo8ezVdffcWFCxdYvnz5G20pN3XqVBo3bkz9+vXJzMxk//79ODo6vulja0VxZWHt2rUolUqaNm2KoaEhGzduxMDAgJo1a7J//37u3LnDp59+SoUKFTh48CA5OTnY29tjbGzM2LFjGTVqFDk5OXz88cekpqYSGxuLiYmJmtO4ohg+fDhBQUG4urrSokULtm7dypUrV6hdu7Ycp7hyVxgjR46kbdu22NnZkZKSQlRU1FvTu0KhIDw8HE9PTz755BMmT56Mg4MDaWlp7Nu3j8jISHkXBR8fH06cOMHIkSPV8sjOzubRo0fk5OTw119/ER0dzcyZM2nYsCH/+c9/AO3awokTJ+Lk5MSQIUMYNGgQ5cqVk7eXy9v22NnZERUVhYeHB7q6umo7JcTExPDJJ5+UuN14G/p6U0aMGMHcuXOxtbXFwcGBRYsWadUxMHToUH744Qf8/f1lr/EJCQlEREQQFhb2Rh15r8M/eMQdFGUUGDZwovb3i7EaM5TyLdwwdK5P+RZuWI0ZSu3vF2PYwEkY7QKBQCAQfCA0aNCARYsWMW/ePD766CM2bdrEnDlztEobHh5Ov379GDNmDPb29nTu3JmzZ89So0aNtyZvp06dOHnyJGXLlqV37944ODjg7+9Pamqq7OEZcqfkRkREFBg5Onz4MFZWVmrHxx9//MZy1a1bl65du9KuXTu8vb1xdnZW8zz+448/kpKSQqNGjejbty/BwcGvtc/x4sWL6dmzJ61bt+bWrVsMHDiQsLAwwsPDcXJyomXLlqxdu1Ye+Sxfvjz79u0jLi4OFxcXJk+erHGa6ZYtW/D29i6w7/2bMGTIECIjI3nw4AFdunTBwcGBgQMHYmJiIk/nnTJlCo0aNcLHxwcPDw8sLS0LdLYA9OvXj5cvX+Lm5sbQoUMZMWIEX3755WvLVq5cOSZOnIizszOffvopOjo6b3WrrbwUVxbMzMz44YcfcHd3x9nZmWPHjrFv3z4qVaqEmZkZu3btonXr1jg6OrJq1Sq2bNkij8J+8803hISEMGfOHBwdHfH19eXAgQNajYSr6NOnDxMnTmTs2LE0atSIu3fvEhgYqLYGvLhyVxhKpZKhQ4fKstnZ2ZXYQ39JcHNz49y5c9StW5egoCAcHR3p2LEj165dUzOKBwwYwMGDB9W2cAS4du0aVlZW1KhRAw8PD7Zt28bEiROJiYlRWxJUXFtoZ2dHZGQkly9fxs3NjebNm7Nnzx6NHtjt7e05fvw4W7ZsUds7PSIioshdFEoDbfX1powZM4a+ffsSEBBA8+bNMTY2pkuXLsWmq1q1KrGxsSiVSry9vXFycmLkyJGYmZkVuhvJ20QhldQzQCnw/PlzTE1NSU1N1cq5jTZISqXaGvb85wKBQCAQCN6Mt/H//W9BkiSaNm3KqFGj8Pf3f9/ifNBkZWVha2vL5s2bNa7nFgi8vLywtLRkw4YN71uUt0aPHj1o1KgREydOfN+iFODQoUOMGTOGK1euaLXdmuDd8I8ecc9LfiNdGO0CgUAgEAjeFQqFgu+//57s7Oz3LcoHT3JyMpMmTRJGuwDI9US+aNEirl27xo0bN5g2bRrHjh3Teqr9P5UFCxa8tT3l35S///6b8PBwYbR/YPxrRtwFAoFAIBC8XcT/t0AgKG1evnyJn58fFy9eJCMjA3t7e6ZMmULXrl3ft2gCwQeF6EYRCAQCgUAgEAgE7wUDAwOOHTv2vsUQCD54/jVT5QUCgUAgEAgEAoFAIPg3Igx3gUDwwSMplUWeCwQCgUAgEAgE/2bEVHmBQPDBIuXkgCTx97lz/H3mDDnpf1PG0AgjNzeMXF1BoUDxHrbjEAgEAoFAIBAI3iXii1cgEHyQSDk5vIyL4/eRI/jvd9+SfvYMGdeukX72DP/97lt+HzmCl3FxSFJO8ZkJBALBOyAkJOSN9vrWlunTp9OwYcO3knd0dDQKhYJnz56VWp5JSUkoFAouXboEQHx8PNWrV+fvv/8utXtoy9q1azEzMysyTmBgoNre7h4eHowcObLINDY2NqW67/SHyo8//oi3t/d7uXdx72HChAkMHz783Qn0hmhTj/OXRcH/NsJwFwgEHxwqo/3PxYtQpqZqjKNMTeXPxYt4eSUud2ReIBB8UAQGBqJQKAocCQkJ71u0EnPx4kV69OiBhYUF+vr62NraEhQUxK1bt+Q4jx49YunSpUyePFm+VpgOfH1938djfDDUq1ePZs2asWjRIq3iBwYGkpSUpFXcqKgo2rVrR6VKlTA0NKRevXqMGTOGBw8eaC3f0qVLWbt2rdbx/w0kJCTQv39/qlevjp6eHrVq1cLf359z587JcTIyMggJCWHatGnytenTp8vlWldXF3Nzcz799FOWLFlCZmbmO32GsWPHsm7dOu7cufPW76WNvkqDd1UWta1jedsxU1NT3N3dOX78+FuXT5CLMNwFAsGHhyTxJOwHKM4gz8nJjffud7UUCARa4Ovry8OHD9WOWrVqlTgfpVJJznvqoNu/fz/NmjUjMzOTTZs2cf36dTZu3IipqSkhISFyvLCwMFq0aEHNmjXV0mvSwZYtW971Y2jFq1ev3tm9+vfvz8qVKwvd9/7p06d899135N21ODExkU2bNhWa5+rVq/H09MTS0pKdO3cSHx/PqlWrSE1NJTQ0VGvZTE1Nix2VLynvUrc2NjZER0drHf/cuXM0btyYW7dusXr1auLj49m9ezcODg6MGTNGjrdjxw5MTExwd3dXS1+/fn0ePnxIcnIyUVFR9OjRgzlz5tCiRQtevHhRWo9VLObm5vj4+LBy5coSpXtb+ioN3kZZVPE6dQwgPDychw8fEhsbi7m5OR06dHgnnSUfKllZWe/sXsJwFwgEHxSSUsnf584WOtKeH2VqKn+fPycc1gkEHyB6enpYWlqqHTo6OixatAgnJyeMjIywtrZmyJAhpKWlyelU05n37t1LvXr10NPTIzk5mczMTMaOHUu1atUwMjKiadOmJfrgLinp6en079+fdu3asXfvXjw9PalVqxZNmzZl4cKFrF69Wo4bERGBn5+fVjqoUKGCHK5QKFi9ejUdOnTA0NAQR0dHTp06RUJCAh4eHhgZGdGiRQsSExML5L169Wqsra0xNDSkZ8+epOZpN8+ePYuXlxfm5uaYmprSsmVLLly4oJZeoVCwcuVKOnbsiJGREbNmzdKog7Zt2+Lu7i5Pnw8LC8PR0RF9fX0cHBxYsWKFWpozZ87g4uKCvr4+rq6uXLx4sUC+Xl5ePH36lF9++UWj7vX19Xnw4AG+vr7cv3+fVatWERgYWGjHz/379wkODiY4OJg1a9bg4eGBjY0Nn376KWFhYUydOlUt/pEjR3B0dKR8+fJy54qK4qYnP378GD8/PwwMDKhVq5ZGQ6cw3e7Zs4dGjRqhr69P7dq1mTFjhlrnhUKhICwsjC5dumBoaIitrS179+4tVJY3RZIkAgMDsbW1JSYmhvbt21OnTh0aNmzItGnT2LNnjxy3sDKuq6uLpaUlVatWxcnJieHDh/PLL79w9epV5s2bJ8fTpv7Gxsbi4eGBoaEhFSpUwMfHh5SUFI2yHzhwAFNTUzX9+/n5ERER8YZaKZyS6Gv8+PHY2dlhaGhI7dq1CQkJ0diBU1Q91rRsIzg4mHHjxlGxYkUsLS2ZPn26mnzTp0+nRo0a6OnpUbVqVYKDgzU+S0nrmAozMzMsLS356KOPWLlyJS9fvuTo0aP89ddf+Pv7U61aNQwNDXFycirQSbljxw6cnJwwMDCgUqVKeHp6yktmoqOjcXNzw8jICDMzM9zd3bl3756ctjTqzt69e7G1tUVfX59WrVqxbt26AkuDTpw4wSeffIKBgQHW1tYEBwerLeuxsbHhm2++oV+/fpiYmPDll1+SlZXFsGHDsLKyQl9fn5o1azJnzpwi9fg6vFfndK9evXqnPZACgeDDp2zZsvx95myJ0qSfOUN5t6aiPREI3jKqOvb8+XO163p6eujp6WmdT5kyZVi2bBm1atXizp07DBkyhHHjxqkZgOnp6cybN4+wsDAqVapElSpVGDZsGPHx8URERFC1alV2796Nr68vcXFx2NravtYz5eTkUKYQJ5dHjhzhyZMnjBs3TmO4aiTs6dOnxMfH4+rq+loyfPPNNyxatIhFixYxfvx4evfuTe3atZk4cSI1atTgiy++YNiwYRw6dEhOk5CQwLZt29i3bx/Pnz9nwIABDBkyRDZiXrx4QUBAAMuXL0eSJEJDQ2nXrh23b9/G2NhYzmf69OnMnTuXJUuWoKurqzZy9uzZM9q3b0/58uU5evQohoaGbNq0ialTp/Ltt9/i4uLCxYsXCQoKwsjIiICAANLS0ujQoQNeXl5s3LiRu3fvMmLEiALPXK5cORo2bEhMTAxt2rQpEG5oaMjs2bM5ePAgHTt2JDs7m+PHj1O2bFmNOty+fTtZWVnFvivILVsLFy5kw4YNlClThs8//5yxY8cWO9KoIjAwkD/++IOoqCjKli1LcHAwjx8/LhAvv25jYmLo168fy5Yt45NPPiExMVH2iZB3+vmMGTOYP38+CxYsYPny5fTp04d79+5RsWJFreQrCZcuXeLatWts3rxZYz3Iq7cTJ07Qt29frfJ1cHCgbdu27Nq1i5kzZwIUW38vXbpEmzZt+OKLL1i6dCm6urpERUWh1NAxv3nzZgYNGsTmzZvp0KGDfN3NzY379++TlJSEjY1NyZShBSXRl7GxMWvXrqVq1arExcURFBSEsbGxWhktrh5rYt26dYwePZrTp09z6tQpAgMDcXd3x8vLi507d7J48WIiIiKoX78+jx494vLlyxrzKWkd04SBgQGQO+qckZFB48aNGT9+PCYmJhw4cIC+fftSp04d3NzcePjwIf7+/syfP58uXbrw4sULYmJikCSJ7OxsOnfuTFBQEFu2bCErK4szZ86gUCgASqXu3L17l+7duzNixAgGDhzIxYsXGTt2rNrzJCYm4uvry8yZM1mzZg3//e9/GTZsGMOGDSM8PFyOt3DhQqZOnSrfe9myZezdu5dt27ZRo0YNfv/9d37//Xet9agt79Vwj4yMxNDQ8H2KIBAIPiAMDAzw9vYmJ71kDouUf6cDuWsbX758+TZEEwgE5Bo8ANbW1mrXp02bpjbqo2L//v2UL19ePm/bti3bt29XczBlY2PDzJkzGTRokJrh/urVK1asWEGDBg0ASE5OJjw8nOTkZKpWrQrkrmk9fPgw4eHhzJ49W6PMiYmJhISEcOzYMSpUqECXLl3o27cv9evXJy4ujkmTJrFv3z6NaW/fvg3kGiFFkZycjCRJslxF6QBg0qRJTJo0ST7v378/PXv2BHJH6Zo3b05ISAg+Pj4AjBgxgv79+6vlkZGRwfr166lWrRoAy5cvp3379oSGhmJpaUnr1q3V4n///feYmZnxyy+/qBk6vXv3VstbZbg/evSIXr16YWtry+bNmylXrhyQ+65DQ0Pp2rUrALVq1SI+Pp7Vq1cTEBDA5s2bycnJ4ccff0RfX5/69etz//59Bg8eXEA3VatWVRtRy/98s2fP5vTp03h4eODq6oqnpycLFizAzc2tQPzbt29jYmKClZWVxvzy8urVK1atWkWdOnWAXIPy66+/LjYdwK1btzh06BBnzpyhSZMmQK7DNkdHxwJx8+v2iy++YMKECQQEBABQu3ZtvvnmG8aNG6dmfAQGBuLv7w/A7NmzWbZsGWfOnHkrvhG0LePPnj0jNTVVYxkvDAcHByIjIwHt6u/8+fNxdXVVawfq169fIN/vvvuOyZMns2/fPlq2bKkWpsr73r17b8Vw11ZfAFOmTJF/29jYMHbsWCIiItQM9+LqsSacnZ3l8mJra8u3337Lzz//jJeXF8nJyVhaWuLp6UnZsmWpUaOGxvqiundJ6lh+0tPTmTJlCjo6OrRs2ZJq1aqpGcLDhw/nyJEjbNu2TTbcs7Oz6dq1q7ykyMnJCcjt/ExNTaVDhw5yvcxbp2bMmPHGdWf16tXY29uzYMECAOzt7bl69araTKM5c+bQp08f+T/K1taWZcuW0bJlS1auXIm+vj4ArVu3VlsWkZycjK2tLR9//DEKhaLAkqnS4r0a7t7e3piYmLxPEQQCwQdIGUOjEsXXMcrtAGzVqtXbEEcgEPx/VCPtv//+u9r/d2Gj7a1atVJbb2pklFu3jx07xpw5c7hx4wbPnz8nOzubjIwM0tPT5Q79cuXK4ezsLKeNi4tDqVRiZ2endo/MzEwqVapUqMyjRo3C3d2diRMncufOHbZs2UKTJk149eoV5ubmzJgxo9C0kpb+M1QdhqqPurzk1wFQYOQ073NaWFgA//dBq7qWkZHB8+fPZb3XqFFD/tgHaN68OTk5Ody8eRNLS0v+/PNPpkyZQnR0NI8fP0apVJKenk5ycrLavQubJeDl5YWbmxtbt25FR0cHgL///pvExEQGDBhAUFCQHDc7OxtTU1MArl+/jrOzs5oumjdvrvEeBgYGcmdQftLT07GwsODw4cP079+fQYMGERQUxKlTpzQaFZIkyaNzxWFoaCgbBwBWVlYaR8w1cf36dXR1dWncuLF8zcHBQeM65Py6vXz5MrGxsWqGglKpLFD285YHIyMjTExMipRv0KBBbNy4UT5XLW9QvTdAbSlKXkqjjBdG3neiTf29dOkSPXr0KDLPHTt28PjxY2JjY+WOk7yoRoALK1fwbvQFsHXrVpYtW0ZiYiJpaWlkZ2cXsHuKq8eayFs+QL389ujRgyVLllC7dm18fX1p164dfn5+6OoWNPlKWsdU+Pv7o6Ojw8uXL6lcuTI//vgjzs7OKJVKZs+ezbZt23jw4AFZWVlkZmbK5bpBgwa0adMGJycnfHx88Pb2pnv37lSoUIGKFSsSGBiIj48PXl5eeHp60rNnT7kjrjTqzs2bNwuUmfzPefnyZa5cuaI240GSJHJycrh7967cmZC/bgcGBuLl5YW9vT2+vr506NDhrey+8F4N97Jly5ZoOoZAIPj3IymVGLm5kX72jNZpDN3ckJRK0Z4IBG8ZVR0zMTHRquPdyMiIunXrql1LSkqiQ4cODB48mFmzZlGxYkVOnDjBgAEDyMrKkj/ADAwM1AyxtLQ0dHR0OH/+vNoHNlBgRDsv69evl40qJycnOnXqRGZmJikpKYV+GKtQGRk3btwo1PiEXKdYACkpKVSuXLlYHeQnb9ulemZN10rioC8gIIC//vqLpUuXUrNmTfT09GjevHkBR0qqzpT8tG/fXnbwpupEUBkzP/zwA02bNlWLn/+daMPTp0/VDOi8VKxYkaFDh6pdq1OnTqHx7ezsSE1N5eHDh8WOuuf/r1AoFCUyyLQlv27T0tKYMWOGPFshL3kNYk3yFfXuv/76a7WRTg8PD+bNm1fgHWkibxl3cXEpNF6lSpVQKBSFrjfXxPXr1+X10trUX5XRXRQuLi5cuHCBNWvW4OrqWqCz5unTpwAF6mFe3oW+Tp06RZ8+fZgxYwY+Pj6YmpoSERFRIieJhVFU+bC2tubmzZscO3aMo0ePMmTIEBYsWMAvv/xSIF1J65iKxYsX4+npiampqZqeFyxYwNKlS1myZInsw2TkyJFym6Ojo8PRo0c5efIkkZGRLF++nMmTJ3P69Glq1apFeHg4wcHBHD58mK1btzJlyhSOHj1Ks2bN3lrdyU9aWhpfffWVRr8ANWrUkH/nr9uNGjXi7t27HDp0iGPHjtGzZ088PT3ZsWOH1vfWhvdquAsEAkF+FDo6GLm68tTUVCsHdTqmphg1dkXxGh+NAoHg3XP+/HlycnIIDQ2V14hu27at2HQuLi4olUoeP37MJ598ovX9NI2EqhzGFYe3tzfm5ubMnz+f3bt3Fwh/9uwZZmZm1KlTBxMTE+Lj4wuMKL4tkpOT+eOPP+Spwb/99htlypTB3t4eyHXytWLFCtq1awfkzpJ48uSJ1vnPnTuX8uXL06ZNG6Kjo6lXrx4WFhZUrVqVO3fu0KdPH43pHB0d2bBhAxkZGfIH9W+//aYx7tWrV+nevXuxsmizHVb37t2ZMGEC8+fPZ/HixQXCVe/qTXFwcCA7O5vz58/Lo3c3b97Uat/7Ro0acfPmzWI7ckpKlSpVqFKlinyuq6tLtWrVtLpPw4YNqVevHqGhofTq1avAum2V3sqVK0e9evWIj4/XaiTxxo0bHD58mIkTJwLa1V9nZ2d+/vnnImfB1KlTh9DQUDw8PNDR0eHbb79VC7969Sply5bVOMVexbvQ18mTJ6lZs6ba9pCaloUUV49fBwMDA/z8/PDz82Po0KE4ODgQFxdHo0aNCk1Tki3nLC0tNeoqNjaWTp068fnnnwO5HY23bt2iXr16chyFQoG7uzvu7u5MnTqVmjVrsnv3bkaPHg3klhMXFxcmTpxI8+bN2bx5M82aNSuVumNvb8/BgwfVrp09q+5TqVGjRsTHx7/WfUxMTOjVqxe9evWie/fu+Pr68vTp01L1TSG8ygsEgg8PhQLzgUFQiMMomTJlcuNpOT1SIBC8f+rWrcurV69Yvnw5d+7cYcOGDaxatarYdHZ2dvTp04d+/fqxa9cu7t69y5kzZ5gzZw4HDhx4K7IaGRkRFhbGgQMH6NixI8eOHSMpKYlz584xbtw4Bg0aBOQ62/P09OTEiRMF8sjMzOTRo0dqR0kM6MLQ19cnICCAy5cvExMTQ3BwMD179pQ7JGxtbdmwYQPXr1/n9OnT9OnTR6sRzbwsXLiQPn360Lp1a27cuAHkrjWdM2cOy5Yt49atW8TFxREeHi7vyd67d28UCgVBQUHEx8dz8OBBFi5cWCDvpKQkHjx4gKen5xtqIhdra2sWL17M0qVLGTBgAL/88gv37t0jNjaWr776im+++aZU7qOaCvvVV19x+vRpzp8/z8CBA7XS7dSpU1m/fj0zZszg2rVrXL9+nYiICLW10O8ahUJBeHg4t27d4pNPPuHgwYPcuXOHK1euMGvWLDp16iTH9fHx0VjGs7OzefToEX/88QdxcXEsX76cli1b0rBhQ/7zn/8A2tXfiRMncvbsWYYMGcKVK1e4ceMGK1euLFBf7OzsiIqKYufOnWr+MiDXiZnKI/jbQFt92drakpycTEREBImJiSxbtkxj519x9bikrF27lh9//JGrV69y584dNm7ciIGBwVtbc50XW1tbeUT9+vXrfPXVV/z5559y+OnTp5k9ezbnzp0jOTmZXbt28d///hdHR0fu3r3LxIkTOXXqFPfu3SMyMpLbt2/LU9NLo+589dVX3Lhxg/Hjx3Pr1i22bdsmd1ioZm6MHz+ekydPMmzYMC5dusTt27fZs2cPw4YNKzLvRYsWsWXLFm7cuMGtW7fYvn07lpaWpb6VnzDcBQLBB4eiTBkMnJ2wGDUanf+/bjI/OqamWIwajYGzE4riDHyBQPDB0KBBAxYtWsS8efP46KOP2LRpk9bb5oSHh9OvXz/GjBmDvb09nTt35uzZs2pTGEubTp06cfLkScqWLUvv3r1xcHDA39+f1NRU2Vs2wMCBA4mIiCgwLfPw4cNYWVmpHR9//PEby1W3bl26du1Ku3bt8Pb2xtnZWc2p148//khKSgqNGjWib9++BAcHq40yasvixYvp2bMnrVu35tatWwwcOJCwsDDCw8NxcnKiZcuWrF27Vp4SXb58efbt20dcXBwuLi5MnjxZbUswFVu2bMHb27tUDYohQ4YQGRnJgwcP6NKlCw4ODgwcOBATE5MC3qPfhPDwcKpWrUrLli3p2rUrX375pVa69fHxYf/+/URGRtKkSROaNWvG4sWL34lRVRRubm6cO3eOunXrEhQUhKOjIx07duTatWssWbJEjjdgwAAOHjyotl0ZwLVr17CysqJGjRp4eHiwbds2Jk6cSExMjNoyluLqr52dHZGRkVy+fBk3NzeaN2/Onj17NK7Ptre35/jx42zZskXNSVhERISa/4W3gTb66tixI6NGjWLYsGE0bNiQkydPEhISUiCv4upxSTEzM+OHH37A3d0dZ2dnjh07xr59+4r0A1JaTJkyhUaNGuHj44OHhweWlpZqW9mZmJjw66+/0q5dO+zs7JgyZQqhoaG0bdsWQ0NDbty4Qbdu3bCzs+PLL79k6NChfPXVV0Dp1J1atWqxY8cOdu3ahbOzMytXrpRnRKj8tDg7O/PLL7/IHTMuLi5MnTq1WKeMxsbGsnPFJk2akJSUxMGDBwvdseR1UUhvY1FPMTx//hxTU1NSU1OFczqBQFAoUk4OSBJ/nz9H+pkzKP9OR8fIEEM3N4wau4JCIYx2geAdIv6/C0eSJJo2bcqoUaNkr8YCzWRlZcne6t3d3d+3OIIS0KNHDxo1aiRPgf+QOHToEGPGjOHKlSsajX2BID+zZs1i1apVb2XrtreBKNUCgeCDRWWUGzV2pbzb/zmNkZRKsaZdIBB8UCgUCr7//nvi4uLetygfPMnJyUyaNEkY7f9AFixYUOj2ie+bv//+m/DwcGG0CwplxYoVNGnShEqVKhEbG8uCBQuKnQb/ISFG3AUCgUAgEGiF+P8WCAQCwT+VUaNGsXXrVp4+fUqNGjXo27cvEydO/Md09gjDXSAQCAQCgVaI/2+BQCAQCN4PYnGoQCAQCAQCgUAgEAgEHzDCcBcI/uVISmWR5wKBQCAQCAQCgeDD5p8xoV8gEJQYlUf2lxfP8fLiGXLS/6aMoREGLm4YuAiP7AKBQCAQCAQCwT8F8dUuEPwLkaQcMuLjeDhpBE9//JaXF86QeeMaLy+c4emP3/Jw0ggy4uOQpJziMxMIBAKBVoSEhPDll1++l3vb2Nio7bmdn88++4zQ0NB3J1AJiI6ORqFQ8OzZs0LjrF27FjMzs3cmk+DfR1ZWFnXr1uXkyZNv/V7F1cc3ITAwUG1/9NJg+vTpNGzYUD6fMGECw4cPL9V7vC5JSUkoFAouXbpUaJz87UP+59HE29Dj20YY7gLBvwwpJ4eMa3H8tXIROc9TNcbJeZ7KXysXkXEtLndkXiAQCEqZwMBAFApFgSMhIeF9i1ZiLl68SI8ePbCwsEBfXx9bW1uCgoK4deuWHOfRo0csXbqUyZMny9fy6qBs2bJYWFjg5eXFmjVryHnHbe+UKVOYNWsWqama/xfykpSURGBgoFb5Pn/+nMmTJ+Pg4IC+vj6WlpZ4enqya9cuStP/ca9evdT0/bZYu3Yta9euLTaeh4eH/G719fWpV68eK1aseOvyCQoiSRLff/89TZs2pXz58piZmeHq6sqSJUtIT0+X461atYpatWrRokUL+ZqmNkqhUBAREfE+HuWDYezYsaxbt447d+689XslJCTQv39/qlevjp6eHrVq1cLf359z585pnce7ah/eN8JwFwj+bUgSKRt+gOI+CnNycuO9+40lBALB/wi+vr48fPhQ7ahVq1aJ81Eqle/c0FWxf/9+mjVrRmZmJps2beL69ets3LgRU1NTQkJC5HhhYWG0aNGCmjVrqqVX6SApKYlDhw7RqlUrRowYQYcOHcjOzn5nz/HRRx9Rp04dNm7cWGicTZs2kZiYKJ9LksR3331HSkqKxvjPnj2jRYsWrF+/nokTJ3LhwgV+/fVXevXqxbhx47TqJNAWAwMDqlSpUmr55Wfx4sW8ePFCPn/x4gWLFy8uMk1QUBAPHz4kPj6enj17MnToULZs2fLWZPzQycrKeuM81q5di4eHR4nS9O3bl5EjR9KpUyeioqK4dOkSISEh7Nmzh8jISCC3LH/77bcMGDCgQPrw8PAC7dSHOBL7LttBc3NzfHx8WLlyZYnS2djYEB0drXX8c+fO0bhxY27dusXq1auJj49n9+7dODg4MGbMGK3zeRvtQ2mU59JGGO4Cwb8ISank5cWzhY605yfneSovL50TDusEAsFbQU9PD0tLS7VDR0eHRYsW4eTkhJGREdbW1gwZMoS0tDQ5nWra4969e6lXrx56enokJyeTmZnJ2LFjqVatGkZGRjRt2rREH4klJT09nf79+9OuXTv27t2Lp6cntWrVomnTpixcuJDVq1fLcSMiIvDz8ytUB9WqVaNRo0ZMmjSJPXv2cOjQIbWR3WfPnjFw4EAqV66MiYkJrVu35vLly2p57du3jyZNmqCvr4+5uTldunQpVPawsDDMzMz4+eef5Wt+fn5FjiTWqlWLgIAAVq1axf379/H19eXBgwfo6elpjD9p0iSSkpI4ffo0AQEB1KtXDzs7O4KCgrh06RLly5cHYMOGDbi6umJsbIylpSW9e/fm8ePHBfKLjY3F2dkZfX19mjVrxtWrV+WwwqbCbtiwARsbG0xNTfnss8/UjO8dO3bg5OSEgYEBlSpVwtPTk7///lvjs1SoUAEvLy9OnDjBiRMn8PLyokKFCoXqCsDQ0BBLS0tq167N9OnTsbW1Ze/evQCMHz8eOzs7DA0NqV27NiEhIbx69UpOe/nyZVq1aoWxsTEmJiY0btxYHmG8d+8efn5+VKhQASMjI+rXr8/BgwfltFevXqVt27aUL18eCwsL+vbty5MnT+RwDw8PgoODGTduHBUrVsTS0pLp06eryX7jxg0+/vhjebbAsWPHUCgU/PTTT3Kc33//nZ49e2JmZkbFihXp1KkTSUlJcrhqqvGsWbOoWrUq9vb2AKxYsQJbW1v09fWxsLCge/fuRerxTdi2bRubNm1iy5YtTJo0iSZNmmBjY0OnTp04fvw4rVq1AuD8+fMkJibSvn37AnmYmZkVaKf09fWB/yt3+/fvx97eHkNDQ7p37056ejrr1q3DxsaGChUqEBwcjDLft9SLFy/w9/fHyMiIatWq8d1336mFv247mJ+zZ89SuXJl5s2bB2jXlsydOxcLCwuMjY0ZMGAAGRkZBfItrr14UyRJIjAwEFtbW2JiYmjfvj116tShYcOGTJs2jT179qjFv3PnDq1atcLQ0JAGDRpw6tQpOay4pTRKpZLRo0djZmZGpUqVGDduXIEZQR4eHgwbNoyRI0fKHRdQOvWttHivzulevXql1ogJBII3o2zZsry8eLZEaV5eOINh46aiLgoEgmJRtRPPnz9Xu66np1eocaeJMmXKsGzZMmrVqsWdO3cYMmQI48aNU5tqnJ6ezrx58wgLC6NSpUpUqVKFYcOGER8fT0REBFWrVmX37t34+voSFxeHra3taz1TTk4OZQpx1HnkyBGePHnCuHHjNIarPhSfPn1KfHw8rq6uWt2zdevWNGjQgF27djFw4EAAevTogYGBAYcOHcLU1JTVq1fTpk0bbt26RcWKFTlw4ABdunRh8uTJrF+/nqysLDVjLi/z589n/vz5REZG4ubmJl93c3Nj1qxZZGZmanxfLVq0ICoqCk9PT2JjY9m3bx9t27bVeI+cnBwiIiLo06cPVatWLRCuMtoht9x888032Nvb8/jxY0aPHk1gYGAB+f/zn/+wdOlSLC0tmTRpEn5+fty6dYuyZctqlCExMZGffvqJ/fv3k5KSQs+ePZk7dy6zZs3i4cOH+Pv7M3/+fLp06cKLFy+IiYkpdPp+YGAgrVu3lvV15swZatSooTFuYRgYGMijdMbGxqxdu5aqVasSFxdHUFAQxsbGclnq06cPLi4urFy5Eh0dHS5duiQ/59ChQ8nKyuLXX3/FyMiI+Ph4WZ/Pnj2jdevWDBw4kMWLF/Py5UvGjx9Pz549OX78uCzLunXrGD16NKdPn+bUqVMEBgbi7u6Ol5cXSqWSzp07U6NGDU6fPs2LFy8KjG6+evUKHx8fmjdvTkxMDLq6usycORNfX1+uXLlCuXLlAPj5558xMTHh6NGjQO4IanBwMBs2bKBFixY8ffqUmJiYEumxJGzatAl7e3s6depUIEyhUGBqagpATEwMdnZ2GBsbl/ge6enpLFu2jIiICF68eEHXrl3p0qULZmZmHDx4kDt37tCtWzfc3d3p1auXnG7BggVMmjSJGTNmcOTIEUaMGIGdnR1eXl7A67eDeTl+/Dhdu3Zl/vz5sn+N4tqSbdu2MX36dL777js+/vhjNmzYwLJly6hdu7Za3m5ubty/f5+kpCRsbGxKrLfiuHTpEteuXWPz5s0a2+D8hvjkyZNZuHAhtra2TJ48GX9/fxISEtDVLd6cDQ0NZe3ataxZswZHR0dCQ0PZvXs3rVu3Vou3bt06Bg8eTGxsLFA69a00ea+Ge2RkJIaGhu9TBIHgX4OBgQHe3t7kpGseTSiMnP+//isqKoqXL1++DdEEAsG/BNV6UWtra7Xr06ZN0zjCsH//fjUDrm3btmzfvp2RI0fK12xsbJg5cyaDBg1S+2B99eoVK1asoEGDBgAkJycTHh5OcnKybCiOHTuWw4cPEx4ezuzZszXKnJiYSEhICMeOHaNChQp06dKFvn37Ur9+feLi4pg0aRL79u3TmPb27dsAODg4FKmX5ORkJEnSaMAWhoODA1euXAHgxIkTnDlzhsePH8sG9cKFC/npp5/YsWMHX375JbNmzeKzzz5jxowZch4q3eRl/PjxbNiwgV9++YX69eurhVWtWpWsrCwePXpUYEo/wOnTp/nPf/5DixYtKFu2LEuWLOHUqVNMmjRJHoFU8eTJE1JSUorVDcAXX3wh/65duzbLli2jSZMmpKWlqZWPadOmyR+669ato3r16uzevZuePXtqzDcnJ4e1a9fKxljfvn35+eefZcM9Ozubrl27ys/q5ORUqIwbN27k22+/lUdke/bsybBhw/j888+LfT6lUsmWLVu4cuWKbDxNmTJFDrexsWHs2LFERETIhntycjL/+c9/ZP3l7XhKTk6mW7dusrx5Dapvv/0WFxcXtfK+Zs0arK2tuXXrFnZ2dgA4Ozszbdo0Oe9vv/2Wn3/+GS8vL44ePUpiYiLR0dFYWloCMGvWLDUjY+vWreTk5BAWFoZCoQByp5SbmZkRHR2Nt7c3AEZGRoSFhcmG/K5duzAyMqJDhw4YGxtTs2ZNXFxcitXh63L79m15pL8o7t27V2j99Pf3R0dHR+1afHy83HHz6tUrVq5cSZ06dQDo3r07GzZs4M8//6R8+fLUq1ePVq1aERUVpWa4u7u7M2HCBADs7OyIjY1l8eLFsp5fpx3My+7du+nXrx9hYWHyfbVpS5YsWcKAAQPkZQMzZ87k2LFjBUbdVfq6d+/eWzHctW1fVYwdO1aunzNmzKB+/fokJCRolX7JkiVMnDiRrl27Arn+Do4cOVIgnq2tLfPnz5fPZ86c+cb1rTR5r4a7t7c3JiYm71MEgeBfRxlDoxLGz+08U00nEwgEgsJQjbT//vvvav/fhY22t2rVSm2NpJFRbvt07Ngx5syZw40bN3j+/DnZ2dlkZGSQnp4ud+iXK1cOZ2dnOW1cXBxKpVL+UFKRmZlJpUqVCpV51KhRuLu7M3HiRO7cucOWLVto0qQJr169wtzcXM0Qzo+2ztVUnZ75jduikCRJNoguX75MWlpaged4+fKlvOb80qVLBAUFFZlnaGgof//9N+fOnSswega5HbyAmsOuvNy+fZvw8HB0dHSYPn064eHhrFixgvT09ALPVhLHc+fPn2f69OlcvnyZlJQUeZ1ucnIy9erVk+M1b95c/l2xYkXs7e25fv16ofna2NiojaBaWVnJU/AbNGhAmzZtcHJywsfHB29vb7p3717o9PfHjx9z9OhRdu7cCeR+6P/www9FPteKFSsICwsjKysLHR0dRo0axeDBg4Fcw3fZsmUkJiaSlpZGdna2Wp0ZPXo0AwcOZMOGDXh6etKjRw/ZMAwODmbw4MFERkbi6elJt27d5Lpw+fJloqKi1Do8VCQmJqoZEnnJq5ubN29ibW0tG+2A2swM1X0SEhIKjFBnZGSo+UFwcnKSjXYALy8vatasSe3atfH19cXX15cuXboUOlCXvwxkZ2fz6tUrteebNGkSkyZN0pi+JHW0sPq5ePFiPD091a7lNfINDQ3ldwNgYWGBjY2NmowWFhYFln/kLc+q87ye5l+nHVRx+vRp9u/fz44dO9TW42vTlly/fp1BgwYVkC0qKkrtWnHtBcCgQYPU/Gakp6fTtm1btY6QvNP/81JS55V59WBlZQXk1tviDPfU1FQePnxI06ZN5Wu6urq4uroWkKFx48Zq56VR30qT92q4ly1bttDpTwKBoORISiUGLm68vHBG6zQGjdyQlEpRFwUCQbGo2gkTExOtOt6NjIyoW7eu2rWkpCQ6dOjA4MGDmTVrFhUrVuTEiRMMGDCArKws+YPVwMBANmwh9+NPR0eH8+fPFxgd0/RRpWL9+vXylEsnJyc6depEZmYmKSkpaoaLJlQfZTdu3CjwEZ4Xc3NzAFJSUqhcuXKReaq4fv267KgvLS0NKysrjev1VbKrPqKL4pNPPuHAgQNs27ZNHunLy9OnTwEKlVE1uqxax6xQKBg6dKjGuJUrV8bMzIwbN24UKdPff/+Nj48PPj4+bNq0icqVK5OcnIyPj88bO3/K/7+lUCjkTgEdHR2OHj3KyZMniYyMZPny5UyePJnTp09rdJA4evRotXNjY+MC1/LTp08fJk+ejIGBAVZWVvJ031OnTtGnTx9mzJiBj48PpqamREREqG3HN336dHr37s2BAwc4dOgQ06ZNIyIigi5dujBw4EB8fHw4cOAAkZGRzJkzh9DQUIYPH05aWhp+fn7yeua8qIyZ4nSjDWlpaTRu3JhNmzYVCMtbflSdcSqMjY25cOEC0dHRREZGMnXqVKZPn87Zs2c1rkGuWrWq2jZfu3btYufOnWr3rVixYqFy2tnZFVsGIbeOxsXFaQyztLQs0E7lRZMu31S/r9sOqqhTpw6VKlVizZo1tG/fXpZHm7ZEW4prLwC+/vprxo4dK597eHgwb948NSO5MPK2r9rMysirc5VOSttZX/7y/K7qm7YI53QCwb8IhY4OBi6ulDEx1Sp+GRNTDBq6osj3ESwQCARvi/Pnz5OTk0NoaCjNmjXDzs6OP/74o9h0Li4uKJVKHj9+TN26ddWOogxwTR+rKodxxeHt7Y25ubna1Mm8qPYdr1OnDiYmJsTHxxebJ+SuS42Li6Nbt24ANGrUiEePHqGrq1vg2VSdAs7OzmqO5jTh5ubGoUOHmD17NgsXLiwQfvXqVapXry7nWRg2NjbFbolWpkwZPvvsMzZt2qTx/alGmW/cuMFff/3F3Llz+eSTT3BwcCh0JOq3336Tf6ekpHDr1i0cHR2LlKMoFAoF7u7uzJgxg4sXL1KuXDl2795dZJrAwECtt8IzNTWlbt26VKtWTW2N7smTJ6lZsyaTJ0/G1dUVW1tb7t27VyC9nZ0do0aNIjIykq5duxIeHi6HWVtbM2jQIHbt2sWYMWPk0f9GjRpx7do1bGxsCpSV/EZHYdjb2/P777/z559/ytfOnlX3j9OoUSNu375NlSpVCtxHtW68MHR1dfH09GT+/PlcuXKFpKQktfXA+ePmzbtKlSoYGBioXSvKcO/duze3bt0q4MgMckd0VTsbuLi4cOPGjVLdorA48pZn1bmqPL9uO6jC3Nyc48ePk5CQQM+ePWX/I9q0JY6Ojpw+fbpIWSG3vShbtmyBJTd5yV8+dHV1qVatmtq1wmjYsCH16tUjNDRUo5Gral/fFFNTU6ysrNSeOTs7m/PnzxebtjTqW2kiDHeB4N+GQkGFvkFQiLMlmTJlcuNp6MkVCASCt0XdunV59eoVy5cv586dO2zYsIFVq1YVm87Ozo4+ffrQr18/du3axd27dzlz5gxz5szhwIEDb0VW1frdAwcO0LFjR44dO0ZSUhLnzp1j3Lhx8nTTMmXK4OnpyYkTJwrkkZmZyaNHj3jw4AEXLlxg9uzZdOrUiQ4dOtCvXz8APD09ad68OZ07dyYyMpKkpCROnjzJ5MmTZU/j06ZNY8uWLUybNo3r168TFxencRSoRYsWHDx4kBkzZqhNy4VcB12qtcmlwaxZs7C2tqZp06asX7+e+Ph4bt++zZo1a3BxcSEtLY0aNWpQrlw5+X3v3buXb775RmN+X3/9NT///DNXr14lMDAQc3Pz196W6/Tp08yePZtz586RnJzMrl27+O9///tGHQHaYmtrS3JyMhERESQmJrJs2TK1DoOXL18ybNgwoqOjuXfvHrGxsZw9e1aWbeTIkRw5coS7d+9y4cIFoqKi5LChQ4fy9OlT/P39OXv2LImJiRw5coT+/fsX8GpeGF5eXtSpU4eAgACuXLlCbGysvCZfNZLZp08fzM3N6dSpEzExMdy9e5fo6GiCg4O5f/9+oXnv37+fZcuWcenSJe7du8f69evJycnRah3669CzZ0969eqFv7+//L7v3bvH/v378fT0lKd/t2rVirS0NK5du1Ygj2fPnvHo0SO1o7DdB0pCbGws8+fP59atW3z33Xds376dESNGAK/fDualSpUqHD9+nBs3buDv7092drZWbcmIESNYs2YN4eHh3Lp1i2nTpmnUS0xMDJ988olWs31eB4VCIcvwySefyI7+rly5wqxZszQ6HHxdRowYwdy5c/npp5+4ceMGQ4YM0apjoDTqW2kiDHeB4F+GokwZ9Os7UWnw6EJH3suYmFJp8Gj06zuhKM7AFwgEglKkQYMGLFq0iHnz5vHRRx+xadMm5syZo1Xa8PBw+vXrx5gxY7C3t6dz586cPXu2xN6/S0KnTp04efIkZcuWpXfv3jg4OODv709qaiozZ86U4w0cOJCIiIgCI0eHDx/GysoKGxsbfH19iYqKYtmyZezZs0ee8q9QKDh48CCffvop/fv3x87Ojs8++4x79+5hYWEB5E5B3b59O3v37qVhw4a0bt2aM2c0L4v6+OOPOXDgAFOmTGH58uVA7trkn376qdh18iWhYsWK/Pbbb3z++eeyE6dPPvmELVu2sGDBAkxNTalcuTJr165l+/bt1KtXj7lz52qcDQC5W1SNGDGCxo0b8+jRI/bt26e2frokmJiY8Ouvv9KuXTvs7OyYMmUKoaGhhXrJL006duzIqFGjGDZsGA0bNuTkyZOEhITI4To6Ovz111/069cPOzs7evbsSdu2bWV/C0qlkqFDh+Lo6Iivry92dnayw7KqVasSGxuLUqnE29sbJycnRo4ciZmZWaG7I+RHR0eHn376ibS0NJo0acLAgQOZPHky8H9+GgwNDfn111+pUaMGXbt2xdHRUd42rKhlMmZmZuzatYvWrVvj6OjIqlWr2LJlS5Gjtm+CQqFg8+bNLFq0iJ9++omWLVvi7OzM9OnT6dSpk7ylV6VKlejSpYvGqf/9+/fHyspK7VDVmzdhzJgxnDt3DhcXF2bOnMmiRYtked6kHcyLpaWlPIOnT58+5OTkFNuW9OrVi5CQEMaNG0fjxo25d++e7JshLxEREaXaXmjCzc2Nc+fOUbduXYKCgnB0dKRjx45cu3atQMfjmzBmzBj69u1LQEAAzZs3x9jYuMjtNFWURn0rTRTSu5wz8v95/vw5pqampKamCud0AsFbQsrJAUni5aVzvLxwhpz0dMoYGmLQyA2Dhq6gUAijXSAQlAjx/104kiTRtGlTRo0ahb+///sWpwArV65k9+7dREZGvm9RBB8gsbGxfPzxxyQkJKg5Yvs3ceXKFby8vEhMTCzSL4YADh06xJgxY7hy5YpW260J3g3iTQgE/1JURrlBQ1cMG/+fkxBJqRRr2gUCgaCUUSgUfP/994U6wHrflC1btlRGEQX/Dnbv3k358uWxtbUlISGBESNG4O7u/q812iHXT8S8efO4e/dukVsDCnKdSoaHhwuj/QNDjLgLBAKBQCDQCvH/LRD8O1i/fj0zZ84kOTkZc3NzPD09CQ0NLXJrRYFA8H4RhrtAIBAIBAKtEP/fAoFAIBC8H8QCV4FAIBAIBAKBQCAQCD5ghOEuEAgEAoFAIBAIBALBB4ww3AUCwQePlG+vzPznAoFAIBAIBALBvxnhKlAgEHywSDk5gETG1fNkXDlLzst0yhgYou/cBH2nxoDY0k4gEAgEAoFA8O9HfPEKBIIPEkmSyLx1lcczR/Ns00oy4s6RlRBPRtw5nm1ayeOZo8m8dZX34F9TIBAINBISEsKXX375Xu5tY2PDkiVLCg3/7LPPCA0NfXcC5aE42ZKSklAoFFy6dAmA6OhoFAoFz549KzTN2rVrMTMzK1U5P0SysrKoW7cuJ0+efOv3Ku49vQmBgYF07ty5VPOcPn06DRs2lM8nTJjA8OHDS/Ueb5P88mvibehN8M9FGO4CgeCDQ8rJIfNmHClrl5KT9lxjnJy056SsXUrmzbj/PzIvEAg+JAIDA1EoFAWOhISE9y1aibl48SI9evTAwsICfX19bG1tCQoK4tatW3KcR48esXTpUiZPnixfy6uDsmXLYmFhgZeXF2vWrCHnHbdbU6ZMYdasWaSmphYbNykpicDAQK3yff78OZMnT8bBwQF9fX0sLS3x9PRk165dWnesWltb8/DhQz766COt4v8bkCSJ77//nqZNm1K+fHnMzMxwdXVlyZIlpKeny/FWrVpFrVq1aNGihXxNU71SKBRERES8j0f5YBg7dizr1q3jzp07b/1eCQkJ9O/fn+rVq6Onp0etWrXw9/fn3LlzpXqfpUuXsnbt2lLNUxOBgYEkJSUVGy9veTM1NcXd3Z3jx4+/dfkEuQjDXSAQfIBIpG77EYr7sM3JIXX7GkCMugsEHyK+vr48fPhQ7ahVq1aJ81Eqle/c0FWxf/9+mjVrRmZmJps2beL69ets3LgRU1NTQkJC5HhhYWG0aNGCmjVrqqVX6SApKYlDhw7RqlUrRowYQYcOHcjOzn5nz/HRRx9Rp04dNm7cWGicTZs2kZiYKJ9L/4+98wyr4ngf9n1AuhQFpBiKBRAVEGMndhCsiBqNGlssUTSo0dhrFI0Fa+xGjIoSjRoNNkQx1tgLEURQATUaY6yIFGHfD7xn/xzPAQ72XzL3de2H3Sn7zOzM7D4zzzwrSSxZsoSHDx9qjP/o0SMaNGjAunXrGDt2LOfOnePw4cN06dKFUaNGaTVJAKCrq4utrS2lSr25HZzZ2dlvLK/iWLt2LU2aNClRmh49ejBs2DACAwOJjY3lwoULTJw4kR07dhAdHQ3k1//3339P37591dKHh4er9a0PcWX2XfZdKysr/P39WbZsWYnSOTs7c+jQIa3jnzlzho8//pirV6+yYsUK4uPj2b59O1WqVGHEiBEllLpozM3N35plyYMHD1iyZInKBNu1a9eIiIgoMp2y7R07dgwrKyvatGnzTiZLPlTe5VgjFHeBQPBBIeXmkhl3ttCV9pfJe/qYzLhzwmGdQPABYmBggK2trcqhq6vLvHnz8PDwwMTEBAcHB4KDg0lPT5fTKc2gd+7cSdWqVTEwMCAtLY2srCxGjhxJ+fLlMTExoW7duiX64C4pGRkZ9OnTh1atWrFz5058fX2pUKECdevWZe7cuaxYsUKOGxkZSdu2bQutg/Lly1OzZk3GjRvHjh072LNnj8pK2qNHj+jXrx/W1taYmZnRrFkzLl68qJLXr7/+Su3atTE0NMTKyoqgoKBCZV+9ejUWFhYcOHBAvta2bdsiV2UrVKhAr169WL58Obdu3SIgIIDbt29jYGCgMf64ceNISUnh5MmT9OrVi6pVq+Lq6kr//v25cOECpUuXVqnLL774AlNTUxwdHVm5cqUc9rKpvCbWrl2Lo6MjxsbGBAUF8c8//6iEK82OV69eTYUKFTA0NASKr1dluvXr1+Ps7Iy5uTmfffYZT58+LVSW12Xz5s1ERESwadMmxo0bR+3atXF2diYwMJCDBw/StGlTAM6ePcu1a9do3bq1Wh4WFhZqfUtZZmX/iYqKws3NDWNjYzp16kRGRgY//vgjzs7OlClThpCQEHJfenc+ffqUrl27YmJiQvny5VmyZIlK+Kv23Zc5ffo01tbWzJo1C9Cu/X/33XfY2NhgampK3759yczMVMu3uDb+ukiSRO/evXFxceHIkSO0bt2aSpUqUaNGDSZPnsyOHTvkuKNHj8bV1RVjY2MqVqzIxIkTycnJUctzxYoVODg4YGxsTOfOnVUmvF42lW/SpAkhISGMGjWKsmXLYmtry5QpU1TkmzJlCo6OjhgYGGBvb09ISIjGshgaGnL79m0CAgK4desWy5cvp3fv3sVOrirbXvXq1Vm2bBnPnz9n//79/PPPP3Tt2pXy5ctjbGyMh4cHmzZtUkn7888/4+HhgZGREZaWlvj6+vLs2TMgf4tMnTp1MDExwcLCAh8fH1JTU+W0O3bsoGbNmhgaGlKxYkWmTp2qMvmpUChYvXo1QUFBGBsb4+Liws6dO1Xuv3PnTlxcXDA0NKRp06b8+OOPattyjh49SsOGDTEyMsLBwYGQkBBZRsif6Jk2bRo9e/bEzMyMAQMGkJ2dzZAhQ7Czs8PQ0BAnJydmzpxZZD2+Cu/VOV1OTo7GBiwQCP676OnpkXnpdInSZMadxsirthhPBIK3jLKPPXmiOrFmYGBQqHKnCR0dHRYtWkSFChW4fv06wcHBjBo1iqVLl8pxMjIymDVrFqtXr8bS0pJy5coxZMgQ4uPjiYyMxN7enu3btxMQEEBcXBwuLi6vVKa8vDx0CnFyuW/fPu7fv8+oUaM0hitXwh48eEB8fDy1atXS6p7NmjXDy8uLbdu20a9fPwA+/fRTjIyM2LNnD+bm5qxYsYLmzZtz9epVypYty65duwgKCmL8+PGsW7eO7Oxsdu/erTH/2bNnM3v2bKKjo6lTp458vU6dOoSGhpKVlaXxeTVo0IDY2Fh8fX05duwYv/76Ky1bttR4j7y8PCIjI+nevTv29vZq4QWVdoCwsDCmTZvGuHHj+Pnnnxk0aBCNGzfGzc2t2Po6efIkffv2ZebMmbRv3569e/cyefJktXjJycls3bqVbdu2oaurCxRfr5C/yvjLL78QFRXFw4cP6dy5M9999x2hoaHFyvYqRERE4ObmRmBgoFqY0gQZ4MiRI7i6umJqalrie2RkZLBo0SIiIyN5+vQpHTp0ICgoCAsLC3bv3s3169fp2LEjPj4+dOnSRU43Z84cxo0bx9SpU9m3bx9Dhw7F1dUVPz8/4NX7bkEOHjxIhw4dmD17tuwTorjntHnzZqZMmcKSJUv45JNPWL9+PYsWLaJixYoqedepU4dbt26RkpKCs7NzieutOC5cuMDly5fZuHGjxnGj4Oq4qakpa9euxd7enri4OPr374+pqanKeJKcnMzmzZv59ddfefLkCX379iU4OLjIVe8ff/yRr7/+mpMnT3LixAl69+6Nj48Pfn5+bN26lfnz5xMZGUm1atW4e/eu2gSIEmNjY2bMmMHu3btp164dL1684ODBg+jp6WldH0ZGRkD+qnNmZiYff/wxo0ePxszMjF27dtGjRw8qVapEnTp1uHPnDl27dmX27NkEBQXx9OlTjhw5giRJvHjxgvbt29O/f382bdpEdnY2p06dQqFQAPl9oWfPnixatIiGDRty7do1ue0UHAumTp3K7NmzmTNnDosXL6Z79+6kpqZStmxZbty4QadOnRg6dCj9+vXj/PnzjBw5UqU8165dIyAggOnTp7NmzRr+/vtvhgwZwpAhQwgPD5fjzZ07l0mTJsn3XrRoETt37mTz5s04Ojpy8+ZNbt68qXU9ast7Vdyjo6MxNjZ+nyIIBIIPCCMjI1q0aEHe84ziIxcg73n+TGhsbCzPnz9/G6IJBAKQ9946ODioXJ88ebLKqo+SqKgoFQWuZcuWbNmyhWHDhsnXnJ2dmT59OgMHDlT5+M/JyWHp0qV4eXkBkJaWRnh4OGlpabKiOHLkSPbu3Ut4eDgzZszQKPO1a9eYOHEiMTExlClThqCgIHr06EG1atWIi4tj3Lhx/PrrrxrTJiUlAVClSpUi6yUtLQ1JkjQqsIVRpUoVLl26BOSv8Jw6dYp79+7JCvXcuXP55Zdf+PnnnxkwYAChoaF89tlnTJ06Vc5DWTcFGT16NOvXr+e3336jWrVqKmH29vZkZ2dz9+5dNZN+yFeQv/nmGxo0aICenh4LFizgxIkTjBs3Tl7NVXL//n0ePnxYbN0oadWqFcHBwbKM8+fPJzY2VivFfeHChQQEBMgKj6urK8ePH2fv3r0q8bKzs1m3bh3W1taAdvUK+ZMQa9eulRXkHj16cODAgbemuCclJWlV7tTU1ELbVNeuXeXJCSXx8fE4OjoC+f1n2bJlVKpUCYBOnTqxfv16/vrrL0qXLk3VqlVp2rQpsbGxKoq7j48PY8aMAfLr+dixY8yfP19W3F+l7xZk+/bt9OzZk9WrV8v31eY5LViwgL59+8rbBqZPn05MTIzaqruyvlJTU9+K4q7tmAD5fiWUODs7M3LkSCIjI1UU98zMTNatW0f58uUBWLx4Ma1btyYsLAxbW1uN+Xp6esoKo4uLC99//z0HDhzAz8+PtLQ02c+Enp4ejo6OKpN3BcnMzGTGjBmcPHmSJk2aUKtWLXx9fZkzZ06haQqSkZHBhAkT0NXVpXHjxpQvX15FEf7qq6/Yt28fmzdvlhX3Fy9e0KFDB3n88fDwAPInPx8/fkybNm3kNuvu7i7nNXXqVMaMGUOvXr0AqFixItOmTWPUqFEqinvv3r3p2rUrADNmzGDRokWcOnWKgIAAVqxYgZubG3PmzAHAzc2NP/74Q6Wfz5w5k+7du8vt3MXFhUWLFtG4cWOWLVsmj4PNmjVT2RaRlpaGi4sLn3zyCQqFQuP4+iZ4r4p7ixYtMDMze58iCASCDxAdo5JN6OkYmQDI5oUCgeDtoFxpv3nzpsr7u7DV9qZNm6rsNzUxye+rMTExzJw5kytXrvDkyRNevHhBZmYmGRkZ8oS+vr4+np6ectq4uDhyc3NxdXVVuUdWVhaWlpaFyjx8+HB8fHwYO3Ys169fZ9OmTdSunW+hY2VlpaIIv4y2ztWUE4YvK7dFIUmSvJp08eJF0tPT1crx/Plzec/5hQsX6N+/f5F5hoWF8ezZM86cOaO2Egn/tzpW0PlZQZKSkggPD0dXV5cpU6YQHh7O0qVLycjIUCtbSf/oUfBZKhQKbG1tuXfvnlZpExIS1LYF1K9fX01xd3JykpV20K5eIV+pKriqbWdnV6RsaWlpVK1aVT5/8eIFOTk5KpNU48aNY9y4cRrTl6RdFdam5s+fj6+vr8q1gkq+sbGxrAAB2NjY4OzsrCKjjY2NWjnr16+vdl7Q0/yr9F0lJ0+eJCoqip9//lnF/Fub55SQkMDAgQPVZIuNjVW5VlwbBxg4cKCKr4eMjAxatmypMhFS0Py/ICVp9z/99BOLFi3i2rVrpKen8+LFCzW9x9HRUVbalWXKy8sjMTGxSMW9IAXb66effsqCBQuoWLEiAQEBtGrVirZt22r0H5GRkYGNjQ179+6lT58+DBw4kP79+3PixIkiFXflpNHz58+xtrbmhx9+wNPTk9zcXGbMmMHmzZu5ffs22dnZZGVlye3Cy8uL5s2b4+Hhgb+/Py1atKBTp06UKVOGsmXL0rt3b/z9/fHz88PX15fOnTtjZ2cH5LeRY8eOqSjZubm5am2vYN2YmJhgZmYm101iYiK1a9dWKcvL5bx48SKXLl1SsXiQJIm8vDxu3LghTya8bF3Vu3dv/Pz8cHNzIyAggDZt2tCiRYtC6/BVea+Ku56eXonMMQQCwb8fKTcXQ8/aZMZp75nV0KM2Um6uGE8EgreMso+ZmZlpNfFuYmJC5cqVVa6lpKTQpk0bBg0aRGhoKGXLluXo0aP07duX7Oxs+QPMyMhIVmwh/0NaV1eXs2fPqq00vmyWXZB169bJ5qseHh4EBgaSlZXFw4cPC/0wVqKcJLhy5YqaQlMQKysrAB4+fKiiOBZFQkKCvJc0PT0dOzs7jfv1lbIrFZKiaNiwIbt27WLz5s3yqmlBHjx4AFCojJ9//jmA7F1aoVAwePBgjXGtra2xsLDgypUrxcoFqI3PCoXijTstU04MKdGmXl9FNnt7e5X9+Nu2bWPr1q0qH/tKM3xNuLq6alVvVlZWxMXFaQyztbVV61sF0VSm130Gr9p3lVSqVAlLS0vWrFlD69atZXm0fU7aUFwbB/j2229VVoabNGnCrFmzqFu3brH5FxwTvL29C4134sQJunfvztSpU/H398fc3JzIyMg38kvGop6jg4MDiYmJxMTEsH//foKDg5kzZw6//fabWrqyZcuq9e9KlSqpTPhoQjlpZG5urlLPc+bMYeHChSxYsED2gzBs2DDZeZuuri779+/n+PHjREdHs3jxYsaPH8/JkyepUKEC4eHhhISEsHfvXn766ScmTJjA/v37qVevHunp6UydOpUOHTqoyVNwcut123h6ejpffvmlRr8ASmsWUB9ratasyY0bN9izZw8xMTF07twZX19ffv75Z63vrQ3vVXEXCASCl1Ho6mLo8TE6pc20clCnY2qOoUdNFDq6xcYVCATvn7Nnz5KXl0dYWJi8R3Tz5s3FpvP29iY3N5d79+7RsGFDre+n6cNf6TCuOFq0aIGVlRWzZ89m+/btauGPHj3CwsKCSpUqYWZmRnx8vJpFgCYOHjxIXFwcw4cPB/I/+u7evUupUqUKNe/19PTkwIED9OnTp9B869Spw5AhQwgICKBUqVJq+zf/+OMPPvroI3mioTCcnZ2L/QWVjo4On332GevXr2fy5MlqJt3p6ekYGhq+EU/x7u7unDx5UuXa77//Xmw6ber1VShVqpSK0lyuXDmMjIyKVKQL0q1bNz777DN27Nihts9dkiSePHmCubk53t7eLFu2TMU6423zcr3+/vvv8irjq/ZdJVZWVmzbto0mTZrQuXNnNm/ejJ6enlbPSdkGevbsWaiskN/G9fT01LaJFKRcuXIq++5LlSpF+fLltXp+NWrUoGrVqoSFhdGlSxe1fe7KMeH48eM4OTmp/B6yoKM1JWlpafz5559y//n999/R0dHRaitFYRgZGdG2bVvatm3L4MGDqVKlCnFxcdSsWbPQNCX55Vxhk0bHjh0jMDBQngDMy8vj6tWrKtYpCoUCHx8ffHx8mDRpEk5OTmzfvp2vv/4ayB/nvb29GTt2LPXr12fjxo3Uq1ePmjVrkpiYqHUf04Sbm5uaX5DTp1V9KtWsWZP4+PhXuo+ZmRldunShS5cudOrUiYCAAB48eFDkJF5JEV7lBQLBB4gC8859oRCHUTI6Oph/+gXwbj5oBALB61O5cmVycnJYvHgx169fZ/369SxfvrzYdK6urnTv3p2ePXuybds2bty4walTp5g5cya7du16K7KamJiwevVqdu3aRbt27YiJiSElJYUzZ84watQo2XRXR0cHX19fjh49qpZHVlYWd+/e5fbt25w7d44ZM2YQGBhImzZtZCXE19eX+vXr0759e6Kjo0lJSeH48eOMHz9e/i/05MmT2bRpE5MnTyYhIYG4uDjZI3dBGjRowO7du5k6daqKiTPkO3h6k+aboaGhODg4ULduXdatW0d8fDxJSUmsWbMGb2/vQs2NS4pyFW7u3LkkJSXx/fffq5nJa0Kben0fdO7cmS5dutC1a1dmzJjBmTNnSE1NJSoqCl9fX9n8u2nTpqSnp3P58mW1PB49esTdu3dVjoKer1+VY8eOMXv2bK5evcqSJUvYsmULQ4cOBV697xakXLlyHDx4kCtXrtC1a1devHih1XMaOnQoa9asITw8nKtXrzJ58mSN9XLkyBHZI/jbQKFQyDI0bNhQdvR36dIlQkND5YkYFxcX0tLSiIyM5Nq1ayxatEjj5J+hoSG9evXi4sWLHDlyhJCQEDp37qzVxKIm1q5dyw8//MAff/zB9evX2bBhA0ZGRm9tz3VBXFxc5BX1hIQEvvzyS/766y85/OTJk3J7T0tLY9u2bfz999+4u7tz48YNxo4dy4kTJ0hNTSU6OpqkpCR50mjSpEmsW7eOqVOncvnyZRISEoiMjFTxI1AcX375JVeuXGH06NFcvXqVzZs3yxMWyomx0aNHc/z4cYYMGcKFCxdISkpix44dDBkypMi8582bx6ZNm7hy5QpXr15ly5Yt2NravvFf+QnFXSAQfHAodHQwcPOgTO+h6Jiaa4yjY2pOmd5DMXDzQFGcgi8QCD4YvLy8mDdvHrNmzaJ69epERERo/duc8PBwevbsyYgRI3Bzc6N9+/acPn1axYTxTRMYGMjx48fR09OjW7duVKlSha5du/L48WOmT58ux+vXrx+RkZFqZpl79+7Fzs4OZ2dnAgICiI2NZdGiRezYsUM2+VcoFOzevZtGjRrRp08fXF1d+eyzz0hNTcXGxgbIN+fdsmULO3fupEaNGjRr1oxTp05plPmTTz5h165dTJgwgcWLFwP5jqh++eWXYvfJl4SyZcvy+++/8/nnnzN9+nS8vb1p2LAhmzZtYs6cObJ39NelXr16rFq1ioULF+Ll5UV0dLRWH+za1Ov7QKFQsHHjRubNm8cvv/xC48aN8fT0ZMqUKQQGBuLv7w+ApaUlQUFBGj2M9+nTBzs7O5VD+axfhxEjRnDmzBm8vb2ZPn068+bNk+V5nb5bEFtbW9nqpHv37uTl5RX7nLp06cLEiRMZNWoUH3/8MampqQwaNEgt78jIyDfaxjVRp04dzpw5Q+XKlenfvz/u7u60a9eOy5cvy5Nl7dq1Y/jw4QwZMoQaNWpw/PhxJk6cqJZX5cqV6dChA61ataJFixZ4enqqOPorKRYWFqxatQofHx88PT2JiYnh119/LdIPyJtiwoQJ1KxZE39/f5o0aYKtra2KLwMzMzMOHz5Mq1atcHV1ZcKECYSFhdGyZUuMjY25cuUKHTt2xNXVlQEDBjB48GC+/PJLAPz9/YmKiiI6OpratWtTr1495s+fX6IJiQoVKvDzzz+zbds2PD09WbZsmWwRofTT4unpyW+//SZPzHh7ezNp0qRiHY+ampoye/ZsatWqRe3atUlJSWH37t2F/rHkVVFIJfUu8gZQmgA9fvxYOKcTCASFIuXlARKZcefIjDtN3vNn6BiZYOhRG0OPmoBCKO0CwTtEvL8LR5Ik6taty/Dhw2Wvxh8Sy5YtY/v27URHR79vUQQl4NKlS/j5+XHt2rUifTkIYM+ePYwYMYJLly69kS0agn8/oaGhLF++/K38uu1tIFq1QCD4YFEq5YbVa2Lk9X+eQKXcXLGnXSAQfFAoFApWrlxZqDOx942ent4bWZEVvFs8PT2ZNWsWN27ckH+dJdDMs2fPCA8PF0q7oFCWLl1K7dq1sbS05NixY8yZM6dYM/gPCbHiLhAIBAKBQCvE+1sgEAgE/6sMHz6cn376iQcPHuDo6EiPHj0YO3bs/8xkj1DcBQKBQCAQaIV4fwsEAoFA8H4Qm0MFAoFAIBAIBAKBQCD4gBGKu0AgeGdIublFngsEAoFAIBAIBAJ1/jcM+gUCwf80Su/wWVfOkRl/DikzA4WhMYZVa2LgLrzDCwQCgUAgEAgERSG+lAUCwVtFkiSyr13m/vzRPN66iqyEs2TfSCAr4SyPt67i/vzRZF+7zHtwtyEQCATvjN69e6v801gTTZo0YdiwYe9EHsH/Ntq0J2dnZ/m/4pD/54Nffvml0PgpKSkoFAouXLjwRmT8kOnRowczZsx46/fR5jm9KmvXrsXCwuKN5nno0CEUCgWPHj0CYO/evdSoUYO8vLw3ep/XITExEVtbW54+ffrO7z1lyhRq1KhRaPjbri+huAsEgreGlJdHdvIfPIpcQt6zJxrj5D17wqPIJWQn//H/V+YFAsG/gd69e6NQKNSO5OTk9y1aiTl//jyffvopNjY2GBoa4uLiQv/+/bl69eobvc+2bduYNm3aG81TE02aNCk2jlKJUx6Wlpa0aNGC8+fPv3X5/q1oU++QP+G9cuVK6tatS+nSpbGwsKBWrVosWLCAjIwMre93+vRpBgwY8IrS/m8SGxtLq1atsLS0xNjYmKpVqzJixAhu374tx7l48SK7d+8mJCREvtakSRON49XAgQPfRzE+GAICAtDT0yMiIuKt3+vJkyeMHz+eKlWqYGhoiK2tLb6+vmzbtk1lcWfs2LF89dVXmJqaAv832aBQKNDR0cHc3Bxvb29GjRrFnTt33rrcBXnb9SUUd4FA8BaReLLzR5CKUcilPJ78ug4Qq+4Cwb+JgIAA7ty5o3JUqFChxPnk5ua+txWfqKgo6tWrR1ZWFhERESQkJLBhwwbMzc2ZOHHiG71X2bJl5Y/RN83Vq1eJjIxUuXbu3DmioqKKTBcTE8OdO3fYt28f6enptGzZUl6N+y+Sk5NTovjHjh0jJiZG5VpMTAzHjx8vNE2PHj0YNmwYgYGBxMbGcuHCBSZOnMiOHTuIjo7W+t7W1tYYGxuXSN6ikCSJFy9evLH8ikI5cVQSVqxYga+vL7a2tmzdupX4+HiWL1/O48ePCQsLk+MtXryYTz/9lNKlS6uk79+/v9p4NXv27DdSnjdNSdvh69C7d28WLVpU4jRTpkzROv6jR49o0KAB69atY+zYsZw7d47Dhw/TpUsXRo0axePHjwFIS0sjKiqK3r17q+WRmJjIn3/+yenTpxk9ejQxMTFUr16duLi4Esn+urxKfWmLUNwFAsFbQcrNJSvhXKEr7S+Tl/6YrITzwmGdQPAvwsDAAFtbW5VDV1eXefPm4eHhgYmJCQ4ODgQHB5Oeni6nU5qA7ty5k6pVq2JgYEBaWhpZWVmMHDmS8uXLY2JiQt26dTl06NBbkz8jI4M+ffrQqlUrdu7cia+vLxUqVKBu3brMnTuXFStWAPkTC3379qVChQoYGRnh5ubGwoULNeY5depUrK2tMTMzY+DAgWRnZ8thL5vKOzs7M2PGDL744gtMTU1xdHRk5cqVcnh2djZDhgzBzs4OQ0NDnJycmDlzpsb7WllZERsbS+fOnXn06BGTJk1i7NixVKxYscg6sLS0xNbWllq1ajF37lz++usvTp48ybVr1wgMDMTGxobSpUtTu3ZtNQV16dKluLi4YGhoiI2NDZ06dZLDfv75Zzw8PDAyMsLS0hJfX1+ePXsmh69evRp3d3cMDQ2pUqUKS5culcOUSt22bdto2rQpxsbGeHl5ceLECZX7r1q1CgcHB4yNjQkKCmLevHlqpsU7duygZs2aGBoaUrFiRaZOnaqinCoUCpYtW0a7du0wMTEhNDSUhw8f0r17d6ytrTEyMsLFxYXw8HCN9efo6MiKFSsIDg7m6dOnBAcHs3LlShwcHDTG37x5MxEREWzatIlx48ZRu3ZtnJ2dCQwM5ODBgzRt2lQl/ty5c7Gzs8PS0pLBgwerKHQvm8q/zKlTp/D29sbQ0JBatWqpWVMoVzL37NnDxx9/jIGBAUePHiUvL4+ZM2fK7d3Ly4uff/5ZLd2BAweoVasWxsbGNGjQgMTExEJleV1u3bpFSEgIISEhrFmzhiZNmuDs7EyjRo1YvXo1kyZNAvL76s8//0zbtm3V8jA2NlYbr5S/vVS2uc2bN9OwYUOMjIyoXbs2V69e5fTp09SqVYvSpUvTsmVL/v77b7W8i+r3e/fu5ZNPPsHCwgJLS0vatGnDtWvX5HDlvX/66ScaN26MoaGhxhXdv//+m1q1ahEUFERWVlaxzwlg9+7duLq6YmRkRNOmTUlJSVHLt23btpw5c0ZFpjfNuHHjSElJ4eTJk/Tq1YuqVavi6upK//79uXDhgjzJsnnzZry8vChfvrxaHuXKlcPW1hZXV1c+++wzjh07hrW1NYMGDVKJV9TYAvltqWvXrpQtWxYTExNq1arFyZMnNcp97do1KlasyJAhQ2SrgLdZX+/VOV1OTs47nTESCATvDj09PTLjz5UoTWbCWQyr1RLjgkDwgaLsm0+eqE7IGRgYYGBgoHU+Ojo6LFq0iAoVKnD9+nWCg4MZNWqUygdURkYGs2bNYvXq1VhaWlKuXDmGDBlCfHw8kZGR2Nvbs337dgICAoiLi8PFxeWVypSXl4dOIc4x9+3bx/379xk1apTGcKUSmJeXx0cffcSWLVuwtLTk+PHjDBgwADs7Ozp37izHP3DgAIaGhhw6dIiUlBT69OmDpaUloaGhhcoXFhbGtGnTGDduHD///DODBg2icePGuLm5sWjRInbu3MnmzZtxdHTk5s2b3Lx5U2M+ZcuWZcWKFaxcuZItW7ZQrVo19u3bp2Ut5WNkZATkTxikp6fTqlUrQkNDMTAwYN26dbRt25bExEQcHR05c+YMISEhrF+/ngYNGvDgwQOOHDkCwJ07d+jatSuzZ88mKCiIp0+fcuTIEfnDNyIigkmTJvH999/j7e3N+fPn6d+/PyYmJvTq1UuWZ/z48cydOxcXFxfGjx9P165dSU5OplSpUhw7doyBAwcya9Ys2rVrR0xMjJqFxJEjR+jZsyeLFi2iYcOGXLt2TTYtnzx5shxvypQpfPfddyxYsIBSpUoxceJE4uPj2bNnD1ZWViQnJ/P8+XONdebg4MCWLVvkVUR/f381RaEgERERuLm5ERgYqBamUCgwNzeXz2NjY7GzsyM2Npbk5GS6dOlCjRo16N+/f5HPESA9PZ02bdrg5+fHhg0buHHjBkOHDtUYd8yYMcydO5eKFStSpkwZZs6cyYYNG1i+fDkuLi4cPnyYzz//HGtraxo3biynGz9+PGFhYVhbWzNw4EC++OILjh07Vqxsr8KWLVvIzs4utq9eunSJx48fU6tWrVe6z+TJk1mwYAGOjo588cUXdOvWDVNTUxYuXIixsTGdO3dm0qRJLFu2TE5TXL9/9uwZX3/9NZ6enqSnpzNp0iSCgoK4cOGCytg0ZswYwsLC5MmWgv335s2b+Pn5Ua9ePX744Qd0dXUJDQ0t8jndvHmTDh06MHjwYAYMGMCZM2cYMWKEWpkdHR2xsbHhyJEjVKpU6ZXqrSjy8vKIjIyke/fu2Nvbq4UXtIw4cuSI1s/OyMiIgQMHMnz4cO7du0e5cuWKHVvS09Np3Lgx5cuXZ+fOndja2nLu3DmNFl+XLl3C39+fvn37Mn36dPn626yv96q4R0dHv1ETHoFA8GFgZGREixYtkDK134sHyPFjY2ML/QgSCATvD+X+2pdXCydPnqzRLDIqKkrlo6tly5Zs2bJFbVV5+vTpDBw4UEWhycnJYenSpXh5eQH5JpLh4eGkpaXJH3cjR45k7969hIeHF+po6tq1a0ycOJGYmBjKlClDUFAQPXr0oFq1asTFxTFu3Dh+/fVXjWmTkpIAqFKlSpH1oqenx9SpU+XzChUqcOLECTZv3qyiuOvr67NmzRqMjY2pVq0a3377Ld988w3Tpk0rdPKgVatWBAcHAzB69Gjmz59PbGwsbm5upKWl4eLiwieffIJCocDJyalQGR8+fMj48eO5f/8+Xl5eVKpUiZYtW7JgwQLc3NyKLB/km7JOmzaN0qVLU6dOHWxsbORnAzBt2jS2b9/Ozp07GTJkCGlpaZiYmNCmTRtMTU1xcnLC29sbyFfcX7x4QYcOHWSZPTw85LwmT55MWFgYHTp0kOszPj6eFStWqCjuI0eOpHXr1kD+ima1atVITk6mSpUqLF68mJYtWzJy5EgAXF1dOX78uMrWgKlTpzJmzBg5z4oVKzJt2jRGjRqlorh369aNPn36yOdpaWl4e3vLCoSzs3Oh9Xb79m1GjBhBmTJlqFmzJg8fPuSzzz4jLCxM46phUlKSVs8DoEyZMnz//ffo6upSpUoVWrduzYEDB7RS3Ddu3EheXh4//PADhoaGVKtWjVu3bqmtTgJ8++23+Pn5AZCVlcWMGTOIiYmhfv36QH69HT16lBUrVqgo7qGhofL5mDFjaN26NZmZmRgaGmpVvpKQlJSEmZkZdnZ2RcZLTU1FV1eXcuXKqYUtXbqU1atXq1xbsWIF3bt3l89HjhyJv78/AEOHDqVr164cOHAAHx8fAPr27cvatWtV8iiu33fs2FEl/po1a7C2tiY+Pp7q1avL14cNGyb3iYIkJibi5+dHUFAQCxYsQKFQaPWcli1bRqVKleRtBG5ubsTFxTFr1iy1e9jb25Oamlpovb4O9+/f5+HDh8WOs5D//Eoy6aLMMyUlhXLlyhU7tmzcuJG///6b06dPU7ZsWQAqV66slu/x48dp06YN48eP1zjZ8bbq670q7i1atJBNUAQCwb8PhWHJJuaU8V82BRQIBB8GypX2mzdvqry/C1ttb9q0qcrKk4mJCZC/x3fmzJlcuXKFJ0+e8OLFCzIzM8nIyJAn9PX19fH09JTTxsXFkZubi6urq8o9srKysLS0LFTm4cOH4+Pjw9ixY7l+/TqbNm2idu3a5OTkYGVlpaJwv0xJ/naxZMkS1qxZQ1paGs+fPyc7O1vN+7CXl5fKgkX9+vVJT0/n5s2bhSrdBetAoVBga2vLvXv3gPy9lH5+fri5uREQEECbNm1o0aKFxnzu3btHw4YN6dq1K02aNOHbb7/l3LlzXL16tUhFsUGDBujo6PDs2TMqVqzITz/9hI2NDenp6UyZMoVdu3bJivjz589JS0sDwM/PDycnJypWrEhAQAABAQEEBQXJZu3NmzfHw8MDf39/WrRoQadOnShTpgzPnj3j2rVr9O3bV0UBffHihcpq88t1o1TY7t27R5UqVUhMTCQoKEglfp06dVQU94sXL3Ls2DEVi4fc3Fy1tviyojBo0CA6duzIuXPnaNGiBe3bt6dBgwYa6y8lJYV+/frh6+tLkyZNWLZsGTExMaSkpGhU3EvS5qpVq4aurq5KHWi7nzchIQFPT08VJVqp4L1MwfInJyeTkZEhK/JKsrOz5YkZJYU9H0dHx0LLo1R2lPVQcOKvYcOG7NmzR2NaSZK02hP//PlzDAwMNMbt3r0748ePV7lmY2Ojcl6wTMqwgpNONjY2cv9UUly/T0pKYtKkSZw8eZL79+/Lq7tpaWkqirsmhfX58+c0bNiQbt26qWyL0OY5JSQkULduXZXwwtqAkZFRkY4RIyIi+PLLL+XzrKwsFAoFc+fOla/t2bOHhg0bqqUtSZt//vx5iSZ+lHkrFAqtxpYLFy7g7e0tK+2aSEtLw8/Pj9DQ0EL/AlJcfb0q71Vx19PTQ09P732KIBAI3hJSbi6GVWuSlXBW6zSG7h8j5eaKcUEg+EBR9k0zMzOtJt5NTEzUVitSUlJo06YNgwYNIjQ0lLJly3L06FH69u1Ldna2/IFrZGSk8nGdnp6Orq4uZ8+eVVFWADUnUwVZt26dbCbr4eFBYGAgWVlZPHz4EFtb2yLlV04SXLlypdAPWoDIyEhGjhxJWFgY9evXx9TUlDlz5hS6L7IkvDweKhQK+cO+Zs2a3Lhxgz179hATE0Pnzp3x9fVV28cK+atpLyvoNWvWpGbNmkXe/6effqJq1apYWlqq7A8fOXIk+/fvZ+7cuVSuXBkjIyM6deok7901NTXl3LlzHDp0iOjoaCZNmsSUKVM4ffo0FhYW7N+/n+PHjxMdHc3ixYsZP348J0+elJ//qlWr1JSKl597wbpRtpWSODFMT09n6tSpGlcxCyoHygknJS1btiQ1NZXdu3ezf/9+mjdvzuDBg1WUFCXKldiC+Pr6FiqTq6srV65c0Ur+otrGm6Rg+ZW+KHbt2qU28fDyBF5Jn8/u3bvl7Ti3b9+mSZMmKr+mU27V0ISrqyuPHz/mzp07Ra66W1lZkZGRQXZ2Nvr6+iph5ubmGldXiyvTy9dK+gzatm2Lk5MTq1atwt7enry8PKpXr66yDx7U2yHk17mvry9RUVF888038jMpyXPShgcPHmBtbV1oeLt27VT66+jRoylfvryK535NE1WQ70TRwsJCq3ZvZWXFw4cPtZY7ISEByLeKUdZJUWNLUW2soLz29vZs2rSJL774QuO7sLj6elWEczqBQPBWUOjqYuBeEx0T7axqdEqbY+DujeKlDzOBQPDv4uzZs+Tl5REWFka9evVwdXXlzz//LDadt7c3ubm53Lt3j8qVK6scRSngmv5zrHSaVxwtWrTAysqqUM/SSu/qx44do0GDBgQHB+Pt7U3lypU1Oia6ePGiyjag33//ndKlSxfqqEwbzMzM6NKlC6tWreKnn35i69atPHjwoMg0JXHo5+DgQKVKldTq8dixY/Tu3ZugoCA8PDywtbVVc2xVqlQpfH19mT17NpcuXSIlJYWDBw8C+QqOj48PU6dO5fz58+jr67N9+3ZsbGywt7fn+vXras+5JH8kcHNz4/Tp0yrXXj6vWbMmiYmJavepXLlyoVsXlFhbW9OrVy82bNjAggULVJwGFoY29d6tWzeuXr3Kjh071MIkSZK9a78u7u7uXLp0iczMTPna77//Xmy6gs4iX66z12nHAE5OTnJeSguUgvkXpvgBdOrUCX19/WL7qtIKJj4+/rVkLQlF9ft//vmHxMREJkyYQPPmzXF3dy+RYqqjo8P69ev5+OOPadq0qTyWavOc3N3dOXXqlEp+mtpAZmYm165dU7OoKIipqanKPUxNTSlbtqzKtcKUYh0dHT777DMiIiI0vgvS09Nlh5He3t5aP7vnz5+zcuVKGjVqhLW1tVZji6enJxcuXChyDDUyMiIqKgpDQ0P8/f3V/ievTX29KkJxFwgEbxEFZu16gaKYoUahg1nbnkDJfv0iEAj+96hcuTI5OTksXryY69evs379epYvX15sOldXV7p3707Pnj3Ztm0bN27c4NSpU8ycOZNdu3a9FVlNTExYvXo1u3btkh2cpaSkcObMGUaNGiX/49nFxYUzZ86wb98+rl69ysSJE9WURMg3U+3bty/x8fHs3r2byZMnM2TIkGKVxMKYN28emzZt4sqVK1y9epUtW7Zga2urcbLiTePi4sK2bdu4cOECFy9epFu3biorjVFRUSxatIgLFy6QmprKunXryMvLw83NjZMnTzJjxgzOnDlDWloa27Zt4++//8bd3R3I33s+c+ZMFi1axNWrV4mLiyM8PJx58+ZpLd9XX33F7t27mTdvHklJSaxYsYI9e/aoWHFMmjSJdevWMXXqVC5fvkxCQgKRkZFMmDChyLwnTZrEjh07SE5O5vLly0RFRcmyvy6dO3emS5cudO3aVa6j1NRUoqKi8PX1JTY29o3cp1u3bigUCvr37y+3R00WAy9jamrKyJEjGT58OD/++CPXrl3j3LlzLF68mB9//PGNyPYqODg4MH/+fBYuXEjfvn357bffSE1N5dixY3z55ZdMmzYNyJ9wqVmzJkePHlXLIyMjg7t376ocJVGiC6Oofl+mTBksLS1ZuXIlycnJHDx4kK+//rpE+evq6hIREYGXlxfNmjXj7t27Wj2ngQMHkpSUxDfffENiYiIbN25U258P+cq8gYFBkVZHr0toaCgODg7UrVuXdevWER8fT1JSEmvWrMHb21teLff39+fEiRPkavgD0b1797h79y5JSUlERkbi4+PD/fv3VbZrFTe2dO3aFVtbW9q3b8+xY8e4fv06W7duVftjhYmJCbt27aJUqVK0bNlS5a8ob7O+hOIuEAjeGgodHfQrV8fis8HolDbXGEentDkWnw1Gv3J1FK/48SoQCP538PLyYt68ecyaNYvq1asTERFR6C/MXiY8PJyePXsyYsQI3NzcaN++PadPny50z+ybIDAwkOPHj6Onp0e3bt2oUqUKXbt25fHjx7In4S+//JIOHTrQpUsX6tatyz///CM7lCtI8+bNcXFxoVGjRnTp0oV27dqV6F/HL2Nqasrs2bOpVasWtWvXJiUlhd27d7/yREBJmDdvHmXKlKFBgwa0bdsWf39/FbN7CwsLtm3bRrNmzXB3d2f58uVs2rSJatWqYWZmxuHDh2nVqhWurq5MmDCBsLAwWrZsCUC/fv1YvXo14eHheHh40LhxY9auXVuiFXcfHx+WL1/OvHnz8PLyYu/evQwfPlzFBN7f35+oqCiio6OpXbs29erVY/78+UU6+YN8/wtjx47F09OTRo0aoaurS2RkZAlrUDMKhYKNGzcyb948fvnlFxo3boynpydTpkwhMDBQdoz2upQuXZpff/2VuLg4vL29GT9+vEanZJqYNm0aEydOZObMmbi7uxMQEMCuXbtK9HzeBsHBwURHR3P79m2CgoKoUqUK/fr1w8zMTHZSCPntS9Pv1FatWoWdnZ3K0bVr19eWq6h+r6OjQ2RkJGfPnqV69eoMHz6cOXPmlPgepUqVkvtXs2bNuHfvXrHPydHRka1bt/LLL7/g5eXF8uXLNTr53LRpE927d3+rDsXLli3L77//zueff8706dPx9vamYcOGbNq0iTlz5sh70Fu2bEmpUqXUfj0J+VY29vb2fPzxx3z33Xf4+vryxx9/ULVqVTlOcWOLvr4+0dHRlCtXjlatWuHh4cF3332ntk0H8vvQnj17kCSJ1q1by7+zfJv1pZBK4hHgDfHkyRPMzc15/PixcE4nEPwHkPLyAImshPNkJpxFysxAYWiMofvHGLh7AwqhtAsE/wOI97fgf5n+/ftz5coV+bd0gv8mz58/x83NjZ9++umtriL/G7h//z5ubm6cOXPmvU/MKFmyZAk7d+4s8e8s3wVvu77eq3M6gUDw30CplBtU8caw2v95RZVyc1HoiD3tAoFAIHjzzJ07Fz8/P0xMTNizZw8//vhjkf9QF/w3MDIyYt26ddy/f/99i/LBk5KSwtKlSz8YpR3yLZwePXrE06dPMTU1fd/iqPC260usuAsEAoFAINAK8f4W/C/RuXNnDh06xNOnT6lYsSJfffWV7JdAIBAI/tcQK+4CgUAgEAgEgn8dmzdvft8iCAQCwRtDbCoVCAQCgUAgEAgEAoHgA0Yo7gKBQCAQCAQCgUAgEHzACMVd8FaRXvrP4svnAoFAIBAIBAKBQCAoGrHHXfBWyP/9F2QlXyDr6gXysjLQMTDGwLUGBi7eAOL3XwKBQCAQCAQCgUCgBUJzErxxJEkiOzWBf1aO58mucLKSzpOTlkhW0nme7Arnn5XjyU5N4D380EAgEAgEgrfGxIkTGTBgwFu/z5QpU6hRo8ZbyfvQoUMoFAoePXr0xvJMSUlBoVBw4cIFAOLj4/noo4949uzZG7vHm6RJkyYMGzasyDjOzs4sWLDgncgj+N/nn3/+oVy5cqSkpLzze69duxYLC4tCwz+0/qhN/1MoFPzyyy+A+viiibcxrr0PhOIueKNIeXlkp8Tz+JcV5GU81RgnL+Mpj39ZQXZKvLwyLxAIBIJ/F71790ahUKgdycnJ71u0EnP+/Hk+/fRTbGxsMDQ0xMXFhf79+3P16lU5zt27d1m4cCHjx4+XrxVWBwEBAe+jGB8MVatWpV69esybN0+r+L1799Za4YmNjaVVq1ZYWlpibGxM1apVGTFiBLdv334NidU5ffr0O5mkadKkSbFxlEqJ8rCxsaFjx45cv379rcv3b0WbegfIzs5m9uzZeHl5YWxsjJWVFT4+PoSHh5OTkyPHCw0NJTAwEGdnZ+D/lE3lYWpqSrVq1Rg8eDBJSUlvoUSFU9L++DpoW1/FcefOHVq2bPkWJf0wEYq74I3zdN8GkIpRyKU8nkZHvBuBBAKBQPBeCAgI4M6dOypHhQoVSpxPbm4uee9pojcqKop69eqRlZVFREQECQkJbNiwAXNzcyZOnCjHW716NQ0aNMDJyUklvaY62LRp07suhlaU5MP5denTpw/Lli3jxYsXGsMfPHjAkiVLVKzzrl27RkRE4d8OK1aswNfXF1tbW7Zu3Up8fDzLly/n8ePHhIWFvVH5ra2tMTY2fqN5Kjl27BgxMTEq12JiYjh+/HiR6RITE/nzzz/ZsmULly9fpm3btuT+h30LlbQ9R0VFce7cOZVrkZGRKhN0BcnOzsbf35/vvvuOAQMGcPz4cU6dOsXgwYNZvHgxly9fBiAjI4MffviBvn37quURExPDnTt3uHjxIjNmzCAhIQEvLy8OHDhQItlfl+L6oyamTJlC7969tY6vbX1pg62tLQYGBlrHL473+Y4pCUJxF7wxpNxcspLOF7rS/jJ5z56QlXxBOKwTCASCfykGBgbY2tqqHLq6usybNw8PDw9MTExwcHAgODiY9PR0OZ3StHPnzp1UrVoVAwMD0tLSyMrKYuTIkZQvXx4TExPq1q3LoUOH3pr8GRkZ9OnTh1atWrFz5058fX2pUKECdevWZe7cuaxYsUKOGxkZSdu2bbWqgzJlysjhCoWCFStW0KZNG4yNjXF3d+fEiRMkJyfTpEkTTExMaNCgAdeuXVPLe8WKFTg4OGBsbEznzp15/PixHHb69Gn8/PywsrLC3Nycxo0bqyklCoWCZcuW0a5dO0xMTAgNDdVYBy1btsTHx0c2M129ejXu7u4YGhpSpUoVli5dqpLm1KlTeHt7Y2hoSK1atTh//rxavn5+fjx48IDffvtNY90bGhpy+/ZtAgICuHXrFsuXL6d3796FTvzcunWLkJAQQkJCWLNmDU2aNMHZ2ZlGjRqxevVqJk2aBOSbLHft2pXy5ctjbGyMh4eHxomUFy9eMGTIEMzNzbGysmLixIkqkwgvm8orFApWr15NUFAQxsbGuLi4sHPnTjn84cOHdO/eHWtra4yMjHBxcSE8PFxjWRwdHVmxYgXBwcE8ffqU4OBgVq5ciYODg8b4SsqVK4ednR2NGjVi0qRJxMfHk5ycXGxbkCSJKVOm4OjoiIGBAfb29oSEhMjhS5cuxcXFBUNDQ2xsbOjUqZMclpeXx8yZM6lQoQJGRkZ4eXnx888/y+FKa4ADBw5Qq1YtjI2NadCgAYmJiSqyT58+nXLlymFqakq/fv0YM2aM2naQotqdcgX7p59+onHjxhgaGhIREUFqaipt27alTJkymJiYUK1aNXbv3q2x/ipWrMjYsWOZPHkyjx49onPnzsTGxmJlZaUx/oIFCzh8+DAHDhxg8ODB1KhRg4oVK9KtWzdOnjyJi4sLALt378bAwIB69eqp5WFpaYmtrS0VK1YkMDCQmJgY6tatS9++fVUmXXbs2EHNmjUxNDSkYsWKTJ06VUXJfvToEV9++aVsFVS9enWioqI0yv33339Tq1YtgoKCyMrKAorvj28CbesL8tvVqFGjKFu2LLa2tkyZMkUlr4Km8prYvXs3rq6uGBkZ0bRpUzWLnVd9xyjT7du3D3d3d0qXLi1Pzr4L3qtzupycnHc6uyt4u+jp6ZF19UKJ0mRdPY+ha03RDgQCgeB/AOVY/eTJE5XrBgYGJVr90NHRYdGiRVSoUIHr168THBzMqFGjVD7EMzIymDVrFqtXr8bS0pJy5coxZMgQ4uPjiYyMxN7enu3btxMQEEBcXJzKR19JyMvLQ6cQZ6n79u3j/v37jBo1SmO4ct/ogwcPiI+Pp1atWq8kw7Rp05g3bx7z5s1j9OjRdOvWTVYiHB0d+eKLLxgyZAh79uyR0yQnJ7N582Z+/fVXnjx5Qt++fQkODpZXpJ8+fUqvXr1YvHgxkiQRFhZGq1atSEpKwtTUVM5nypQpfPfddyxYsIBSpUqpmFc/evSI1q1bU7p0afbv34+xsTERERFMmjSJ77//Hm9vb86fP0///v0xMTGhV69epKen06ZNG/z8/NiwYQM3btxg6NChamXW19enRo0aHDlyhObNm6uFGxsbM2PGDHbv3k27du148eIFBw8eRE9PT2Mdbtmyhezs7GKfVWZmJh9//DGjR4/GzMyMXbt20aNHDypVqkSdOnXk+D/++CN9+/bl1KlTnDlzhgEDBuDo6Ej//v0LfY5Tp05l9uzZzJkzh8WLF9O9e3dSU1MpW7YsEydOJD4+nj179mBlZUVycjLPnz/XmI+DgwNbtmxh7NixnDt3Dn9/f7XJkeIwMjIC8lc5i2sLW7duZf78+URGRlKtWjXu3r3LxYsXAThz5gwhISGsX7+eBg0a8ODBA44cOSLfZ+bMmWzYsIHly5fj4uLC4cOH+fzzz7G2tqZx48ZyvPHjxxMWFoa1tTUDBw7kiy++4NixYwBEREQQGhrK0qVL8fHxITIykrCwMJVJmuLanZIxY8YQFhYmTxz179+f7OxsDh8+jImJCfHx8ZQuXVpjnVWtWpV9+/bRrVs3Ll68SHBwcJHbISIiIvD19cXb21stTE9PT26rR44c4eOPPy72mUH+2Dh06FCCgoI4e/YsderU4ciRI/Ts2ZNFixbRsGFDrl27Jss1efJk8vLyaNmyJU+fPmXDhg1UqlSJ+Ph4dHV11fK/efMmfn5+1KtXjx9++EGOU1x/fBNoW1+Q3/++/vprTp48yYkTJ+jduzc+Pj74+fkVe5+bN2/SoUMHBg8ezIABAzhz5gwjRoxQi/eq75iMjAzmzp3L+vXr0dHR4fPPP2fkyJFFWgO9Kd6r4h4dHf3WzIwE7xYjIyNatGhBXlZGidLlZea/tGJjYwt9gQkEAoHgwyAjI3+Mf3nlb/LkyWorIpBvelrwI7lly5Zs2bJFxfGQs7Mz06dPZ+DAgSrKSU5ODkuXLsXLywuAtLQ0wsPDSUtLw97eHoCRI0eyd+9ewsPDmTFjhkaZr127xsSJE4mJiaFMmTIEBQXRo0cPqlWrRlxcHOPGjePXX3/VmFa517RKlSpF1ktaWhqSJMlyFVUHAOPGjWPcuHHyeZ8+fejcuTMAo0ePpn79+kycOBF/f38Ahg4dSp8+fVTyyMzMZN26dZQvXx6AxYsX07p1a8LCwrC1taVZs2Yq8VeuXImFhQW//fYbbdq0ka9369ZNJW+l4n737l26dOmCi4sLGzduRF9fH8h/1mFhYXTo0AGAChUqEB8fz4oVK+jVqxcbN24kLy+PH374AUNDQ6pVq8atW7cYNGiQWt3Y29uTmpqqsU4zMzOZMWMGJ0+epEmTJtSqVQtfX1/mzJmjomArSUpKwszMDDs7O435KSlfvjwjR46Uz7/66iv27dvH5s2bVfJ1cHBg/vz5KBQK3NzciIuLY/78+UUq7r1796Zr164AzJgxg0WLFnHq1CkCAgJIS0vD29tbntxR7nXWxO3btxkxYgRlypShZs2aPHz4kM8++4ywsDD5eRfFnTt3mDt3LuXLl8fNzQ0PDw+V8JfbQlpaGra2tvj6+qKnp4ejo6NcF2lpaZiYmNCmTRtMTU1xcnKSFa+srCxmzJhBTEwM9evXB/JXrY8ePcqKFStUFPfQ0FD5fMyYMbRu3ZrMzEwMDQ1ZvHgxffv2ldvhpEmTiI6OVrHCKa7dKRk2bJgcRyl/x44d5TqoWLFiofWWmJjIsGHDqF27Nl5eXsTExHDhwgVCQ0NVrGSUJCUlabUXPjU1VePYUBjK8SYlJYU6deowdepUxowZI5ezYsWKTJs2jVGjRjF58mRiYmI4deoUCQkJuLq6FlrOxMRE/Pz8CAoKYsGCBSgUCpXwovrjm0Db+gLw9PRk8uTJALi4uPD9999z4MABrRT3ZcuWUalSJXl7jLL/zpo1SyXeq75jcnJyWL58OZUqVQJgyJAhfPvtt1qV63V5r4p7ixYtMDMze58iCN4wOgYlm4jRMcyfEW7atOnbEEcgEAgEbxDlSvvNmzdV3t+FrbY3bdqUZcuWyecmJiZA/r7OmTNncuXKFZ48ecKLFy/IzMwkIyNDntDX19fH09NTThsXF0dubq78YaokKysLS0vLQmUePnw4Pj4+jB07luvXr7Np0yZq165NTk4OVlZWTJ06tdC02v79RDnxbGhoqBb2ch0AlC1bVuW8YDltbGwAVJQtGxsbMjMzefLkiVzvjo6OKkpc/fr1ycvLIzExEVtbW/766y8mTJjAoUOHuHfvHrm5uWRkZJCWlqZy78KsBPz8/KhTpw4//fSTvCr37Nkzrl27Rt++fVUU2BcvXmBubg5AQkICnp6eKnWhVOpexsjISJ4MepmMjAxsbGzYu3cvffr0YeDAgfTv358TJ05oVNwlSVJTRDSRm5vLjBkz2Lx5M7dv3yY7O5usrCy1haR69eqp5Fe/fn3CwsLIzc3VuJIJqs/RxMQEMzMz7t27B8CgQYPo2LEj586do0WLFrRv354GDRpozCclJYV+/frh6+tLkyZNWLZsGTExMaSkpBSpuH/00UdIkkRGRgZeXl5s3boVfX39YtvCp59+yoIFC6hYsSIBAQG0atWKtm3bUqpUKfz8/HBycpLDAgIC5O0AycnJZGRkqClT2dnZaquqBetGObly7949HB0dSUxMJDg4WCV+nTp1OHjwIKBdu1PycnsOCQlh0KBBREdH4+vrS8eOHVVkKcjVq1cJDQ2lZs2aHD58mM2bN7Np0yb+/vtvjYp7ScYHTWNDYSjzVba/ixcvcuzYMZWtLLm5ufKYeeHCBT766CO1sfFlGRo2bEi3bt0K/RtCUf0R8i0HCjqEy87ORpIkla0RK1asoHv37kWWSxtefkZ2dnZyXyqOhIQE6tatq3JN0xj0qu8YY2NjWWkvqWyvy3tV3F82ixD8byPl5mLgWoOsJPW9bIVh4OqNlJsr2oFAIBD8D6Acq83MzLSaeDcxMaFy5coq11JSUmjTpg2DBg0iNDSUsmXLcvToUfr27Ut2drasQBkZGakoTunp6ejq6nL27Fk1xakw01eAdevWyWbSHh4eBAYGkpWVxcOHD7G1tS1SfuUH3JUrVwpVPgF5D+zDhw+xtrYutg5epuA7UFlmTddK4jypV69e/PPPPyxcuBAnJycMDAyoX78+2dnZavJponXr1rKDN+UkgnIFdNWqVWofxoUps0Xx4MEDlQ/ggpQtW5bBgwerXKtUqVKh8V1dXXn8+DF37twpctV9zpw5LFy4kAULFsh+FoYNG6ZWL6/Cy98yCoVCfmYtW7YkNTWV3bt3s3//fpo3b87gwYOZO3euWj4+Pj5q13x9fYu9/5EjRzAzM5P3iispri04ODiQmJhITEwM+/fvJzg4mDlz5vDbb79hamrKuXPnOHToENHR0UyaNIkpU6Zw+vRpuT3s2rVLbULh5cm812nPJWl3L7fnfv364e/vz65du4iOjmbmzJmEhYXx1Vdfqd1Hk48KpQWFJlxdXbly5Uqx8ltZWfHw4cNi4ylJSEgAkLcKpKenM3XqVBVLAiWGhobytoiiMDAwwNfXl6ioKL755huNE0BF9UfInxQp+Mu1RYsWcfv2bZWVbOXEoya0rS8oui+9KV71HaNJtnf1i2vhnE7wxlDo6mLg4o2OsWnxkQEdEzMMKtdA8Qove4FAIBD8b3L27Fny8vIICwujXr16uLq68ueffxabztvbm9zcXO7du0flypVVjqIUcE3/L1Y6jCuOFi1aYGVlxezZszWGK521VapUCTMzM+Lj44vN802RlpamUm+///47Ojo6uLm5AfmeyUNCQmjVqhXVqlXDwMCA+/fva53/d999R69evWjevLlcLhsbG+zt7bl+/braM1AqGe7u7ly6dInMzEwV2TTxxx9/aNzv+jJr164t0rQcoFOnTujr6xf7rI4dO0ZgYCCff/45Xl5eVKxYUaPX8JMnT6qc//7777i4uLzSBIUSa2trevXqxYYNG1iwYAErV64sNk1JnC9WqFCBSpUqqSjtoF1bMDIyom3btixatIhDhw5x4sQJ4uLiAChVqhS+vr7Mnj2bS5cukZKSwsGDB1Wcer3cHopzpFcQNzc3Tp8+rXKt4Lk27a4oHBwcGDhwINu2bWPEiBGsWrWq2DTa1Hu3bt2IiYnR6HwxJydH/i+6t7e31mNDXl6e7P9D2Tdq1qxJYmKiWtkrV66Mjo4Onp6e3Lp1q1Dv95C/d379+vV8/PHHNG3aVOOYW1x/NDIyUrl32bJlMTU1Vbn2ctsriLb19bq4u7tz6tQplWuFjUEFedV3zLtEKO6CN46p/+egKKZpKXQwbaHZlEYgEAgE/14qV65MTk4Oixcv5vr166xfv57ly5cXm87V1ZXu3bvTs2dPtm3bxo0bNzh16hQzZ85k165db0VWExMTVq9eza5du2jXrp1srnzmzBlGjRrFwIEDgfyPYl9fX44ePaqWR1ZWFnfv3lU5SqJAF4ahoSG9evXi4sWLHDlyhJCQEDp37ix/YLq4uLB+/XoSEhI4efIk3bt312plriBz586le/fuNGvWTF4pmzp1KjNnzmTRokVcvXqVuLg4wsPD5X9Ad+vWDYVCQf/+/YmPj2f37t0aV5VTUlK4ffu2VivJ2qDck75w4UL69u3Lb7/9RmpqKseOHePLL79k2rRpQH697N+/n+PHj5OQkMCXX37JX3/9pZZfWloaX3/9NYmJiWzatInFixdrdLKnLZMmTWLHjh0kJydz+fJloqKicHd3f+X8SkJxbWHt2rX88MMP/PHHH1y/fp0NGzZgZGSEk5MTUVFRLFq0iAsXLpCamsq6devIy8vDzc0NU1NTRo4cyfDhw/nxxx+5du0a586dY/Hixfz4449ay/fVV1/xww8/8OOPP5KUlMT06dO5dOmSympoce2uMIYNG8a+ffu4ceMG586dIzY29o3V+7Bhw/Dx8aF58+YsWbKEixcvcv36dTZv3ky9evVkHxn+/v5cvnxZ46r7P//8w927d7l+/br854pTp06pOI6bNGkS69atY+rUqVy+fJmEhAQiIyOZMGECAI0bN6ZRo0Z07NiR/fv3c+PGDfbs2cPevXtV7qWrq0tERAReXl40a9aMu3fvymFvuj9qQtv6el0GDhxIUlIS33zzDYmJiWzcuJG1a9cWm+59vGNKilDcBW8UhY4O+s5VMW//JTomms0odUzMMG//JfrOVVEU4slXIBAIBP9OvLy8mDdvHrNmzaJ69epEREQwc+ZMrdKGh4fTs2dPRowYgZubG+3bt+f06dM4Ojq+NXkDAwM5fvw4enp6dOvWjSpVqtC1a1ceP37M9OnT5Xj9+vUjMjJSzZxz79692NnZqRyffPLJa8tVuXJlOnToQKtWrWjRogWenp4qzv1++OEHHj58SM2aNenRowchISGUK1euxPeZP38+nTt3plmzZly9epV+/fqxevVqwsPD8fDwoHHjxqxdu1Ze+SxdujS//vorcXFxeHt7M378eDWnUACbNm2iRYsWav+9fx2Cg4OJjo7m9u3bBAUFUaVKFfr164eZmZnskG7ChAnUrFkTf39/mjRpgq2tLe3bt1fLq2fPnjx//pw6deowePBghg4dWqSH8eLQ19dn7NixeHp60qhRI3R1dYmMjHzl/EpCcW3BwsKCVatW4ePjg6enJzExMfz6669YWlpiYWHBtm3baNasGe7u7ixfvpxNmzZRrVo1IP+PCBMnTmTmzJm4u7sTEBDArl27tFoJV9K9e3fGjh3LyJEjqVmzJjdu3KB3794q+8KLa3eFkZuby+DBg2XZXF1dS+yhvzAMDAzYv38/o0aNYsWKFdSrV4/atWuzaNEiQkJCqF69OpC/RadmzZps3rxZLQ9fX1/s7Ozw8PBgzJgxssVKQd9P/v7+REVFER0dTe3atalXrx7z589X6Ttbt26ldu3adO3alapVqzJq1CiV38kpKVWqlPz8mjVrJu/Nfhv98WW0ra/XxdHRka1bt/LLL7/g5eXF8uXLC3Ve+jLv4x1TEhTSuzLKL8CTJ08wNzfn8ePHwjndvxTp/3+4ZCVfIOvqefIyn6NjaISBqzcGlWsACKVdIBAI/scQ7+/CkSSJunXrMnz48CL3xQrynVopvdVr2s8tEPj5+WFra8v69evftyhvhF27dvHNN9/wxx9/FPr7yfeF6I//O7xX53SCfy9KpdygkheGrjXl61JurlDYBQKBQPCvQ6FQsHLlSnlfsKBw0tLSGDdunFASBED+HwSWL1+Ov78/urq6bNq0SXaU92+hdevWJCUlcfv27RLt/38XiP74v4NYcRcIBAKBQKAV4v0tEAjeNM+fP6dt27acP3+ezMxM3NzcmDBhgkYv6gLBfxmx4i4QCAQCgUAgEAjeC0ZGRsTExLxvMQSCDx5hsywQCAQCgUAgEAgEAsEHjFDcC0HKyy3yXCAQCAQCgUAgEAgEgneBMJV/CaU39OwbcWRdv4iU/RyFvhEGFb3Qr+AJCG/oAoFAIBAIBAKBQCB4dwgNtACSJJFzK5EHEd/y9MB6sm9cIud2Etk3LvH0wHoeRHxLzq1E3oM/P4FAIBAIBB84EydOfK1/fWvLlClTqFGjxlvJ+9ChQygUCh49evTG8kxJSUGhUHDhwgUA4uPj+eijj3j27Nkbu8ebpEmTJgwbNqzIOM7OzixYsOCdyCP430ab9qRQKPjll18A9f6iibfRTz9UGjVqxMaNG9/5fYt7DtnZ2Tg7O3PmzJl3JpNQ3P8/Ul4eOTev8GTfGqTnTzXHef6UJ/vWkHPzirwyLxAIBAKBQJ3evXujUCjUjuTk5PctWok5f/48n376KTY2NhgaGuLi4kL//v25evWqHOfu3bssXLiQ8ePHy9cKq4OAgID3UYwPhqpVq1KvXj3mzZunVfzevXuTkpKiVdzY2FhatWqFpaUlxsbGVK1alREjRnD79u3XkFid06dPv5NJmiZNmhQbR6nEKQ8bGxs6duzI9evX37p8/1a0qXfIV95mz56Nl5cXxsbGWFlZ4ePjQ3h4ODk5OVrf786dO7Rs2fIVpf3fZOvWrTRp0gRzc3NKly6Np6cn3377LQ8ePJDj7Ny5k7/++ovPPvtMvubs7Cy3dSMjI5ydnencuTMHDx58p/Lr6+szcuRIRo8e/c7uKRT3Ajz97SeQilHIpbz8eAKBQCAQCIokICCAO3fuqBwVKlQocT65ubnkvacJ86ioKOrVq0dWVhYREREkJCSwYcMGzM3NmThxohxv9erVNGjQACcnJ5X0mupg06ZN77oYWlESReN16dOnD8uWLePFixcawx88eMCSJUtUrByvXbtGREREoXmuWLECX19fbG1t2bp1K/Hx8SxfvpzHjx8TFhb2RuW3trbG2Nj4jeap5NixY2pe1mNiYjh+/HiR6RITE/nzzz/ZsmULly9fpm3btuTm/nd9NJW0PUdFRXHu3DmVa5GRkSoTdAXJzs7G39+f7777jgEDBnD8+HFOnTrF4MGDWbx4MZcvX9b63ra2thgYGJRI3qJ4l2PmoUOHcHZ2LlGa8ePH06VLF2rXrs2ePXv4448/CAsL4+LFi6xfv16Ot2jRIvr06YPOS9uUv/32W+7cuUNiYiLr1q3DwsICX19fQkND30SRtKZ79+4cPXq0RM/6dRCKO/mO57JvXCp0pV0t/vOn+fGFwzqBQCAQCArFwMAAW1tblUNXV5d58+bh4eGBiYkJDg4OBAcHk56eLqdbu3YtFhYW7Ny5k6pVq2JgYEBaWhpZWVmMHDmS8uXLY2JiQt26dTl06NBbkz8jI4M+ffrQqlUrdu7cia+vLxUqVKBu3brMnTuXFStWyHEjIyNp27atVnVQpkwZOVyhULBixQratGmDsbEx7u7unDhxguTkZJo0aYKJiQkNGjTg2rVranmvWLECBwcHjI2N6dy5M48fP5bDTp8+jZ+fH1ZWVpibm9O4cWM1pUShULBs2TLatWuHiYmJxo/ejIwMWrZsiY+Pj2yWu3r1atzd3TE0NKRKlSosXbpUJc2pU6fw9vbG0NCQWrVqcf78ebV8/fz8ePDgAb/99pvGujc0NOT27dsEBARw69Ytli9fTu/evQud+Ll16xYhISGEhISwZs0amjRpgrOzM40aNWL16tVMmjQJgH/++YeuXbtSvnx5jI2N8fDw0DiR8uLFC4YMGYK5uTlWVlZMnDhRZRLhZVN5hULB6tWrCQoKwtjYGBcXF3bu3CmHP3z4kO7du2NtbY2RkREuLi6Eh4drLIujoyMrVqwgODiYp0+fEhwczMqVK3FwcNAYX0m5cuWws7OjUaNGTJo0ifj4eJKTk4ttC5IkMWXKFBwdHTEwMMDe3p6QkBA5fOnSpbi4uGBoaIiNjQ2dOnWSw/Ly8pg5cyYVKlTAyMgILy8vfv75ZzlcaQ1w4MABatWqhbGxMQ0aNCAxMVFF9unTp1OuXDlMTU3p168fY8aMUdsOUlS7U5o1//TTTzRu3BhDQ0MiIiJITU2lbdu2lClTBhMTE6pVq8bu3bs11l/FihUZO3YskydP5tGjR3Tu3JnY2FisrKw0xl+wYAGHDx/mwIEDDB48mBo1alCxYkW6devGyZMncXFxUamnUaNGUbZsWWxtbZkyZYpKXgVN5TWxe/duXF1dMTIyomnTpmoWKK86ZirT7du3D3d3d0qXLi1PNr4tTp06xYwZMwgLC2POnDk0aNAAZ2dn/Pz82Lp1K7169QLg77//5uDBgxrHVVNTU2xtbXF0dKRRo0asXLmSiRMnMmnSJJW29ccff9CyZUtKly6NjY0NPXr04P79+3J4Xl4es2fPpnLlyhgYGODo6Fio8p+bm8sXX3xBlSpVSEtLA6BMmTL4+PgQGRn5JquoUN6rc7qcnJx3OrtbGHp6emRdv1iiNFk3LmFQqcYHIb9AIBAIBO8C5TvvyZMnKtcNDAxKtFqko6PDokWLqFChAtevXyc4OJhRo0apfIhnZGQwa9YsVq9ejaWlJeXKlWPIkCHEx8cTGRmJvb0927dvJyAggLi4OJWP5JKQl5entpqjZN++fdy/f59Ro0ZpDLewsADyV4fj4+OpVavWK8kwbdo05s2bx7x58xg9ejTdunWTlQhHR0e++OILhgwZwp49e+Q0ycnJbN68mV9//ZUnT57Qt29fgoOD5RXpp0+f0qtXLxYvXowkSYSFhdGqVSuSkpIwNTWV85kyZQrfffcdCxYsoFSpUirm1Y8ePaJ169aULl2a/fv3Y2xsTEREBJMmTeL777/H29ub8+fP079/f0xMTOjVqxfp6em0adMGPz8/NmzYwI0bNxg6dKhamfX19alRowZHjhyhefPmauHGxsbMmDGD3bt3065dO168eMHBgwfR09PTWIdbtmwhOzu72GeVmZnJxx9/zOjRozEzM2PXrl306NGDSpUqUadOHTn+jz/+SN++fTl16hRnzpxhwIABODo60r9//0Kf49SpU5k9ezZz5sxh8eLFdO/endTUVMqWLcvEiROJj49nz549WFlZkZyczPPnzzXm4+DgwJYtWxg7diznzp3D399fbXKkOIyMjID8VeHi2sLWrVuZP38+kZGRVKtWjbt373LxYv538ZkzZwgJCWH9+vU0aNCABw8ecOTIEfk+M2fOZMOGDSxfvhwXFxcOHz7M559/jrW1NY0bN5bjjR8/nrCwMKytrRk4cCBffPEFx44dAyAiIoLQ0FCWLl0qK0FhYWEqkzTFtTslY8aMISwsTJ446t+/P9nZ2Rw+fBgTExPi4+MpXbq0xjqrWrUq+/bto1u3bly8eJHg4OAit0NERETg6+uLt7e3Wpienp5KW/3xxx/5+uuvOXnyJCdOnKB37974+Pjg5+dX5HMEuHnzJh06dGDw4MEMGDCAM2fOMGLECLV4rzpmZmRkMHfuXNavX4+Ojg6ff/45I0eOLNK65XWIiIigdOnSBAcHawxX9tWjR4/Kk5naMHToUKZNm8aOHTsYNWoUjx49olmzZvTr14/58+fz/PlzRo8erWJWP3bsWFatWsX8+fP55JNPuHPnDleuXFHLOysri65du5KSksKRI0ewtraWw+rUqaPSJ94m71Vxj46OfmtmRtpiZGREixYtkLI1D56FIWXlx4+NjS104BUIBAKB4N9ERkYGgNrK3+TJk9VWkCDf9LTgR3LLli3ZsmWLiqMmZ2dnpk+fzsCBA1WUk5ycHJYuXYqXlxcAaWlphIeHk5aWhr29PQAjR45k7969hIeHM2PGDI0yX7t2jYkTJxITE0OZMmUICgqiR48eVKtWjbi4OMaNG8evv/6qMW1SUhIAVapUKbJe0tLSkCRJlquoOgAYN24c48aNk8/79OlD586dARg9ejT169dn4sSJ+Pv7A/kfpH369FHJIzMzk3Xr1lG+fHkAFi9eTOvWrQkLC8PW1pZmzZqpxF+5ciUWFhb89ttvtGnTRr7erVs3lbyVivvdu3fp0qULLi4ubNy4EX19fSD/WYeFhdGhQwcAKlSoQHx8PCtWrKBXr15s3LiRvLw8fvjhBwwNDalWrRq3bt1i0KBBanVjb29PamqqxjrNzMxkxowZnDx5kiZNmlCrVi18fX2ZM2eOioKtJCkpCTMzM+zs7DTmp6R8+fKMHDlSPv/qq6/Yt28fmzdvVsnXwcGB+fPno1AocHNzIy4ujvnz5xepuPfu3ZuuXbsCMGPGDBYtWsSpU6cICAggLS0Nb29veXKnKNPi27dvM2LECMqUKUPNmjV5+PAhn332GWFhYfLzLoo7d+4wd+5cypcvj5ubGx4eHirhL7eFtLQ0bG1t8fX1RU9PD0dHR7ku0tLSMDExoU2bNpiamuLk5CQrqllZWcyYMYOYmBjq168P5K9aHz16lBUrVqgo7qGhofL5mDFjaN26NZmZmRgaGrJ48WL69u0rt8NJkyYRHR2tYoVTXLtTMmzYMDmOUv6OHTvKdVCxYsVC6y0xMZFhw4ZRu3ZtvLy8iImJ4cKFC4SGhqpYyShJSkrSei+8p6cnkydPBsDFxYXvv/+eAwcOaKW4L1u2jEqVKsnbPZTtcdasWSrxXnXMzMnJYfny5VSqVAmAIUOG8O2332pVrlchKSmJihUrFjoJpyQ1NRUbG5tCJ1ZfpmzZspQrV062RlBO8hR8N6xZswYHBweuXr2KnZ0dCxcu5Pvvv5fbUKVKlfjkk09U8k1PT6d169ZkZWURGxuLubm5SnhR49ib5r0q7i1atMDMzOx9iiCj0DcqWXyD/PhNmzZ9G+IIBAKBQPDBoVxpv3nzpsr7u7DV9qZNm7Js2TL53MTEBMjfrztz5kyuXLnCkydPePHiBZmZmWRkZMgT+vr6+nh6espp4+LiyM3NxdXVVeUeWVlZWFpaFirz8OHD8fHxYezYsVy/fp1NmzZRu3ZtcnJysLKyYurUqYWm1fYvMsoJfENDQ7Wwl+sA8j8wC1KwnDY2NgAqypaNjQ2ZmZk8efJErndHR0cVJa5+/frk5eWRmJiIra0tf/31FxMmTODQoUPcu3eP3NxcMjIyZBNPJYVZCfj5+VGnTh1++ukndHV1AXj27BnXrl2jb9++Kgrsixcv5I/ZhIQEPD09VepCqdS9jJGRkTwZ9DIZGRnY2Niwd+9e+vTpw8CBA+nfvz8nTpzQqLhLkoRCodCYV0Fyc3OZMWMGmzdv5vbt22RnZ5OVlaW2kFSvXj2V/OrXr09YWBi5ublyfbxMwedoYmKCmZkZ9+7dA2DQoEF07NiRc+fO0aJFC9q3b0+DBg005pOSkkK/fv3w9fWlSZMmLFu2jJiYGFJSUopU3D/66CMkSSIjIwMvLy+2bt2Kvr5+sW3h008/ZcGCBVSsWJGAgABatWpF27ZtKVWqFH5+fjg5OclhAQEB8naA5ORkMjIy1JTP7OxstVXognWjnFy5d+8ejo6OJCYmqq2+1qlTR14V1abdKXm5PYeEhDBo0CCio6Px9fWlY8eOKrIU5OrVq4SGhlKzZk0OHz7M5s2b2bRpE3///bdGxb0kf5l6+Z52dnZy2yiOhIQE6tatq3JNU5961THT2NhYVtq1la3gZGRubi5ZWVkq1z7//HOWL1+uMW1JxlVNY2pRFBwHLl68SGxsrEYLi2vXrvHo0SOysrI0WvwUpGvXrnz00UccPHhQtmQpSFHj2JvmvSruL5uRvC+kvFwMKnqRfeOS1mkMKngi5eV+EPILBAKBQPAuUL7zzMzMtJp4NzExoXLlyirXUlJSaNOmDYMGDSI0NJSyZcty9OhR+vbtS3Z2tqxAGRkZqShO6enp6OrqcvbsWTXFqTDTV0B2XAT5ynBgYCBZWVk8fPgQW1vbIuVXfvBeuXKlUOUTkPfAPnz4UMWEsrA6eJmC3xLKMmu6VhJnU7169eKff/5h4cKFODk5YWBgQP369cnOzlaTTxOtW7eWHbwpJxGUK6CrVq1SUyQKU2aL4sGDByoKQ0HKli3L4MGDVa5VqlSp0Piurq48fvyYO3fuFLnqPmfOHBYuXMiCBQtkPwvDhg1Tq5dX4eVvQoVCIT+zli1bkpqayu7du9m/fz/Nmzdn8ODBzJ07Vy0fHx8ftWu+vr7F3v/IkSOYmZnJe8WVFNcWHBwcSExMJCYmhv379xMcHMycOXP47bffMDU15dy5cxw6dIjo6GgmTZrElClTOH36tNwedu3apTah8PJk3uu055K0u5fbc79+/fD392fXrl1ER0czc+ZMwsLC+Oqrr9Tuo2kvtdKCQhOurq4azao1UVTbeFO86pipSbbilOuCv0g7efIko0ePVtk7X9T7wdXVlaNHj5KTk1OkHmVlZcXDhw+LlKMg//zzD3///be8xSI9PZ22bduqWSZA/uSEtn9daNWqFRs2bODEiRNqlkyQP469PO6/LYRzOkCho4t+BU8URqbFRwYURqb58XVK/pISCAQCgeC/zNmzZ8nLyyMsLIx69erh6urKn3/+WWw6b29vcnNzuXfvHpUrV1Y5ilLAlUp7QZQO44qjRYsWWFlZMXv2bI3hSmdtlSpVwszMjPj4+GLzfFOkpaWp1Nvvv/+Ojo4Obm5uQL5n8pCQEFq1akW1atUwMDBQccpUHN999x29evWiefPmcrlsbGywt7fn+vXras9A+bHs7u7OpUuXyMzMVJFNE3/88YfG/cEvs3bt2mK9Vnfq1Al9ff1in9WxY8cIDAzk888/x8vLi4oVK2r0Gn7y5EmV899//x0XF5dXmqBQYm1tTa9evdiwYQMLFixg5cqVxaYpifPFChUqUKlSJRWlHbRrC0ZGRrRt25ZFixZx6NAhTpw4QVxcHAClSpXC19eX2bNnc+nSJVJSUjh48KCKE7SX20NxjvQK4ubmxunTp1WuFTzXpt0VhYODAwMHDmTbtm2MGDGCVatWFZtGm3rv1q0bMTExGp0v5uTk8OzZs2Lz0AZ3d3dOnTqlcq2wPlWQVx0ztaFgXuXLl6dUqVIq18qVK1do2m7dupGenl6o3wZlX/X29ubu3btaK+8LFy5ER0eH9u3bA1CzZk0uX76Ms7OzWvlNTExwcXHByMiIAwcOFJnvoEGD+O6772jXrp1GZ5rajmNvgve64v6hYdq4C0/2rSn6l3AKHUwbd3l3QgkEAoFA8C+icuXK5OTksHjxYtq2bcuxY8cKNaksiKurK927d6dnz56y86m///6bAwcO4OnpSevWrd+4rCYmJqxevZpPP/2Udu3aERISQuXKlbl//z6bN28mLS2NyMhIdHR08PX15ejRo/JHo5KsrCzu3r2rcq1UqVKFeqrWFkNDQ3r16sXcuXN58uQJISEhdO7cWf4gd3FxYf369dSqVYsnT57wzTffaDTzLIq5c+eSm5tLs2bNOHToEFWqVGHq1KmEhIRgbm5OQEAAWVlZnDlzhocPH/L111/TrVs3xo8fT//+/Rk7diwpKSkaV5VTUlK4ffu2VivJ2qDckz5kyBCePHlCz549cXZ25tatW6xbt47SpUsTFhaGi4sLP//8M8ePH6dMmTLMmzePv/76i6pVq6rkl5aWxtdff82XX37JuXPnWLx48Wv9Um7SpEl8/PHHVKtWjaysLKKiorR2uvW6FNcW1q5dS25uLnXr1sXY2JgNGzZgZGSEk5MTUVFRXL9+nUaNGlGmTBl2795NXl4ebm5umJqaMnLkSIYPH05eXh6ffPIJjx8/5tixY5iZmansPS+Kr776iv79+1OrVi0aNGjATz/9xKVLl1T2oxfX7gpj2LBhtGzZEldXVx4+fEhsbOwbq/dhw4axa9cumjdvzrRp0/jkk08wNTXlzJkzzJo1ix9++EHNM/6rMHDgQMLCwvjmm2/o168fZ8+eZe3atcWmex9jpjbUrVuXUaNGMWLECG7fvk1QUBD29vYkJyezfPlyPvnkE4YOHYq3tzdWVlYcOz/1ky0AAC3mSURBVHZMxS8H5DvfvHv3Ljk5Ody4cYMNGzawevVqZs6cKVs4DR48mFWrVtG1a1fZo39ycjKRkZGsXr0aQ0NDRo8ezahRo9DX18fHx4e///6by5cv07dvX5X7ffXVV+Tm5tKmTRv27Nmjsg/+yJEjTJs27e1XHID0Hnj8+LEESI8fP34fty+UvLw8KSs1Xrq/brL094qv1Y776yZLWanxUl5e3vsWVSAQCASCd05J3t+9evWSAgMDNYbNmzdPsrOzk4yMjCR/f39p3bp1EiA9fPhQkiRJCg8Pl8zNzdXSZWdnS5MmTZKcnZ0lPT09yc7OTgoKCpIuXbr0GqUqntOnT0sdOnSQrK2tJQMDA6ly5crSgAEDpKSkJDnO7t27pfLly0u5ubnytV69ekmA2uHm5ibHAaTt27fL5zdu3JAA6fz58/K12NhYlfqZPHmy5OXlJS1dulSyt7eXDA0NpU6dOkkPHjyQ05w7d06qVauWZGhoKLm4uEhbtmyRnJycpPnz5xd6b033kiRJ+uqrryQ7OzspMTFRkiRJioiIkGrUqCHp6+tLZcqUkRo1aiRt27ZNjn/ixAnJy8tL0tfXl2rUqCFt3bpVrUwzZsyQ/P39tan+ErF//37J399fKlOmjGRoaChVqVJFGjlypPTnn39KkiRJ//zzjxQYGCiVLl1aKleunDRhwgSpZ8+eKm21cePGUnBwsDRw4EDJzMxMKlOmjDRu3DiV7z9t6tLc3FwKDw+XJEmSpk2bJrm7u0tGRkZS2bJlpcDAQOn69etvpMyanllBimsL27dvl+rWrSuZmZlJJiYmUr169aSYmBhJkiTpyJEjUuPGjaUyZcpIRkZGkqenp/TTTz/Jeefl5UkLFiyQ3NzcJD09Pcna2lry9/eXfvvtt0JlO3/+vARIN27ckK99++23kpWVlVS6dGnpiy++kEJCQqR69eqplKOodqep30iSJA0ZMkSqVKmSZGBgIFlbW0s9evSQ7t+//wq1rJnMzExp5syZkoeHh2RoaCiVLVtW8vHxkdauXSvl5ORIkpTfnoYOHaqSLjAwUOrVq5d8XrD9aCrLr7/+KlWuXFkyMDCQGjZsKK1Zs+aNjJma0m3fvl0qiYoYGxsrOTk5aR1fyU8//SQ1atRIMjU1lUxMTCRPT0/p22+/VWkro0aNkj777DOVdE5OTvJYqq+vLzk6OkqdO3eWDh48qHaPq1evSkFBQZKFhYVkZGQkValSRRo2bJjcl3Nzc6Xp06dLTk5Okp6enuTo6CjNmDFDkiTNzyEsLEwyNTWVjh07JkmSJB0/flyysLCQMjIySlz+V0EhSSXwrPCGePLkCebm5jx+/PiDcU6nRPr/+02yb1wi68YlpKznKAyMMKjgiX6FfIcPCi29GwoEAoFA8G/iQ35/v28kSaJu3boMHz68yH2xgnznZUpv9Zr2cwsEfn5+2Nrasn79+vctiuA9cvfuXapVq8a5c+dwcnJ63+Ko0aVLF7y8vFT+EvI2EabyL6FUyvUreGBQqYZ8XcrLFQq7QCAQCAQCjSgUClauXCnvCxYUTlpaGuPGjRNKuwDI/4PA8uXL8ff3R1dXl02bNsmO8gT/bWxtbfnhhx9IS0v74BT37OxsPDw8GD58+Du7p1hxFwgEAoFAoBXi/S0QCN40z58/p23btpw/f57MzEzc3NyYMGGCyv/YBQKBWHEXCAQCgUAgEAgE7wkjIyNiYmLetxgCwQePsP0WCAQCgUAgEAgEAoHgA+Y/rbhLeblFngsEAoFAIBAIBAKBQPC++U+aysue429eJjvtD6Ts5yj0jdB3rI6+Q3VAeI4XCAQCgUAgEAgEAsGHwX9OcZckiZw7SaT/vhUpM10lLDvtDxSGpSldryN69q4oFIr3JKVAIBAIBAKBQCAQCAT5/KeWlaW8PHL+vMrT39arKe1ynMx0nv62npw/r8or8wKBQCAQCATFMXHiRAYMGPBe7u3s7MyCBQsKDf/ss88ICwt7dwIVoDjZUlJSUCgUXLhwAYBDhw6hUCh49OhRoWnWrl2LhYXFG5XzQyQ7O5vKlStz/Pjxt36v4p7T69C7d2/at2//RvOcMmUKNWrUkM/HjBnDV1999Ubv8aZ4uY1rQpt2L/hv859S3AHSf98KUjEKuZRH+u/b3o1AAoFAIBD8C+nduzcKhULtSE5Oft+ilZjz58/z6aefYmNjg6GhIS4uLvTv35+rV6/Kce7evcvChQsZP368fK1gHejp6WFjY4Ofnx9r1qwh7x0vDkyYMIHQ0FAeP35cbNyUlBR69+6tVb5Pnjxh/PjxVKlSBUNDQ2xtbfH19WXbtm1o+8dhBwcH7ty5Q/Xq1bWK/29AkiRWrlxJ3bp1KV26NBYWFtSqVYsFCxaQkZEhx1u+fDkVKlSgQYMG8jVN/UqhUBAZGfk+ivLBMHLkSH788UeuX7+uVfwmTZpoFS87O5vZs2fj5eWFsbExVlZW+Pj4EB4eTk5OzmtIrEqDBg24c+cO5ubmbyxPTRw6dIgpU6YUG6/g+KWvr0/lypX59ttvefHixVuVT1A4/xnFXcrLJfvmH4WutKvFz3xK9s3LwmGdQCAQCASvSEBAAHfu3FE5KlSoUOJ8cnNz37miqyQqKop69eqRlZVFREQECQkJbNiwAXNzcyZOnCjHW716NQ0aNMDJyUklvbIOUlJS2LNnD02bNmXo0KG0adPmnX4AV69enUqVKrFhw4ZC40RERHDt2jX5XJIklixZwsOHDzXGf/ToEQ0aNGDdunWMHTuWc+fOcfjwYbp06cKoUaO0miQA0NXVxdbWllKl3twOzuzs7DeWV3GsXbtWayVQSY8ePRg2bBiBgYHExsZy4cIFJk6cyI4dO4iOjgby6//777+nb9++aunDw8PV+tabXtF+E7zLvmtlZYW/vz/Lli0rNE5UVBTnzp1TuRYZGakyCVeQ7Oxs/P39+e677xgwYADHjx/n1KlTDB48mMWLF3P58uU3Jr++vj62trZvbavu8uXLuXfvnnyenZ1NWFhYkZMPyvErKSmJESNGMGXKFObMmfNW5Ptf4F2OK5p4r4p7Tk7OOzsUOrpkp/1RIvmy0/5AoaP7TuUUhzjEIQ5xiONDPiB/lbXgkZWVpfE9amBggK2trcqhq6vLvHnz8PDwwMTEBAcHB4KDg0lP/7+JdaUZ9M6dO6latSoGBgakpaWRlZXFyJEjKV++PCYmJtStW5dDhw698ndIcWRkZNCnTx9atWrFzp078fX1pUKFCtStW5e5c+eyYsUKOW5kZCRt27YttA7Kly9PzZo1GTduHDt27GDPnj2sXbtWjvfo0SP69euHtbU1ZmZmNGvWjIsXL6rk9euvv1K7dm0MDQ2xsrIiKCioUNlXr16NhYUFBw4ckK+1bdu2yFXZChUq0KtXL5YvX86tW7cICAjg9u3bGBgYaIw/btw4UlJSOHnyJL169aJq1aq4urrSv39/Lly4QOnSpVXq8osvvsDU1BRHR0dWrlwph2ljRrx27VocHR0xNjYmKCiIf/75RyVcaTa9evVqKlSogKGhIVB8vSrTrV+/HmdnZ8zNzfnss894+vRpobK8Lps3byYiIoJNmzYxbtw4ateujbOzM4GBgRw8eJCmTZsCcPbsWa5du0br1q3V8rCwsFDrW8oyK/tPVFQUbm5uGBsb06lTJzIyMvjxxx9xdnamTJkyhISEkJurukD19OlTunbtiomJCeXLl2fJkiUq4a/ad1/m9OnTWFtbM2vWLEC79v/dd99hY2ODqakpffv2JTMzUy3f4tp4xYoVGTt2LJMnT+bRo0d07tyZ2NhYrKysNMZfsGABhw8f5sCBAwwePJgaNWpQsWJFunXrxsmTJ3FxcQFg7969fPLJJ1hYWGBpaUmbNm1UJsGUXLlyhQYNGmBoaEj16tX57bff5LCXTeWVdblv3z7c3d0pXbq0rEgXTFOnTh1MTEywsLDAx8eH1NRUjWVxcHCgXbt2bN++ncuXL9OsWTOAIicKlOOXk5MTgwYNwtfXl507dwLFt4XU1FTatm1LmTJlMDExoVq1auzevRuAhw8f0r17d6ytrTEyMsLFxYXw8HA57c2bN+ncuTMWFhaULVuWwMBAUlJS5HDl1ou5c+diZ2eHpaUlgwcPlt9RAHfu3KF169YYGRlRoUIFNm7cqLYdRNvx4eVx5eeff8bDwwMjIyMsLS3x9fXl2bNnhdbjm+K9OqeLjo7G2Nj4rd/HyMiIFi1aIGU/L1E6ZfzY2FiePy9ZWoFAIBAI/m0oTXgdHBxUrk+ePFkr00slOjo6LFq0iAoVKnD9+nWCg4MZNWoUS5cuVbnXrFmzWL16NZaWlpQrV44hQ4YQHx9PZGQk9vb2bN++nYCAAOLi4uQP6JKSl5eHTiF/ktm3bx/3799n1KhRGsOVe6wfPHhAfHw8tWrV0uqezZo1w8vLi23bttGvXz8APv30U4yMjNizZw/m5uasWLGC5s2bc/XqVcqWLcuuXbsICgpi/PjxrFu3juzsbPkj+GVmz57N7NmziY6Opk6dOvL1OnXqEBoaSlZWlkZlvEGDBsTGxuLr68uxY8f49ddfadmypcZ75OXlERkZSffu3bG3t1cLL6i0A4SFhTFt2jTGjRvHzz//zKBBg2jcuDFubm7F1tfJkyfp27cvM2fOpH379uzdu5fJkyerxUtOTmbr1q1s27YNXV1doPh6Bbh27Rq//PILUVFRPHz4kM6dO/Pdd98RGhparGyvQkREBG5ubgQGBqqFKRQK2VT6yJEjuLq6YmpqWuJ7ZGRksGjRIiIjI3n69CkdOnQgKCgICwsLdu/ezfXr1+nYsSM+Pj506dJFTjdnzhzGjRvH1KlT2bdvH0OHDsXV1RU/Pz/g1ftuQQ4ePEiHDh2YPXu27BOiuOe0efNmpkyZwpIlS/jkk09Yv349ixYtomLFiip516lTh1u3bpGSkoKzs7NavVStWpV9+/bRrVs3Ll68SHBwcJF+KSIiIvD19cXb21stTE9PDz09PQCePXvG119/jaenJ+np6UyaNImgoCAuXLigMr588803LFiwgKpVqzJv3jzatm3LjRs3sLS0LPQ5zp07l/Xr16Ojo8Pnn3/OyJEjiYiI4MWLF7Rv357+/fuzadMmsrOzOXXqVKGKeOvWrfnkk0+oV68eaWlpHD16VGO5isLIyEieNCuuLQwePJjs7GwOHz6MiYkJ8fHx8rgwceJE4uPj2bNnD1ZWViQnJ8u6Vk5ODv7+/tSvX58jR45QqlQppk+fTkBAAJcuXUJfXx/I18/s7OyIjY0lOTmZLl26UKNGDfr37w9Az549uX//PocOHUJPT4+vv/5axeIAtBsfXh5X7ty5Q9euXZk9ezZBQUE8ffqUI0eOaL016LWQ3gOPHz+WAOn+/ftSdnb2OzkkSZKeHI6Q7m8Yq/Xx5PBGSZKkdyajOMQhDnGIQxwf8nH//n0JkG7evCk9fvxYPjIzM9Xe9b169ZJ0dXUlExMT+ejUqZPG74ItW7ZIlpaW8nl4eLgESBcuXJCvpaamSrq6utLt27dV0jZv3lwaO3Zsod8cycnJUteuXSVra2vJ1dVVGj16tPTHH39IkiRJly5dktq0aVNo2lmzZkmA9ODBg0LjSJIknT9/XgKktLQ0leu9evWSAgMDNabp0qWL5O7uLkmSJB05ckQyMzNTq8dKlSpJK1askCRJkurXry917969UBmcnJyk+fPnS6NGjZLs7OzkMhbk4sWLEiClpKRozOP333+XGjZsKI0ePVpq3ry51KJFC2nixInS8+fP1eL+9ddfEiDNmzevUJkKyvb555/L53l5eVK5cuWkZcuWSZIkSTdu3JAA6fz585IkSVJsbKwESA8fPpQkSZK6du0qtWrVSiXPLl26SObm5vL55MmTJT09PenevXvyNW3qdfLkyZKxsbH05MkTOfybb76R6tatW2y5lISHh0uNGzfWOr67u7vUrl27YuMNHTpUatasmdp1QDI0NFTpWyYmJlJqaqosDyAlJyfLab788kvJ2NhYevr0qXzN399f+vLLL+VzJycnKSAgQOVeXbp0kVq2bFmojNr0XUn6v76wbds2qXTp0lJkZKQcpm37Dw4OVgmvW7eu5OXlpXJNqWMcOnRIo7xXrlyRAgICpIkTJ0peXl7Sp59+Kg0aNKjQPm5kZCSFhIQUWv7C+PvvvyVAiouLkyTp/9r4d999J8fJycmRPvroI2nWrFmSJKm3e03PccmSJZKNjY0kSZL0zz//FFnWl9mzZ49Ur149KSQkROrUqZP0ySefSAsWLJBevHihMX7B8SsvL0/av3+/ZGBgII0cOVJj/JfbgoeHhzRlyhSNcdu2bSv16dNHY9j69eslNzc3KS8vT76WlZUlGRkZSfv27ZNlc3JyUpH9008/lbp06SJJkiQlJCRIgHT69Gk5PCkpSQKk+fPnS5Kk/fjw8rhy9uzZIsfRt8l7XXEvOFP1tpHyctF3rF4ic3l9x+pIebnvTEaBQCAQCD5klO9DMzMzzMzMio3ftGlTlf2mJiYmAMTExDBz5kyuXLnCkydPePHiBZmZmWRkZMiWePr6+nh6espp4+LiyM3NxdXVVeUeWVlZha5WAQwfPhwfHx/Gjh3L9evX2bRpE7Vr1yYnJwcrKyumTp1aaFpJyxUU5UqR0oxSGyRJklfGLl68SHp6ulo5nj9/LpvbXrhwQV5JKoywsDCePXvGmTNn1FYiIX+1DFBxflaQpKQkwsPD0dXVZcqUKYSHh7N06VIyMjLUyqZt3Sgp+CwVCgW2trZqq1+FkZCQoLYtoH79+uzdu1flmpOTE9bW1vK5NvUK+d7UC65q29nZFSlbWloaVatWlc9fvHhBTk6OipXBuHHjGDdunMb0JWlXhbWp+fPn4+vrq3KtoOWDsbExlSpVks9tbGxwdnZWkdHGxkatnPXr11c7L2ha/Cp9V8nJkyeJiori559/VtmPr81zSkhIYODAgWqyxcbGqlwrro1fvXqV0NBQatasyeHDh9m8eTObNm3i77//pkyZMmrxtX1WSUlJTJo0iZMnT3L//n15X39aWpqK08WC9VuqVClq1apFQkJCofm+/BwLts2yZcvSu3dv/P398fPzw9fXl86dO2NnZ6cxrxs3brBjxw7i4+M5dOgQERERLFq0iLy8PNlC5WWioqIoXbo0OTk55OXl0a1bN9m6qri2EBISwqBBg4iOjsbX15eOHTvK7WLQoEF07NiRc+fO0aJFC9q3by87YLx48SLJyclqliaZmZkq/bZatWoqctvZ2REXFwdAYmIipUqVombNmnJ45cqVVZ6xtuPDy+OKl5cXzZs3x8PDA39/f1q0aEGnTp00tp83zX/mP+4KHV30HaqjMCytlYM6haEp+g7VUBRiPicQCAQCgaBoTExMqFy5ssq1lJQU2rRpw6BBgwgNDaVs2bIcPXqUvn37kp2dLX/8GxkZqZh8pqeno6ury9mzZ9U+Ml82yy7IunXrZJN2Dw8PAgMDycrK4uHDh9ja2hYpv3KS4MqVK2oKTUGU+2MfPnyo8oFXFAkJCbKjvvT0dOzs7DTu11fKrlRIiqJhw4bs2rWLzZs3M2bMGLXwBw8eABQq4+effw4g7yVVKBQMHjxYY1xra2ssLCy4cuVKsXIBaosgCoXijTstU04MKdGmXl9FNnt7e5X9+Nu2bWPr1q1ERETI15RmtppwdXXVqt6srKxkReRlbG1t1fpWQTSV6XWfwav2XSWVKlXC0tKSNWvW0Lp1a1kebZ+TNhTXxjX5oejatWuh+Wn7rNq2bYuTkxOrVq3C3t6evLw8qlev/trOzDQ9s4KTCeHh4YSEhLB3715++uknJkyYwP79+6lXr55aXoMGDQIgPj4eyJ9gGTlyZJH3V06+6uvrY29vLzuQ1KYt9OvXD39/f3bt2kV0dDQzZ84kLCyMr776ipYtW5Kamsru3bvZv38/zZs3Z/DgwcydO5f09HQ+/vhjlf6kpOBzfd32rG27e3lc0dXVZf/+/Rw/fpzo6GgWL17M+PHjOXny5Cs5Xy0J/zmttHS9jqAoptgKHUrX6/BuBBIIBAKB4D/E2bNnycvLIywsjHr16uHq6sqff/5ZbDpvb29yc3O5d+8elStXVjmKUsA1ffgrHS4VR4sWLbCysmL27Nkaw5VOpCpVqoSZmZn8QVwcBw8eJC4ujo4dOwJQs2ZN7t69S6lSpdTKppwU8PT0VHE0p4k6deqwZ88eZsyYwdy5c9XC//jjDz766KNCHXEpcXZ2VnGcpwkdHR0+++wzIiIiND6/9PT0N+Y1393dnZMnT6pc+/3334tNp029vgov51euXDmMjIxUrhWluHfr1o2rV6+yY8cOtTBJkmRv/N7e3ly5cuXd7J39/7xcr7///jvu7u7Aq/ddJVZWVhw8eJDk5GQ6d+4sOxLT5jlp2wb++OMP9PT0qFatWrHyaOPYslu3bsTExHD+/Hm1sJycHJ49e8Y///xDYmIiEyZMoHnz5ri7uxf6J4aCMr948YKzZ8/K9fuqeHt7M3bsWI4fP0716tXZuHFjkfGbNGmitU8S5eSro6Ojyl8ftG0LDg4ODBw4kG3btjFixAhWrVolh1lbW9OrVy82bNjAggULZIeVNWvWJCkpiXLlyqm1B21/lefm5saLFy9UnltycrLKc3md8UGhUODj48PUqVM5f/48+vr6bN++XSvZXof/lOKu0NFBz94V08Y9UBhqdvShMDTFtHEP9OxdxWq7QCAQCARvmMqVK5OTk8PixYu5fv0669evZ/ny5cWmc3V1pXv37vTs2ZNt27Zx48YNTp06xcyZM9m1a9dbkdXExITVq1eza9cu2rVrR0xMDCkpKZw5c4ZRo0bJprs6Ojr4+vpy9OhRtTyysrK4e/cut2/f5ty5c8yYMYPAwEDatGlDz549AfD19aV+/fq0b9+e6OhoUlJSOH78OOPHj+fMmTNAvgPATZs2MXnyZBISEoiLi5M9chekQYMG7N69m6lTp6qYOEO+s7MWLVq8sfoJDQ3FwcGBunXrsm7dOuLj40lKSmLNmjV4e3ureJh+HZQrinPnziUpKYnvv/9ezUxeE9rU6/ugc+fOdOnSha5duzJjxgzOnDlDamoqUVFR+Pr6yubfTZs2JT09XeMvxx49esTdu3dVjjfh1frYsWPMnj2bq1evsmTJErZs2cLQoUOBV++7BSlXrhwHDx7kypUrdO3alRcvXmj1nIYOHcqaNWsIDw/n6tWrTJ48WWO9HDlyhIYNG2ploaINw4YNw8fHh+bNm7NkyRIuXrzI9evX2bx5M/Xq1SMpKYkyZcpgaWnJypUrSU5O5uDBg3z99dca81uyZAnbt2/nypUrDB48mIcPH/LFF1+8kmw3btxg7NixnDhxgtTUVKKjo0lKSnrtiQBt0KYtDBs2jH379nHjxg3OnTtHbGysLNukSZPYsWMHycnJXL58maioKDmse/fuWFlZERgYyJEjR7hx4waHDh0iJCSEW7duaSVflSpV8PX1ZcCAAZw6dYrz588zYMAAFWuQVx0fTp48KffbtLQ0tm3bxt9///1O6v29Oqd7/Pjx+7i9lJebK+Xl5kqZKZekJ4c3So9jfpCeHN4oZaZcksMEAoFAIBCoUpL3d1GO2ebNmyfZ2dlJRkZGkr+/v7Ru3To1p0wFHY8pyc7OliZNmiQ5OztLenp6kp2dnRQUFCRdunTpNUpVPKdPn5Y6dOggWVtbSwYGBlLlypWlAQMGSElJSXKc3bt3S+XLl5dyC3xD9OrVSwIkQCpVqpRkbW0t+fr6SmvWrFGJJ0mS9OTJE+mrr76S7O3tJT09PcnBwUHq3r27isO7rVu3SjVq1JD09fUlKysrqUOHDnKY0jmdkt9++00yMTGRFi1aJEmSJD1//lwyNzeXTpw48Ubr5tGjR9KYMWMkFxcXSV9fX7KxsZF8fX2l7du3y86lXpZNkiTJy8tLmjx5siRJxTunkyRJ+uGHH6SPPvpIMjIyktq2bSvNnTtXzTndy47KJKn4etWUbv78+ZKTk5PWdVBS53SSJEm5ubnSsmXLpNq1a0vGxsaSmZmZ9PHHH0sLFy6UMjIy5HidO3eWxowZo5JW2aZePmbOnCnL83L/0VTOl/uok5OTNHXqVOnTTz+VjI2NJVtbW2nhwoUqaV617758rz///FNydXWVOnfuLL148UKr9h8aGipZWVlJpUuXlnr16iWNGjVKrUxubm7Spk2bNNT4q5OZmSnNnDlT8vDwkAwNDaWyZctKPj4+0tq1a6WcnBxJkiRp//79kru7u2RgYCB5enpKhw4dkgBp+/btkiT9XxvfuHGjVKdOHUlfX1+qWrWqdPDgQfk+mpzTvVyX27dvl5Tq2927d6X27dtLdnZ2kr6+vuTk5CRNmjRJbWx5VYoawyWp+LYwZMgQqVKlSpKBgYFkbW0t9ejRQ7p//74kSZI0bdo0yd3dXTIyMpLKli0rBQYGStevX5fzvnPnjtSzZ0/JyspKMjAwkCpWrCj1799ffvdokm3o0KEq/fDPP/+UWrZsKRkYGEhOTk7Sxo0bpXLlyknLly+X47zK+BAfHy/5+/vL7wNXV1dp8eLFJazdV0MhSe/Q/ub/8+TJE8zNzXn8+LFWzm3eFlJeLgod3ULPBQKBQCAQ/B8fyvv7Q0SSJOrWrcvw4cOL3DP7vli2bBnbt28nOjr6fYsiKAGXLl3Cz8+Pa9euFenLQQB79uxhxIgRXLp0ScWsWyAAuHXrFg4ODsTExNC8efP3Lc4r8Z+2BX9ZSRdKu0AgEAgEgldBoVCwcuXKN7av+02jp6fH4sWL37cYghLi6enJrFmzuHHjxvsW5YPn2bNnhIeHC6VdAOT7Etm5cyc3btzg+PHjfPbZZzg7O9OoUaP3Ldor815W3B8/foyFhQU3b94UM/YCgUAgEPyP8OTJExwcHHj06JHWToIEAoFAIHjX7Nu3jxEjRnD9+nVMTU1p0KABCxYswMnJ6X2L9sq8F8VdaaogEAgEAoHgf4+bN2/y0UcfvW8xBAKBQCD4z/BeFPe8vDz+/PNPTE1NNf7n8VVRrgT811byRblFuf8LiHL/d8r9Xywz/G+UW5Iknj59ir29PTrizysCgUAgELwz3ssmEB0dnbc6U29mZvbBfvS8TUS5/1uIcv+3+C+W+79YZvjwyy1M5AUCgUAgePeI6XKBQCAQCAQCgUAgEAg+YITiLhAIBAKBQCAQCAQCwQfMv0pxNzAwYPLkyRgYGLxvUd4potyi3P8FRLn/O+X+L5YZ/rvlFggEAoFAUDzvxTmdQCAQCAQCgUAgEAgEAu34V624CwQCgUAgEAgEAoFA8G9DKO4CgUAgEAgEAoFAIBB8wAjFXSAQCAQCgUAgEAgEgg8YobgLBAKBQCAQCAQCgUDwAfPBKe6HDx+mbdu22Nvbo1Ao+OWXXwqNO3DgQBQKBQsW/L927j8m6vqPA/jz6PSAiJ+CAiYgJvZLZdqYzBosFRzL1dQ5VCBGhrWhWwyc2krZJNIZFWnaus4yF9KYsyHGKCkyQQzvjqBkaQHjl24iim1xXLy+f3zHZ51cccgPP3c+H9tn897v1+d8P32zz3jdx/u8azPe09ODDRs2wNvbG76+vsjIyMDt27dtahoaGvD000/D3d0dDz/8MPbu3TsBaRw3Hrn37NmD2NhYeHp6wtfX1+65bW1tSEpKgqenJ4KCgpCTkwOr1Tp+QUZprLlbWlqQkZGBiIgIeHh4IDIyEm+++SYsFovNua6436tWrcKsWbPg7u6O4OBgpKSkoLOz06bGFXMP6e/vx8KFC6HRaGAymWzmXDF3eHg4NBqNzVFQUGBTo6bc47XXp06dQkxMDDw8PODn54fnn3/eZt7VrmnffffdsH0eOi5cuKDUqWmviYiIaOKprnH/888/sWDBAhw4cOA/606cOIHa2lqEhIQMm9uwYQOamppQWVmJsrIyVFdX4+WXX1bmb926hRUrViAsLAz19fXYt28fdu3ahY8++mjc8zhqPHJbLBasXbsWr7zyit1z//77byQlJcFiseDcuXP49NNPceTIEbzxxhvjkuFujDX3pUuXMDg4iMOHD6OpqQmFhYU4dOgQduzYodS46n7Hx8ejpKQEzc3NKC0txZUrV7BmzRpl3lVzD8nNzbU778q58/Ly0NXVpRxZWVnKnNpyj0fm0tJSpKSkID09HWazGT/++CPWr1+vzLviNS02NtZmj7u6uvDSSy8hIiICixcvBqC+vSYiIqJJICoGQE6cODFsvL29XUJDQ6WxsVHCwsKksLBQmfvll18EgFy4cEEZO336tGg0Guno6BARkYMHD4qfn5/09/crNdu2bZOoqKgJyzIad5P7nwwGg/j4+AwbLy8vFzc3N+nu7lbGPvzwQ/H29rb5t7hXxpp7yN69eyUiIkJ57er7PeTkyZOi0WjEYrGIiGvnLi8vl3nz5klTU5MAEKPRqMy5au6RfgbUnPtuMg8MDEhoaKh8/PHH//q+98M1zWKxSGBgoOTl5Sljat5rIiIimhiqu+M+ksHBQaSkpCAnJwePP/74sPmamhr4+voqdyYAYNmyZXBzc8P58+eVmmeeeQZTp05VahISEtDc3IwbN25MfIi7MFJuR9TU1ODJJ5/E9OnTlbGEhATcunULTU1N47XUcXU3uW/evAl/f3/l9f2w3z09PTh27BhiY2MxZcoUAK6b++rVq9i0aROOHj0KT0/PYfOumhsACgoKEBAQgOjoaOzbt8/mv4Q7W+6RMl+8eBEdHR1wc3NDdHQ0goODsXLlSjQ2Nio198M17auvvsL169eRnp6ujDnbXhMREdHYOV3j/vbbb0Or1WLLli1257u7uxEUFGQzptVq4e/vj+7ubqXmn7/oAVBeD9WozUi5HXE/5L58+TKKioqQmZmpjLly7m3btuHBBx9EQEAA2tracPLkSWXOFXOLCF588UVs3rzZ5sO5f3LF3ACwZcsWFBcXo6qqCpmZmcjPz0dubq4y72y5R8r8+++/AwB27dqF119/HWVlZfDz80NcXBx6enoAOF9mYPTXNL1ej4SEBMycOVMZc8bcRERENDbae72A0aivr8d7772HixcvQqPR3OvlTBrmdix3R0cHEhMTsXbtWmzatGkSVjgxRpM7JycHGRkZaG1txe7du5GamoqysjKn/DlxJHdRURH6+vqwffv2SV7dxHF0v1977TXlz/Pnz8fUqVORmZmJt956CzqdbjKWOm4cyTw4OAgA2LlzJ1avXg0AMBgMmDlzJr788kubD+ecxWivae3t7aioqEBJSckkrI6IiIjUzKnuuP/www+4du0aZs2aBa1WC61Wi9bWVmRnZyM8PBwAMGPGDFy7ds3mPKvVip6eHsyYMUOpuXr1qk3N0OuhGjVxJLcjXDl3Z2cn4uPjERsbO+wBTa6ce9q0aZg7dy6WL1+O4uJilJeXo7a2FoBr5j5z5gxqamqg0+mg1WoxZ84cAMDixYuRlpYGwDVz2xMTEwOr1YqWlhYAzpXbkczBwcEAgMcee0w5T6fTYfbs2WhrawPgXJmB0e+1wWBAQEAAVq1aZTPubLmJiIho7JyqcU9JSUFDQwNMJpNyhISEICcnBxUVFQCAJUuWoLe3F/X19cp5Z86cweDgIGJiYpSa6upqDAwMKDWVlZWIioqCn5/f5IZygCO5HbFkyRL8/PPPNh9sVFZWwtvb2+aXY7VwNHdHRwfi4uKwaNEiGAwGuLnZ/ljfL/s9dIeyv78fgGvmfv/992E2m5X58vJyAMDx48exZ88eAK6Z2x6TyQQ3Nzflq0HOlNuRzIsWLYJOp0Nzc7Ny3sDAAFpaWhAWFgbAda9pwP+/FmIwGJCamqo8t2KIM+01ERERjZN7/XS8O/X19YnRaBSj0SgA5J133hGj0Sitra126+09kTcxMVGio6Pl/PnzcvbsWXnkkUckOTlZme/t7ZXp06dLSkqKNDY2SnFxsXh6esrhw4cnMtp/Go/cra2tYjQaZffu3eLl5aW8X19fn4iIWK1WeeKJJ2TFihViMpnk66+/lsDAQNm+fftEx/tXY83d3t4uc+bMkWeffVba29ulq6tLOYa44n7X1tZKUVGRGI1GaWlpkW+//VZiY2MlMjJS/vrrLxFxzdx3+uOPP4Y9Vd4Vc587d04KCwvFZDLJlStX5PPPP5fAwEBJTU1VatSWezz2euvWrRIaGioVFRVy6dIlycjIkKCgIOnp6RER17ymDfnmm28EgPz666/D5tS210RERDTxVNe4V1VVCYBhR1pamt16e7/0XL9+XZKTk8XLy0u8vb0lPT1daV6HmM1mWbp0qeh0OgkNDZWCgoIJSuSY8cidlpZm9z2qqqqUmpaWFlm5cqV4eHjItGnTJDs7WwYGBiYu2AjGmttgMNg9/87PpFxtvxsaGiQ+Pl78/f1Fp9NJeHi4bN68Wdrb223Oc7Xcd7LXuIu4Xu76+nqJiYkRHx8fcXd3l0cffVTy8/OVD2mGqCn3eOy1xWKR7OxsCQoKkoceekiWLVsmjY2NNjWudk0bkpycLLGxsf/696hpr4mIiGjiaURExuPOPRERERERERGNP6f6jjsRERERERHR/YaNOxEREREREZGKsXEnIiIiIiIiUjE27kREREREREQqxsadiIiIiIiISMXYuBMRERERERGpGBt3IiIiIiIiIhVj405ERERERESkYmzciYiIiIiIiFSMjTsRjUpNTQ0eeOABJCUl2Yy3tLRAo9HAZDLZjJeWliIuLg4+Pj7w8vLC/PnzkZeXh56enklcNRERERGR82LjTkSjotfrkZWVherqanR2dv5n7c6dO7Fu3To89dRTOH36NBobG7F//36YzWYcPXp0klZMREREROTctPd6AUTkPG7fvo3jx4/jp59+Qnd3N44cOYIdO3bYra2rq0N+fj7effddbN26VRkPDw/H8uXL0dvbO0mrJiIiIiJybrzjTkQOKykpwbx58xAVFYWNGzfik08+gYjYrT127Bi8vLzw6quv2p339fWdwJUSEREREbkONu5E5DC9Xo+NGzcCABITE3Hz5k18//33dmt/++03zJ49G1OmTJnMJRIRERERuRw27kTkkObmZtTV1SE5ORkAoNVqsW7dOuj1erv1/3YnnoiIiIiIRoffcScih+j1elitVoSEhChjIgKdTocPPvhgWP3cuXNx9uxZDAwM8K47EREREdEY8I47EY3IarXis88+w/79+2EymZTDbDYjJCQEX3zxxbBz1q9fj9u3b+PgwYN235MPpyMiIiIicgzvuBPRiMrKynDjxg1kZGTAx8fHZm716tXQ6/VITEy0GY+JiUFubi6ys7PR0dGBF154ASEhIbh8+TIOHTqEpUuX2jxtnoiIiIiI7NMIv4hKRCN47rnnMDg4iFOnTg2bq6urQ0xMDMxmMxYsWACj0YiFCxcq8yUlJThw4ACMRiMGBwcRGRmJNWvWICsri0+WJyIiIiJyABt3IiIiIiIiIhXjd9yJiIiIiIiIVIyNOxEREREREZGKsXEnIiIiIiIiUjE27kREREREREQqxsadiIiIiIiISMXYuBMRERERERGpGBt3IiIiIiIiIhVj405ERERERESkYmzciYiIiIiIiFSMjTsRERERERGRirFxJyIiIiIiIlKx/wEYWmeQayap+AAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -3478,23 +4249,13 @@
"ax.yaxis.grid(True)\n",
"ax.yaxis.set_ticks_position('right');"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2f483181",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "scientific_python",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "scientific_python"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -3506,7 +4267,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.4"
+ "version": "3.10.12"
},
"toc": {
"base_numbering": 1,
diff --git a/notebooks/statsmodels_cours.ipynb b/notebooks/statsmodels_cours.ipynb
index 33aab83c3de8d692b089924e50002972bea2b024..57c7c3825dfabe51dbba6e3b48defb4befa0da1c 100644
--- a/notebooks/statsmodels_cours.ipynb
+++ b/notebooks/statsmodels_cours.ipynb
@@ -2,18 +2,16 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "ce9d2cf1-9281-4a58-9dcd-3b0b1ab7cb0b",
"metadata": {
- "jupyter": {
- "source_hidden": true
- },
"tags": []
},
"outputs": [],
"source": [
"import sys\n",
- "!\"{sys.executable}\" -m pip install statsmodels"
+ "!\"{sys.executable}\" -m pip install statsmodels\n",
+ "import statsmodels_material"
]
},
{
@@ -21,10 +19,11 @@
"id": "f0b66572-e886-4b73-9a82-2f26a1295280",
"metadata": {},
"source": [
- "<div style=\"text-align: center;\"><img alt=\"StatsModels logo\" src=\"images/statsmodels-logo-v2-horizontal.svg\" width=\"60%\" /></div>\n",
+ "<div style=\"text-align: center; margin-top: 50px; margin-bottom: 50px\"><img alt=\"StatsModels logo\" src=\"images/statsmodels-logo-v2-horizontal.svg\" width=\"60%\" /></div>\n",
+ "\n",
+ "The statsmodels library provides utilities for the design of linear models of one or more response (or dependent) variables as a function of explanatory (or independent) variables.\n",
"\n",
- "The `statsmodels` library stands out for its general modelling approach for designing statistical tests.\n",
- "It leverages the principle of *regression* to represent the association between multiple variables."
+ "It features many modules. We will import two of them:"
]
},
{
@@ -48,15 +47,189 @@
"import numpy as np\n",
"import pandas as pd\n",
"from scipy import stats\n",
+ "import pingouin as pg\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sns"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "047523b9-169d-487c-b4a2-ed29aa6d0e16",
+ "metadata": {},
+ "source": [
+ "For example, we will specify linear models using Wilkinson formulae, *e.g.*:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "79b07451-6f60-43b3-980b-1e8ebd46baaa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sex</th>\n",
+ " <th>Risk</th>\n",
+ " <th>Drug</th>\n",
+ " <th>Cholesterol</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>M</td>\n",
+ " <td>Low</td>\n",
+ " <td>A</td>\n",
+ " <td>4.868845</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>M</td>\n",
+ " <td>Low</td>\n",
+ " <td>B</td>\n",
+ " <td>5.573970</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>M</td>\n",
+ " <td>Low</td>\n",
+ " <td>C</td>\n",
+ " <td>6.507308</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>M</td>\n",
+ " <td>High</td>\n",
+ " <td>A</td>\n",
+ " <td>7.787113</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>M</td>\n",
+ " <td>High</td>\n",
+ " <td>B</td>\n",
+ " <td>6.877862</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>M</td>\n",
+ " <td>High</td>\n",
+ " <td>C</td>\n",
+ " <td>5.320824</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>F</td>\n",
+ " <td>Low</td>\n",
+ " <td>A</td>\n",
+ " <td>4.675374</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35</th>\n",
+ " <td>F</td>\n",
+ " <td>Low</td>\n",
+ " <td>B</td>\n",
+ " <td>6.942870</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>40</th>\n",
+ " <td>F</td>\n",
+ " <td>Low</td>\n",
+ " <td>C</td>\n",
+ " <td>4.659411</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45</th>\n",
+ " <td>F</td>\n",
+ " <td>High</td>\n",
+ " <td>A</td>\n",
+ " <td>5.429768</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50</th>\n",
+ " <td>F</td>\n",
+ " <td>High</td>\n",
+ " <td>B</td>\n",
+ " <td>4.702679</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>55</th>\n",
+ " <td>F</td>\n",
+ " <td>High</td>\n",
+ " <td>C</td>\n",
+ " <td>5.151045</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sex Risk Drug Cholesterol\n",
+ "0 M Low A 4.868845\n",
+ "5 M Low B 5.573970\n",
+ "10 M Low C 6.507308\n",
+ "15 M High A 7.787113\n",
+ "20 M High B 6.877862\n",
+ "25 M High C 5.320824\n",
+ "30 F Low A 4.675374\n",
+ "35 F Low B 6.942870\n",
+ "40 F Low C 4.659411\n",
+ "45 F High A 5.429768\n",
+ "50 F High B 4.702679\n",
+ "55 F High C 5.151045"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pg.read_dataset('anova3')\n",
+ "df.loc[range(0, df.shape[0], 5)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "0b44ca4b-baa1-4210-85ce-5284e0f320db",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = smf.ols('Cholesterol ~ Sex * Risk * Drug', data=df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c651c104-a610-40a4-b4ec-bcd6c46f3c07",
+ "metadata": {},
+ "source": [
+ "We will also see several criteria for determining whether a model adequately fits the data, as well as for choosing between multiple candidate models."
+ ]
+ },
{
"cell_type": "markdown",
"id": "4b642a0a-0046-4a61-87d6-d303d41d3f06",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@@ -68,12 +241,14 @@
"id": "10c1d50d-d4f2-4ca2-bb73-7c0613069a2d",
"metadata": {},
"source": [
- "In the previous class, we performed an ANOVA to determine whether the following 3 groups of observations differ in their means:"
+ "Similarly to Pingouin, statsmodels relies of the so-called *long* format, *i.e.* the data are expected to be organized in a DataFrame with one row = one observation, and each variable (be it dependent or independent, categorical or continuous) as a column.\n",
+ "\n",
+ "To convert groups of observations, *e.g.* three groups of a single measurement:"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 6,
"id": "aec2f6b1-c4fc-465e-9434-b32333770e24",
"metadata": {},
"outputs": [],
@@ -83,39 +258,17 @@
"C = [79, 78, 88, 94, 92, 85, 83, 85, 82, 81]"
]
},
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "bd00bd2f-b7eb-47e4-8af4-187ce9473481",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "F_onewayResult(statistic=2.3575322551335636, pvalue=0.11384795345837218)"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from scipy import stats\n",
- "stats.f_oneway(A, B, C)"
- ]
- },
{
"cell_type": "markdown",
"id": "69aa48a3-1623-413b-b49d-1777b8ef92c9",
"metadata": {},
"source": [
- "We can perform the same analysis with a linear model but first have to represent the data in a single DataFrame and the so-called *long format*, with one row = one observation, and each variable or factor as a column:"
+ "we concatenate the measurements into a single column, and the group information as replicated values in a second column:"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"id": "696ff83f-d827-4513-9801-51488e5a1df0",
"metadata": {},
"outputs": [
@@ -129,7 +282,7 @@
" 'C', 'C', 'C', 'C'], dtype='<U1'))"
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -142,7 +295,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"id": "c324bd0f-e769-4a0d-8e6b-d4d346e76f68",
"metadata": {},
"outputs": [
@@ -360,7 +513,7 @@
"29 81 C"
]
},
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -383,104 +536,17 @@
},
{
"cell_type": "markdown",
- "id": "64759d8f-7fee-405c-84f2-207fd8786904",
- "metadata": {
- "hidden": true
- },
- "source": [
- "`statsmodels` understands Wilkinson formulae.\n",
- "\n",
- "Let us designate *Y* as the *dependent variable* or *response variable* in our analysis.\n",
- "We use the query `'Y ~ Group'` to build a linear model of the effect of the `Group` categorical variable on `Y`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "4a2a00ec-43c6-4299-82e3-15b807110828",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": [
- "fitted_model = smf.ols('Y ~ Group', data=dataframe).fit()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a8110314",
- "metadata": {
- "hidden": true
- },
+ "id": "d72b28d4-e4df-4ee3-b4c3-5ec1ef85d1f6",
+ "metadata": {},
"source": [
- "OLS stands for *ordinary least squares*."
+ "To perform a one-way ANOVA on the above data, we can use tools we already saw:"
]
},
{
"cell_type": "code",
"execution_count": 9,
- "id": "21bf5c1d-caa3-4072-bc86-ce860b8f33c8",
- "metadata": {
- "hidden": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " OLS Regression Results \n",
- "==============================================================================\n",
- "Dep. Variable: Y R-squared: 0.149\n",
- "Model: OLS Adj. R-squared: 0.086\n",
- "Method: Least Squares F-statistic: 2.358\n",
- "Date: Mon, 26 Sep 2022 Prob (F-statistic): 0.114\n",
- "Time: 01:44:26 Log-Likelihood: -96.604\n",
- "No. Observations: 30 AIC: 199.2\n",
- "Df Residuals: 27 BIC: 203.4\n",
- "Df Model: 2 \n",
- "Covariance Type: nonrobust \n",
- "==============================================================================\n",
- " coef std err t P>|t| [0.025 0.975]\n",
- "------------------------------------------------------------------------------\n",
- "Intercept 83.4000 2.019 41.308 0.000 79.257 87.543\n",
- "Group[T.B] 5.9000 2.855 2.066 0.049 0.041 11.759\n",
- "Group[T.C] 1.3000 2.855 0.455 0.653 -4.559 7.159\n",
- "==============================================================================\n",
- "Omnibus: 0.758 Durbin-Watson: 1.379\n",
- "Prob(Omnibus): 0.684 Jarque-Bera (JB): 0.665\n",
- "Skew: 0.336 Prob(JB): 0.717\n",
- "Kurtosis: 2.715 Cond. No. 3.73\n",
- "==============================================================================\n",
- "\n",
- "Notes:\n",
- "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
- ]
- }
- ],
- "source": [
- "print(fitted_model.summary())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fa80f8df-1f58-4de0-be95-a551d27e07eb",
- "metadata": {
- "hidden": true
- },
- "source": [
- "---\n",
- "\n",
- "In the summary table, we find the same *F* statistic and corresponding *p*-value as with `scipy.stats.f_oneway`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "638bdd6b-6964-4209-b762-2991fd3fb7fc",
- "metadata": {
- "hidden": true,
- "tags": []
- },
+ "id": "a597c0ef-75e7-47e1-a959-e7a7324b02e2",
+ "metadata": {},
"outputs": [
{
"data": {
@@ -488,267 +554,102 @@
"F_onewayResult(statistic=2.3575322551335636, pvalue=0.11384795345837218)"
]
},
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "A = [85, 86, 88, 75, 78, 94, 98, 79, 71, 80]\n",
- "B = [91, 92, 93, 85, 87, 84, 82, 88, 95, 96]\n",
- "C = [79, 78, 88, 94, 92, 85, 83, 85, 82, 81]\n",
"stats.f_oneway(A, B, C)"
]
},
- {
- "cell_type": "markdown",
- "id": "e64a5b9a-9214-4ecc-ad70-2505c5b9e78c",
- "metadata": {
- "hidden": true
- },
- "source": [
- "To get a more classical table layout, with explicit sums of squares calculation:"
- ]
- },
{
"cell_type": "code",
- "execution_count": 11,
- "id": "0fe841a0-21b1-4c92-a767-c4ab3abd37f8",
- "metadata": {
- "hidden": true,
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " df sum_sq mean_sq F PR(>F)\n",
- "Group 2.0 192.2 96.100000 2.357532 0.113848\n",
- "Residual 27.0 1100.6 40.762963 NaN NaN\n"
- ]
- }
- ],
- "source": [
- "anova_table = sm.stats.anova_lm(fitted_model)\n",
- "print(anova_table)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e30bf249-4720-488e-a5d4-39497ca1e1c1",
- "metadata": {
- "hidden": true
- },
- "source": [
- "We will come back to `anova_lm` later, as this function is actually mostly useful in multi-way ANOVA.\n",
- "\n",
- "The residuals are what the model cannot account for:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "a5b2750a-8965-4769-b053-cd95e3583320",
- "metadata": {
- "hidden": true,
- "jupyter": {
- "source_hidden": true
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 640x480 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "_, axes = plt.subplots(1, 2)\n",
- "sns.boxplot(x='Group', y='Y', data=dataframe, ax=axes[0])\n",
- "ax = axes[1]\n",
- "sns.boxplot(x=dataframe['Group'], y=fitted_model.resid, ax=ax)\n",
- "ax.set_ylabel('residuals')\n",
- "ax.yaxis.set_label_position('right')\n",
- "ax.yaxis.tick_right()\n",
- "ax.axhline(0, linestyle='--', color='red', linewidth=1);"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "248559af",
- "metadata": {
- "hidden": true
- },
- "source": [
- "The model effectively catches the grand mean and group means of $Y$, which results in estimates $\\hat{y}_i$ for each observation $y_i$ and a residual $\\epsilon_i = y_i - \\hat{y}_i$.\n",
- "\n",
- "$$\n",
- "R^2 = 1 - \\frac{\\sum_i\\epsilon_i^2}{SS_{total}}\n",
- "$$"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a30928ea-89bd-40d1-aabf-93c9ff35686c",
- "metadata": {
- "hidden": true
- },
- "source": [
- "In the bottom part of the table, a few statistics are given about the *residuals*:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "fc687c84-7ad2-4055-b00d-efbcb4545ea2",
- "metadata": {
- "hidden": true,
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "==============================================================================\n",
- "Omnibus: 0.758 Durbin-Watson: 1.379\n",
- "Prob(Omnibus): 0.684 Jarque-Bera (JB): 0.665\n",
- "Skew: 0.336 Prob(JB): 0.717\n",
- "Kurtosis: 2.715 Cond. No. 3.73\n",
- "==============================================================================\n"
- ]
- }
- ],
- "source": [
- "print(fitted_model.summary().tables[-1])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "38dc6aff-1529-4114-812b-8b802f857f4e",
- "metadata": {
- "hidden": true
- },
- "source": [
- "For example, we find mentions of an [Omnibus test of normality](https://www.statsmodels.org/stable/generated/statsmodels.stats.stattools.omni_normtest.html) (*Omnibus*) and the [Jarque-Bera test of normality](https://www.statsmodels.org/stable/generated/statsmodels.stats.stattools.jarque_bera.html) (*JB*), and intermediate measurements of skewness (*Skew*) and kurtosis (*Kurtosis*).\n",
- "The so-called omnibus test is actually the D'Agostino-Pearson test (`scipy.stats.normaltest`) applied to the residuals:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "1ecb4ddd-d9f4-42cb-8611-0f0d69da4972",
- "metadata": {
- "hidden": true
- },
+ "execution_count": 10,
+ "id": "90c687e7-e9b7-44f0-8148-d93bb8605505",
+ "metadata": {},
"outputs": [
{
"data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Source</th>\n",
+ " <th>ddof1</th>\n",
+ " <th>ddof2</th>\n",
+ " <th>F</th>\n",
+ " <th>p-unc</th>\n",
+ " <th>np2</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Group</td>\n",
+ " <td>2</td>\n",
+ " <td>27</td>\n",
+ " <td>2.357532</td>\n",
+ " <td>0.113848</td>\n",
+ " <td>0.14867</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
"text/plain": [
- "NormaltestResult(statistic=0.7583012334839461, pvalue=0.6844425164005732)"
+ " Source ddof1 ddof2 F p-unc np2\n",
+ "0 Group 2 27 2.357532 0.113848 0.14867"
]
},
- "execution_count": 14,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "stats.normaltest(fitted_model.resid)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "38e9651e-9e9a-4008-a545-5df63f30706a",
- "metadata": {
- "hidden": true
- },
- "source": [
- "Note that, here, the kurtosis is estimated as $\\beta_2$ and its expected value for a normal distribution is $3$.\n",
- "\n",
- "The [Durbin-Watson statistic](https://www.statsmodels.org/stable/generated/statsmodels.stats.stattools.durbin_watson.html) quantifies the autocorrelation of the residuals.\n",
- "This statistic takes values in the $[0,4]$ range, it should be as close as possible to $2$, and informs about the homoscedasticity (=equality of variance) of the residuals."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f7a66725-297c-45c1-b262-1a62b3ebd49f",
- "metadata": {
- "heading_collapsed": true,
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "## Linear models"
+ "pg.anova(dataframe, dv='Y', between='Group')"
]
},
{
"cell_type": "markdown",
- "id": "88e9d692-e006-459b-81bd-8655b069a9fe",
- "metadata": {
- "hidden": true
- },
+ "id": "21969382-f0b9-49e5-90f8-b8d31ca64a49",
+ "metadata": {},
"source": [
- "The middle table is the most important one. It gives information about the terms in the linear model, and how significant is the contribution of each term in the model."
+ "We now have a third way of doing the same analysis:"
]
},
{
"cell_type": "code",
- "execution_count": 15,
- "id": "02834d6d-cc6a-4746-9f2e-443c10a65fd3",
- "metadata": {
- "hidden": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "==============================================================================\n",
- " coef std err t P>|t| [0.025 0.975]\n",
- "------------------------------------------------------------------------------\n",
- "Intercept 83.4000 2.019 41.308 0.000 79.257 87.543\n",
- "Group[T.B] 5.9000 2.855 2.066 0.049 0.041 11.759\n",
- "Group[T.C] 1.3000 2.855 0.455 0.653 -4.559 7.159\n",
- "==============================================================================\n"
- ]
- }
- ],
- "source": [
- "print(fitted_model.summary().tables[1])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "546d7d79-a709-4178-b338-acf08d0a29cf",
+ "execution_count": 11,
+ "id": "4a2a00ec-43c6-4299-82e3-15b807110828",
"metadata": {
"hidden": true
},
+ "outputs": [],
"source": [
- "Three terms are mentioned, meaning that the *response variable* `Y` is approximated as `a + b * Group[T.B] + c * Group[T.C]` with `a` the *intercept*.\n",
- "\n",
- "`a`, `b` and `c` are given in the `coef` column.\n",
- "\n",
- "At this point, we may ask:\n",
- "\n",
- "* why is `Group` decomposed as `Group[T.B]` and `Group[T.C]`?\n",
- "* why group `A`, or `Group[T.A]`, does not appear in the table?\n",
- "* why does the intercept equal group `A`'s mean?\n",
- "\n",
- "On the other side, `ols` did model the `A` group. This can be checked running post-hoc tests (more about these tests later):"
+ "fitted_model = smf.ols('Y ~ Group', data=dataframe).fit()"
]
},
{
"cell_type": "code",
- "execution_count": 16,
- "id": "cdd44a30-6648-495f-98e7-c89739921066",
+ "execution_count": 12,
+ "id": "21bf5c1d-caa3-4072-bc86-ce860b8f33c8",
"metadata": {
"hidden": true
},
@@ -774,580 +675,875 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>coef</th>\n",
- " <th>std err</th>\n",
- " <th>t</th>\n",
- " <th>P>|t|</th>\n",
- " <th>Conf. Int. Low</th>\n",
- " <th>Conf. Int. Upp.</th>\n",
- " <th>pvalue-hs</th>\n",
- " <th>reject-hs</th>\n",
+ " <th>df</th>\n",
+ " <th>sum_sq</th>\n",
+ " <th>mean_sq</th>\n",
+ " <th>F</th>\n",
+ " <th>PR(>F)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>B-A</th>\n",
- " <td>5.9</td>\n",
- " <td>2.855275</td>\n",
- " <td>2.066351</td>\n",
- " <td>0.048510</td>\n",
- " <td>0.041461</td>\n",
- " <td>11.758539</td>\n",
- " <td>0.138585</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>C-A</th>\n",
- " <td>1.3</td>\n",
- " <td>2.855275</td>\n",
- " <td>0.455298</td>\n",
- " <td>0.652535</td>\n",
- " <td>-4.558539</td>\n",
- " <td>7.158539</td>\n",
- " <td>0.652535</td>\n",
- " <td>False</td>\n",
+ " <th>Group</th>\n",
+ " <td>2.0</td>\n",
+ " <td>192.2</td>\n",
+ " <td>96.100000</td>\n",
+ " <td>2.357532</td>\n",
+ " <td>0.113848</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>C-B</th>\n",
- " <td>-4.6</td>\n",
- " <td>2.855275</td>\n",
- " <td>-1.611053</td>\n",
- " <td>0.118798</td>\n",
- " <td>-10.458539</td>\n",
- " <td>1.258539</td>\n",
- " <td>0.223484</td>\n",
- " <td>False</td>\n",
+ " <th>Residual</th>\n",
+ " <td>27.0</td>\n",
+ " <td>1100.6</td>\n",
+ " <td>40.762963</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " coef std err t P>|t| Conf. Int. Low Conf. Int. Upp. \\\n",
- "B-A 5.9 2.855275 2.066351 0.048510 0.041461 11.758539 \n",
- "C-A 1.3 2.855275 0.455298 0.652535 -4.558539 7.158539 \n",
- "C-B -4.6 2.855275 -1.611053 0.118798 -10.458539 1.258539 \n",
- "\n",
- " pvalue-hs reject-hs \n",
- "B-A 0.138585 False \n",
- "C-A 0.652535 False \n",
- "C-B 0.223484 False "
+ " df sum_sq mean_sq F PR(>F)\n",
+ "Group 2.0 192.2 96.100000 2.357532 0.113848\n",
+ "Residual 27.0 1100.6 40.762963 NaN NaN"
]
},
- "execution_count": 16,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "post_hoc_tests = fitted_model.t_test_pairwise('Group')\n",
- "post_hoc_tests.result_frame"
+ "sm.stats.anova_lm(fitted_model)"
]
},
{
"cell_type": "markdown",
- "id": "aa935207-cb37-4ee2-9562-ba5852230881",
+ "id": "64759d8f-7fee-405c-84f2-207fd8786904",
"metadata": {
- "heading_collapsed": true,
"hidden": true
},
"source": [
- "### Design matrices"
+ "statsmodels - in particular the functions from the `statsmodels.formula.api` module - understands Wilkinson formulae.\n",
+ "\n",
+ "With expression `Y ~ Group`, we designated *Y* as the *dependent variable* or *response variable* in our analysis, and told the `ols` function to build a linear model of the effect of the `Group` categorical variable on `Y`. OLS stands for *ordinary least squares*."
]
},
{
"cell_type": "markdown",
- "id": "51d30cc1-196b-49ec-9548-07384aa970e1",
+ "id": "fa80f8df-1f58-4de0-be95-a551d27e07eb",
"metadata": {
"hidden": true
},
"source": [
- "To understand what is happening, let us first have a look at the *design* `endog` and `exog` matrices:"
+ "---\n",
+ "\n",
+ "In the summary table, we find again the same *F* statistic and corresponding *p*-value as with `scipy.stats.f_oneway` or `pingouin.anova`:"
]
},
{
"cell_type": "code",
- "execution_count": 17,
- "id": "86386730-0784-4304-bb82-c8909e7ffb01",
+ "execution_count": 13,
+ "id": "638bdd6b-6964-4209-b762-2991fd3fb7fc",
"metadata": {
- "hidden": true
+ "hidden": true,
+ "tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "F_onewayResult(statistic=2.3575322551335636, pvalue=0.11384795345837218)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "from patsy import dmatrices\n",
- "endog, exog = dmatrices('Y ~ Group', dataframe)"
+ "stats.f_oneway(A, B, C)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e64a5b9a-9214-4ecc-ad70-2505c5b9e78c",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "To get a more detailed output about the fitted model, instead of the `statsmodels.api.stats.anova_lm` function we can use the `summary` method:"
]
},
{
"cell_type": "code",
- "execution_count": 18,
- "id": "6fd10d16-b7da-4adb-ba6c-2c82b6529ae6",
+ "execution_count": 14,
+ "id": "0fe841a0-21b1-4c92-a767-c4ab3abd37f8",
"metadata": {
"hidden": true,
- "jupyter": {
- "source_hidden": true
- },
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<table class=\"simpletable\">\n",
+ "<caption>OLS Regression Results</caption>\n",
+ "<tr>\n",
+ " <th>Dep. Variable:</th> <td>Y</td> <th> R-squared: </th> <td> 0.149</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.086</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 2.358</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Date:</th> <td>Mon, 21 Aug 2023</td> <th> Prob (F-statistic):</th> <td> 0.114</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Time:</th> <td>16:37:44</td> <th> Log-Likelihood: </th> <td> -96.604</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>No. Observations:</th> <td> 30</td> <th> AIC: </th> <td> 199.2</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Df Residuals:</th> <td> 27</td> <th> BIC: </th> <td> 203.4</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
+ "</tr>\n",
+ "</table>\n",
+ "<table class=\"simpletable\">\n",
+ "<tr>\n",
+ " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Intercept</th> <td> 83.4000</td> <td> 2.019</td> <td> 41.308</td> <td> 0.000</td> <td> 79.257</td> <td> 87.543</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Group[T.B]</th> <td> 5.9000</td> <td> 2.855</td> <td> 2.066</td> <td> 0.049</td> <td> 0.041</td> <td> 11.759</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Group[T.C]</th> <td> 1.3000</td> <td> 2.855</td> <td> 0.455</td> <td> 0.653</td> <td> -4.559</td> <td> 7.159</td>\n",
+ "</tr>\n",
+ "</table>\n",
+ "<table class=\"simpletable\">\n",
+ "<tr>\n",
+ " <th>Omnibus:</th> <td> 0.758</td> <th> Durbin-Watson: </th> <td> 1.379</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Prob(Omnibus):</th> <td> 0.684</td> <th> Jarque-Bera (JB): </th> <td> 0.665</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Skew:</th> <td> 0.336</td> <th> Prob(JB): </th> <td> 0.717</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Kurtosis:</th> <td> 2.715</td> <th> Cond. No. </th> <td> 3.73</td>\n",
+ "</tr>\n",
+ "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+ ],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\toprule\n",
+ "\\textbf{Dep. Variable:} & Y & \\textbf{ R-squared: } & 0.149 \\\\\n",
+ "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.086 \\\\\n",
+ "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 2.358 \\\\\n",
+ "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Prob (F-statistic):} & 0.114 \\\\\n",
+ "\\textbf{Time:} & 16:37:44 & \\textbf{ Log-Likelihood: } & -96.604 \\\\\n",
+ "\\textbf{No. Observations:} & 30 & \\textbf{ AIC: } & 199.2 \\\\\n",
+ "\\textbf{Df Residuals:} & 27 & \\textbf{ BIC: } & 203.4 \\\\\n",
+ "\\textbf{Df Model:} & 2 & \\textbf{ } & \\\\\n",
+ "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lcccccc}\n",
+ " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
+ "\\midrule\n",
+ "\\textbf{Intercept} & 83.4000 & 2.019 & 41.308 & 0.000 & 79.257 & 87.543 \\\\\n",
+ "\\textbf{Group[T.B]} & 5.9000 & 2.855 & 2.066 & 0.049 & 0.041 & 11.759 \\\\\n",
+ "\\textbf{Group[T.C]} & 1.3000 & 2.855 & 0.455 & 0.653 & -4.559 & 7.159 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\textbf{Omnibus:} & 0.758 & \\textbf{ Durbin-Watson: } & 1.379 \\\\\n",
+ "\\textbf{Prob(Omnibus):} & 0.684 & \\textbf{ Jarque-Bera (JB): } & 0.665 \\\\\n",
+ "\\textbf{Skew:} & 0.336 & \\textbf{ Prob(JB): } & 0.717 \\\\\n",
+ "\\textbf{Kurtosis:} & 2.715 & \\textbf{ Cond. No. } & 3.73 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "%\\caption{OLS Regression Results}\n",
+ "\\end{center}\n",
+ "\n",
+ "Notes: \\newline\n",
+ " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+ ],
+ "text/plain": [
+ "<class 'statsmodels.iolib.summary.Summary'>\n",
+ "\"\"\"\n",
+ " OLS Regression Results \n",
+ "==============================================================================\n",
+ "Dep. Variable: Y R-squared: 0.149\n",
+ "Model: OLS Adj. R-squared: 0.086\n",
+ "Method: Least Squares F-statistic: 2.358\n",
+ "Date: Mon, 21 Aug 2023 Prob (F-statistic): 0.114\n",
+ "Time: 16:37:44 Log-Likelihood: -96.604\n",
+ "No. Observations: 30 AIC: 199.2\n",
+ "Df Residuals: 27 BIC: 203.4\n",
+ "Df Model: 2 \n",
+ "Covariance Type: nonrobust \n",
+ "==============================================================================\n",
+ " coef std err t P>|t| [0.025 0.975]\n",
+ "------------------------------------------------------------------------------\n",
+ "Intercept 83.4000 2.019 41.308 0.000 79.257 87.543\n",
+ "Group[T.B] 5.9000 2.855 2.066 0.049 0.041 11.759\n",
+ "Group[T.C] 1.3000 2.855 0.455 0.653 -4.559 7.159\n",
+ "==============================================================================\n",
+ "Omnibus: 0.758 Durbin-Watson: 1.379\n",
+ "Prob(Omnibus): 0.684 Jarque-Bera (JB): 0.665\n",
+ "Skew: 0.336 Prob(JB): 0.717\n",
+ "Kurtosis: 2.715 Cond. No. 3.73\n",
+ "==============================================================================\n",
+ "\n",
+ "Notes:\n",
+ "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+ "\"\"\""
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fitted_model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e6d455b-bbf5-476e-86b3-b513cd4967a4",
+ "metadata": {},
"source": [
- "design_matrix_to_str = lambda g: repr(g.data.obj)\n",
+ "* The first table gives several indicators of how well the model fits the data.\n",
+ "* The second table shows the coefficients (intercept and slopes) of the model, and per-coefficient statistical information.\n",
+ "* The third table lists a few tests for additional properties (normality, skewness, kurtosis, etc.).\n",
"\n",
- "def side_by_side(*strs, sep=' | '):\n",
- " \"\"\"\n",
- " Horizontally concatenate multiline string representations of objects.\n",
- " \n",
- " Example:\n",
- " \n",
- " >>> import numpy as np\n",
- " >>> rng = np.random.default_rng(seed=1)\n",
- " >>> a = rng.integers(10, size=(5,3))\n",
- " >>> b = rng.random(size=(6,1))\n",
- " >>> c = rng.choice(list('abcd'), size=(4,2))\n",
- " >>> print(side_by_side(str(a), str(b), str(c)))\n",
- " [[4 5 7] | [[0.54959369] | [['b' 'a'] \n",
- " [9 0 1] | [0.02755911] | ['b' 'a'] \n",
- " [8 9 2] | [0.75351311] | ['b' 'd'] \n",
- " [3 8 4] | [0.53814331] | ['a' 'b']]\n",
- " [2 8 2]] | [0.32973172] | \n",
- " | [0.7884287 ]] | \n",
- " >>>\n",
- " \"\"\"\n",
- " row_series = [ s.split('\\n') for s in strs ]\n",
- " nrows = max([ len(rows) for rows in row_series ])\n",
- " padded_row_series = []\n",
- " for rows in row_series:\n",
- " max_len = max([len(row) for row in rows])\n",
- " padded_rows = []\n",
- " r = 0\n",
- " for r, row in enumerate(rows):\n",
- " padded_rows.append(row + ' '*(max_len-len(row)))\n",
- " empty_row = ' '*max_len\n",
- " r += 1\n",
- " while r < nrows:\n",
- " padded_rows.append(empty_row)\n",
- " r += 1\n",
- " padded_row_series.append(padded_rows)\n",
- " concatenated_rows = [ sep.join(rows) for rows in zip(*padded_row_series) ]\n",
- " return '\\n'.join(concatenated_rows)"
+ "To understand the model, let us have a look at the second table first:"
]
},
{
"cell_type": "code",
- "execution_count": 19,
- "id": "c885739b-16dc-4269-b0cf-1f0379e6c4c8",
- "metadata": {
- "hidden": true
- },
+ "execution_count": 15,
+ "id": "fb1e8107-18e7-4627-83af-eada7e8dcfca",
+ "metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DesignMatrix with shape (30, 1) | DesignMatrix with shape (30, 3) \n",
- " Y | Intercept Group[T.B] Group[T.C]\n",
- " 85 | 1 0 0\n",
- " 86 | 1 0 0\n",
- " 88 | 1 0 0\n",
- " 75 | 1 0 0\n",
- " 78 | 1 0 0\n",
- " 94 | 1 0 0\n",
- " 98 | 1 0 0\n",
- " 79 | 1 0 0\n",
- " 71 | 1 0 0\n",
- " 80 | 1 0 0\n",
- " 91 | 1 1 0\n",
- " 92 | 1 1 0\n",
- " 93 | 1 1 0\n",
- " 85 | 1 1 0\n",
- " 87 | 1 1 0\n",
- " 84 | 1 1 0\n",
- " 82 | 1 1 0\n",
- " 88 | 1 1 0\n",
- " 95 | 1 1 0\n",
- " 96 | 1 1 0\n",
- " 79 | 1 0 1\n",
- " 78 | 1 0 1\n",
- " 88 | 1 0 1\n",
- " 94 | 1 0 1\n",
- " 92 | 1 0 1\n",
- " 85 | 1 0 1\n",
- " 83 | 1 0 1\n",
- " 85 | 1 0 1\n",
- " 82 | 1 0 1\n",
- " 81 | 1 0 1\n",
- " Terms: | Terms: \n",
- " 'Y' (column 0) | 'Intercept' (column 0) \n",
- " | 'Group' (columns 1:3) \n"
- ]
+ "data": {
+ "text/html": [
+ "<table class=\"simpletable\">\n",
+ "<tr>\n",
+ " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Intercept</th> <td> 83.4000</td> <td> 2.019</td> <td> 41.308</td> <td> 0.000</td> <td> 79.257</td> <td> 87.543</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Group[T.B]</th> <td> 5.9000</td> <td> 2.855</td> <td> 2.066</td> <td> 0.049</td> <td> 0.041</td> <td> 11.759</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Group[T.C]</th> <td> 1.3000</td> <td> 2.855</td> <td> 0.455</td> <td> 0.653</td> <td> -4.559</td> <td> 7.159</td>\n",
+ "</tr>\n",
+ "</table>"
+ ],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lcccccc}\n",
+ "\\toprule\n",
+ " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
+ "\\midrule\n",
+ "\\textbf{Intercept} & 83.4000 & 2.019 & 41.308 & 0.000 & 79.257 & 87.543 \\\\\n",
+ "\\textbf{Group[T.B]} & 5.9000 & 2.855 & 2.066 & 0.049 & 0.041 & 11.759 \\\\\n",
+ "\\textbf{Group[T.C]} & 1.3000 & 2.855 & 0.455 & 0.653 & -4.559 & 7.159 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\end{center}"
+ ],
+ "text/plain": [
+ "<class 'statsmodels.iolib.table.SimpleTable'>"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "print(side_by_side(design_matrix_to_str(endog), design_matrix_to_str(exog)))"
+ "fitted_model.summary().tables[1]"
]
},
{
"cell_type": "markdown",
- "id": "207b6cb4-e44f-4ef5-b85b-2670680fa742",
- "metadata": {
- "hidden": true
- },
+ "id": "274f1098-05ae-46d3-82ea-58c71d786cfb",
+ "metadata": {},
"source": [
- "$\\require{color}$"
+ "In the present case, the intercept encodes group A's mean, while the `Group[T.B]` term is the difference between group B's mean and the intercept (or group A's mean), and the `Group[T.C]` term is the difference between group C's mean and the intercept."
]
},
{
- "cell_type": "markdown",
- "id": "ca7ee778-b453-4890-ae89-c7453f1a0b57",
- "metadata": {
- "hidden": true
- },
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "b4aca144-0aa3-45f3-82ff-2b9d5922437d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Intercept 83.4\n",
+ "Group[T.B] 5.9\n",
+ "Group[T.C] 1.3\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "The right-hand side (`endog`) is a vector that represents the response variable we previously called *Y*.\n",
- "Here, this is a vector because we model a single response variable.\n",
- "\n",
- "The left-hand side is the (main) *design matrix* (`exog`) and represents the terms involved as input to the linear model.\n",
- "As already said, fitting such a model consists in finding $a$, $b$ and $c$ such that:\n",
- "\n",
- "$$\n",
- "\\mathtt{\\colorbox{#F2F3F4}{Y}} = a \\mbox{ } \\mathtt{\\colorbox{#F2F3F4}{Intercept}} + b \\mbox{ } \\mathtt{\\colorbox{#F2F3F4}{Group[T.B]}} + c \\mbox{ } \\mathtt{\\colorbox{#F2F3F4}{Group[T.C]}} + \\epsilon\n",
- "$$\n",
- "\n",
- "As the intercept is a constant, the corresponding term is always modelled as a constant vector.\n",
- "\n",
- "We can observe that the `Group` variable is represented as several binary variables; one per level of the original categorical variable, **minus one**.\n",
- "These binary variables are called *dummy variables*. All categorical variables are translated this way, into one or several dummy variables.\n",
- "\n",
- "`A` is not explicitly modelled, because all the values in a `Group[T.A]` column could be predicted knowing the corresponding values in the other two `Group` columns.\n",
- "In other words, a `Group[T.A]` dummy variable would not bring additional information.\n",
- "\n",
- "Basically, `A` is taken as a reference group. The intercept is enough to capture group `A`'s mean, and the `Group[T.B]` and `Group[T.C]` variables encodes the offsets with group `A`'s mean for the other 2 groups.\n",
- "\n",
- "If we force `ols` to explicitly use an additional dummy variable for group `A`, designing the matrices ourselves, we get correct output in this case, but `ols` complains about collinearity:"
+ "coefficients = fitted_model.params\n",
+ "coefficients"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "3511838e-a886-4d0f-8be6-bce37a54aa2c",
- "metadata": {
- "hidden": true,
- "jupyter": {
- "source_hidden": true
- },
- "tags": []
- },
- "outputs": [],
+ "execution_count": 17,
+ "id": "59a500b3-c264-42a2-ac09-faa0eb07aed6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "import sys\n",
- "!\"{sys.executable}\" -m pip install formulaic"
+ "intercept, B_term, C_term = coefficients\n",
+ "ax = sns.boxplot(x='Group', y='Y', data=dataframe)\n",
+ "ax.plot(\n",
+ " ax.get_xticks(), # abscissa of the boxplots\n",
+ " np.array([\n",
+ " intercept, # A mean\n",
+ " intercept + B_term, # B mean\n",
+ " intercept + C_term]), # C mean\n",
+ " 'rs'); # red squares"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9645b7a5-70f7-4f01-9135-902ab55d9dcf",
+ "metadata": {},
+ "source": [
+ "The associated statistical is of limited interest here.\n",
+ "\n",
+ "Let us instead focus on the omnibus statistic and other fitness measurements in the first table:"
]
},
{
"cell_type": "code",
- "execution_count": 21,
- "id": "8960bbfa-ef9f-4530-94eb-2a894140b51b",
- "metadata": {
- "hidden": true
- },
+ "execution_count": 18,
+ "id": "37d47be2-6e62-4fe7-b56f-bb47e951aca9",
+ "metadata": {},
"outputs": [
{
"data": {
"text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Intercept</th>\n",
- " <th>Group A</th>\n",
- " <th>Group B</th>\n",
- " <th>Group C</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>1.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
+ "<table class=\"simpletable\">\n",
+ "<caption>OLS Regression Results</caption>\n",
+ "<tr>\n",
+ " <th>Dep. Variable:</th> <td>Y</td> <th> R-squared: </th> <td> 0.149</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.086</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 2.358</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Date:</th> <td>Mon, 21 Aug 2023</td> <th> Prob (F-statistic):</th> <td> 0.114</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Time:</th> <td>16:37:44</td> <th> Log-Likelihood: </th> <td> -96.604</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>No. Observations:</th> <td> 30</td> <th> AIC: </th> <td> 199.2</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Df Residuals:</th> <td> 27</td> <th> BIC: </th> <td> 203.4</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
+ "</tr>\n",
+ "</table>"
],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\toprule\n",
+ "\\textbf{Dep. Variable:} & Y & \\textbf{ R-squared: } & 0.149 \\\\\n",
+ "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.086 \\\\\n",
+ "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 2.358 \\\\\n",
+ "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Prob (F-statistic):} & 0.114 \\\\\n",
+ "\\textbf{Time:} & 16:37:44 & \\textbf{ Log-Likelihood: } & -96.604 \\\\\n",
+ "\\textbf{No. Observations:} & 30 & \\textbf{ AIC: } & 199.2 \\\\\n",
+ "\\textbf{Df Residuals:} & 27 & \\textbf{ BIC: } & 203.4 \\\\\n",
+ "\\textbf{Df Model:} & 2 & \\textbf{ } & \\\\\n",
+ "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "%\\caption{OLS Regression Results}\n",
+ "\\end{center}"
+ ],
+ "text/plain": [
+ "<class 'statsmodels.iolib.table.SimpleTable'>"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fitted_model.summary().tables[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e30bf249-4720-488e-a5d4-39497ca1e1c1",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "The residuals are what the model cannot account for:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "a5b2750a-8965-4769-b053-cd95e3583320",
+ "metadata": {
+ "hidden": true,
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "statsmodels_material.illustration_residuals(dataframe, fitted_model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "158674d4-7123-4f60-b1fa-887f81261816",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 1.6\n",
+ "1 2.6\n",
+ "2 4.6\n",
+ "3 -8.4\n",
+ "4 -5.4\n",
+ "5 10.6\n",
+ "6 14.6\n",
+ "7 -4.4\n",
+ "8 -12.4\n",
+ "9 -3.4\n",
+ "10 1.7\n",
+ "11 2.7\n",
+ "12 3.7\n",
+ "13 -4.3\n",
+ "14 -2.3\n",
+ "15 -5.3\n",
+ "16 -7.3\n",
+ "17 -1.3\n",
+ "18 5.7\n",
+ "19 6.7\n",
+ "20 -5.7\n",
+ "21 -6.7\n",
+ "22 3.3\n",
+ "23 9.3\n",
+ "24 7.3\n",
+ "25 0.3\n",
+ "26 -1.7\n",
+ "27 0.3\n",
+ "28 -2.7\n",
+ "29 -3.7\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fitted_model.resid"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "248559af",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "The model effectively catches the group means of $Y$, which results in estimates $\\hat{y}_i$ for each observation $y_i$ and a residual $\\epsilon_i = y_i - \\hat{y}_i$.\n",
+ "\n",
+ "We are given the $R^2$ and adjusted $R_{adj}^2$:\n",
+ "$$\n",
+ "R^2 = 1 - \\frac{\\sum_i\\epsilon_i^2}{SS_{total}} \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad R_{adj}^2 = 1 - \\frac{(n-1)(1 - R^2)}{n-k-1}\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a30928ea-89bd-40d1-aabf-93c9ff35686c",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "The last table displays a few statistics about the residuals:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "fc687c84-7ad2-4055-b00d-efbcb4545ea2",
+ "metadata": {
+ "hidden": true,
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "==============================================================================\n",
+ "Omnibus: 0.758 Durbin-Watson: 1.379\n",
+ "Prob(Omnibus): 0.684 Jarque-Bera (JB): 0.665\n",
+ "Skew: 0.336 Prob(JB): 0.717\n",
+ "Kurtosis: 2.715 Cond. No. 3.73\n",
+ "==============================================================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(fitted_model.summary().tables[-1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "38dc6aff-1529-4114-812b-8b802f857f4e",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "For example, we find mentions of an [Omnibus test of normality](https://www.statsmodels.org/stable/generated/statsmodels.stats.stattools.omni_normtest.html) (*Omnibus*) and the [Jarque-Bera test of normality](https://www.statsmodels.org/stable/generated/statsmodels.stats.stattools.jarque_bera.html) (*JB*), and intermediate measurements of skewness (*Skew*) and kurtosis (*Kurtosis*).\n",
+ "The so-called omnibus test is actually the D'Agostino-Pearson test (`scipy.stats.normaltest`) applied to the residuals:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "1ecb4ddd-d9f4-42cb-8611-0f0d69da4972",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NormaltestResult(statistic=0.7583012334839462, pvalue=0.6844425164005732)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "stats.normaltest(fitted_model.resid)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "38e9651e-9e9a-4008-a545-5df63f30706a",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "Note that, here, the kurtosis is estimated as $\\beta_2$ and its expected value for a normal distribution is $3$.\n",
+ "\n",
+ "The [Durbin-Watson statistic](https://www.statsmodels.org/stable/generated/statsmodels.stats.stattools.durbin_watson.html) quantifies the autocorrelation of the residuals.\n",
+ "This statistic takes values in the $[0,4]$ range, it should be as close as possible to $2$, and informs about the homoscedasticity (=equality of variance) of the residuals."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f7a66725-297c-45c1-b262-1a62b3ebd49f",
+ "metadata": {
+ "heading_collapsed": true,
+ "tags": []
+ },
+ "source": [
+ "## Model specification"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f4c1c21f-c77b-478f-a310-af3cc91b5262",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "The *response variable* `Y` is approximated as $a + b * \\mathbb{1}_B + c * \\mathbb{1}_C$ denoting\n",
+ "$a$, $b$ and $c$ the three coefficients that appear in the `coef` column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "02834d6d-cc6a-4746-9f2e-443c10a65fd3",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "==============================================================================\n",
+ " coef std err t P>|t| [0.025 0.975]\n",
+ "------------------------------------------------------------------------------\n",
+ "Intercept 83.4000 2.019 41.308 0.000 79.257 87.543\n",
+ "Group[T.B] 5.9000 2.855 2.066 0.049 0.041 11.759\n",
+ "Group[T.C] 1.3000 2.855 0.455 0.653 -4.559 7.159\n",
+ "==============================================================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(fitted_model.summary().tables[1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa935207-cb37-4ee2-9562-ba5852230881",
+ "metadata": {
+ "heading_collapsed": true,
+ "hidden": true
+ },
+ "source": [
+ "### Design matrices"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "51d30cc1-196b-49ec-9548-07384aa970e1",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "The linear model relies on a specific representation of the data: the `endog` and `exog` *design* matrices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "86386730-0784-4304-bb82-c8909e7ffb01",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": [
+ "from patsy import dmatrices\n",
+ "endog, exog = dmatrices('Y ~ Group', dataframe)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "c885739b-16dc-4269-b0cf-1f0379e6c4c8",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DesignMatrix with shape (30, 1) | DesignMatrix with shape (30, 3) \n",
+ " Y | Intercept Group[T.B] Group[T.C]\n",
+ " 85 | 1 0 0\n",
+ " 86 | 1 0 0\n",
+ " 88 | 1 0 0\n",
+ " 75 | 1 0 0\n",
+ " 78 | 1 0 0\n",
+ " 94 | 1 0 0\n",
+ " 98 | 1 0 0\n",
+ " 79 | 1 0 0\n",
+ " 71 | 1 0 0\n",
+ " 80 | 1 0 0\n",
+ " 91 | 1 1 0\n",
+ " 92 | 1 1 0\n",
+ " 93 | 1 1 0\n",
+ " 85 | 1 1 0\n",
+ " 87 | 1 1 0\n",
+ " 84 | 1 1 0\n",
+ " 82 | 1 1 0\n",
+ " 88 | 1 1 0\n",
+ " 95 | 1 1 0\n",
+ " 96 | 1 1 0\n",
+ " 79 | 1 0 1\n",
+ " 78 | 1 0 1\n",
+ " 88 | 1 0 1\n",
+ " 94 | 1 0 1\n",
+ " 92 | 1 0 1\n",
+ " 85 | 1 0 1\n",
+ " 83 | 1 0 1\n",
+ " 85 | 1 0 1\n",
+ " 82 | 1 0 1\n",
+ " 81 | 1 0 1\n",
+ " Terms: | Terms: \n",
+ " 'Y' (column 0) | 'Intercept' (column 0) \n",
+ " | 'Group' (columns 1:3) \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(statsmodels_material.side_by_side(endog, exog))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "207b6cb4-e44f-4ef5-b85b-2670680fa742",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "$\\require{color}$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca7ee778-b453-4890-ae89-c7453f1a0b57",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "The right-hand side (`endog`) is a vector that represents the response variable we previously called *Y*.\n",
+ "Here, this is a vector because we model a single response variable.\n",
+ "\n",
+ "The left-hand side is the (main) *design matrix* (`exog`) and represents the terms involved as input to the linear model.\n",
+ "As already said, fitting such a model consists in finding $a$, $b$ and $c$ such that:\n",
+ "\n",
+ "$$\n",
+ "\\mathtt{\\colorbox{#F2F3F4}{Y}} = a \\mbox{ } \\mathtt{\\colorbox{#F2F3F4}{Intercept}} + b \\mbox{ } \\mathtt{\\colorbox{#F2F3F4}{Group[T.B]}} + c \\mbox{ } \\mathtt{\\colorbox{#F2F3F4}{Group[T.C]}} + \\epsilon\n",
+ "$$\n",
+ "\n",
+ "As the intercept is a constant, the corresponding term is always modelled as a constant vector.\n",
+ "\n",
+ "We can observe that the `Group` variable is represented as several binary variables; one per level of the original categorical variable, **minus one**.\n",
+ "These binary variables are called *dummy variables*. All categorical variables are translated this way, into one or several dummy variables.\n",
+ "\n",
+ "`A` is not explicitly modelled, because all the values in a `Group[T.A]` column could be predicted knowing the corresponding values in the other two `Group` columns.\n",
+ "In other words, a `Group[T.A]` dummy variable would not bring additional information.\n",
+ "\n",
+ "Basically, `A` is taken as a reference group. The intercept is enough to capture group `A`'s mean, and the `Group[T.B]` and `Group[T.C]` variables encodes the offsets with group `A`'s mean for the other 2 groups.\n",
+ "\n",
+ "If we force `ols` to explicitly use an additional dummy variable for group `A`, designing the matrices ourselves, we get correct output in this case, but `ols` complains about collinearity:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "f7ab66b2-b6aa-4437-8427-e3e731fd58e8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
"text/plain": [
- " Intercept Group A Group B Group C\n",
- "0 1.0 1 0 0\n",
- "1 1.0 1 0 0\n",
- "2 1.0 1 0 0\n",
- "3 1.0 1 0 0\n",
- "4 1.0 1 0 0\n",
- "5 1.0 1 0 0\n",
- "6 1.0 1 0 0\n",
- "7 1.0 1 0 0\n",
- "8 1.0 1 0 0\n",
- "9 1.0 1 0 0\n",
- "10 1.0 0 1 0\n",
- "11 1.0 0 1 0\n",
- "12 1.0 0 1 0\n",
- "13 1.0 0 1 0\n",
- "14 1.0 0 1 0\n",
- "15 1.0 0 1 0\n",
- "16 1.0 0 1 0\n",
- "17 1.0 0 1 0\n",
- "18 1.0 0 1 0\n",
- "19 1.0 0 1 0\n",
- "20 1.0 0 0 1\n",
- "21 1.0 0 0 1\n",
- "22 1.0 0 0 1\n",
- "23 1.0 0 0 1\n",
- "24 1.0 0 0 1\n",
- "25 1.0 0 0 1\n",
- "26 1.0 0 0 1\n",
- "27 1.0 0 0 1\n",
- "28 1.0 0 0 1\n",
- "29 1.0 0 0 1"
+ "array([[1., 1., 0., 0.],\n",
+ " [1., 1., 0., 0.],\n",
+ " [1., 1., 0., 0.],\n",
+ " [1., 1., 0., 0.],\n",
+ " [1., 1., 0., 0.],\n",
+ " [1., 1., 0., 0.],\n",
+ " [1., 1., 0., 0.],\n",
+ " [1., 1., 0., 0.],\n",
+ " [1., 1., 0., 0.],\n",
+ " [1., 1., 0., 0.],\n",
+ " [1., 0., 1., 0.],\n",
+ " [1., 0., 1., 0.],\n",
+ " [1., 0., 1., 0.],\n",
+ " [1., 0., 1., 0.],\n",
+ " [1., 0., 1., 0.],\n",
+ " [1., 0., 1., 0.],\n",
+ " [1., 0., 1., 0.],\n",
+ " [1., 0., 1., 0.],\n",
+ " [1., 0., 1., 0.],\n",
+ " [1., 0., 1., 0.],\n",
+ " [1., 0., 0., 1.],\n",
+ " [1., 0., 0., 1.],\n",
+ " [1., 0., 0., 1.],\n",
+ " [1., 0., 0., 1.],\n",
+ " [1., 0., 0., 1.],\n",
+ " [1., 0., 0., 1.],\n",
+ " [1., 0., 0., 1.],\n",
+ " [1., 0., 0., 1.],\n",
+ " [1., 0., 0., 1.],\n",
+ " [1., 0., 0., 1.]])"
]
},
- "execution_count": 21,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# formulaic gives easier access to the design matrix\n",
- "from formulaic import model_matrix\n",
- "endog, exog = model_matrix('Y ~ Group', dataframe)\n",
- "# add a dummy variable for group A\n",
- "exog['Group[T.A]'] = (dataframe['Group'] == 'A').astype(int)\n",
- "# just for readability, rename a few columns\n",
- "exog = exog.rename(columns={'Group[T.A]': 'Group A', 'Group[T.B]': 'Group B', 'Group[T.C]': 'Group C'})\n",
- "# reorder the columns\n",
- "exog = exog[['Intercept', 'Group A', 'Group B', 'Group C']]\n",
- "exog"
+ "intercept, dummyB, dummyC = exog.T\n",
+ "dummyA = intercept - dummyB - dummyC\n",
+ "overdefined_exog = np.stack((intercept, dummyA, dummyB, dummyC), axis=1)\n",
+ "overdefined_exog"
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 27,
"id": "b18356ea-7df2-4596-abe4-b6a320280809",
"metadata": {
"hidden": true
@@ -1359,7 +1555,7 @@
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
- " <th>Dep. Variable:</th> <td>Y</td> <th> R-squared: </th> <td> 0.149</td>\n",
+ " <th>Dep. Variable:</th> <td>y</td> <th> R-squared: </th> <td> 0.149</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.086</td>\n",
@@ -1368,10 +1564,10 @@
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 2.358</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Date:</th> <td>Mon, 26 Sep 2022</td> <th> Prob (F-statistic):</th> <td> 0.114</td> \n",
+ " <th>Date:</th> <td>Mon, 21 Aug 2023</td> <th> Prob (F-statistic):</th> <td> 0.114</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Time:</th> <td>01:44:31</td> <th> Log-Likelihood: </th> <td> -96.604</td>\n",
+ " <th>Time:</th> <td>16:37:44</td> <th> Log-Likelihood: </th> <td> -96.604</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 30</td> <th> AIC: </th> <td> 199.2</td>\n",
@@ -1388,19 +1584,19 @@
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
- " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
+ " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
- " <th>Intercept</th> <td> 64.3500</td> <td> 0.874</td> <td> 73.606</td> <td> 0.000</td> <td> 62.556</td> <td> 66.144</td>\n",
+ " <th>const</th> <td> 64.3500</td> <td> 0.874</td> <td> 73.606</td> <td> 0.000</td> <td> 62.556</td> <td> 66.144</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Group A</th> <td> 19.0500</td> <td> 1.674</td> <td> 11.380</td> <td> 0.000</td> <td> 15.615</td> <td> 22.485</td>\n",
+ " <th>x1</th> <td> 19.0500</td> <td> 1.674</td> <td> 11.380</td> <td> 0.000</td> <td> 15.615</td> <td> 22.485</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Group B</th> <td> 24.9500</td> <td> 1.674</td> <td> 14.904</td> <td> 0.000</td> <td> 21.515</td> <td> 28.385</td>\n",
+ " <th>x2</th> <td> 24.9500</td> <td> 1.674</td> <td> 14.904</td> <td> 0.000</td> <td> 21.515</td> <td> 28.385</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Group C</th> <td> 20.3500</td> <td> 1.674</td> <td> 12.156</td> <td> 0.000</td> <td> 16.915</td> <td> 23.785</td>\n",
+ " <th>x3</th> <td> 20.3500</td> <td> 1.674</td> <td> 12.156</td> <td> 0.000</td> <td> 16.915</td> <td> 23.785</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
@@ -1414,20 +1610,59 @@
" <th>Skew:</th> <td> 0.336</td> <th> Prob(JB): </th> <td> 0.717</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Kurtosis:</th> <td> 2.715</td> <th> Cond. No. </th> <td>2.39e+16</td>\n",
+ " <th>Kurtosis:</th> <td> 2.715</td> <th> Cond. No. </th> <td>2.43e+16</td>\n",
"</tr>\n",
- "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 6.98e-32. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
+ "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 6.79e-32. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
+ ],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\toprule\n",
+ "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared: } & 0.149 \\\\\n",
+ "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.086 \\\\\n",
+ "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 2.358 \\\\\n",
+ "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Prob (F-statistic):} & 0.114 \\\\\n",
+ "\\textbf{Time:} & 16:37:44 & \\textbf{ Log-Likelihood: } & -96.604 \\\\\n",
+ "\\textbf{No. Observations:} & 30 & \\textbf{ AIC: } & 199.2 \\\\\n",
+ "\\textbf{Df Residuals:} & 27 & \\textbf{ BIC: } & 203.4 \\\\\n",
+ "\\textbf{Df Model:} & 2 & \\textbf{ } & \\\\\n",
+ "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lcccccc}\n",
+ " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
+ "\\midrule\n",
+ "\\textbf{const} & 64.3500 & 0.874 & 73.606 & 0.000 & 62.556 & 66.144 \\\\\n",
+ "\\textbf{x1} & 19.0500 & 1.674 & 11.380 & 0.000 & 15.615 & 22.485 \\\\\n",
+ "\\textbf{x2} & 24.9500 & 1.674 & 14.904 & 0.000 & 21.515 & 28.385 \\\\\n",
+ "\\textbf{x3} & 20.3500 & 1.674 & 12.156 & 0.000 & 16.915 & 23.785 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\textbf{Omnibus:} & 0.758 & \\textbf{ Durbin-Watson: } & 1.379 \\\\\n",
+ "\\textbf{Prob(Omnibus):} & 0.684 & \\textbf{ Jarque-Bera (JB): } & 0.665 \\\\\n",
+ "\\textbf{Skew:} & 0.336 & \\textbf{ Prob(JB): } & 0.717 \\\\\n",
+ "\\textbf{Kurtosis:} & 2.715 & \\textbf{ Cond. No. } & 2.43e+16 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "%\\caption{OLS Regression Results}\n",
+ "\\end{center}\n",
+ "\n",
+ "Notes: \\newline\n",
+ " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
+ " [2] The smallest eigenvalue is 6.79e-32. This might indicate that there are \\newline\n",
+ " strong multicollinearity problems or that the design matrix is singular."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
- "Dep. Variable: Y R-squared: 0.149\n",
+ "Dep. Variable: y R-squared: 0.149\n",
"Model: OLS Adj. R-squared: 0.086\n",
"Method: Least Squares F-statistic: 2.358\n",
- "Date: Mon, 26 Sep 2022 Prob (F-statistic): 0.114\n",
- "Time: 01:44:31 Log-Likelihood: -96.604\n",
+ "Date: Mon, 21 Aug 2023 Prob (F-statistic): 0.114\n",
+ "Time: 16:37:44 Log-Likelihood: -96.604\n",
"No. Observations: 30 AIC: 199.2\n",
"Df Residuals: 27 BIC: 203.4\n",
"Df Model: 2 \n",
@@ -1435,42 +1670,32 @@
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
- "Intercept 64.3500 0.874 73.606 0.000 62.556 66.144\n",
- "Group A 19.0500 1.674 11.380 0.000 15.615 22.485\n",
- "Group B 24.9500 1.674 14.904 0.000 21.515 28.385\n",
- "Group C 20.3500 1.674 12.156 0.000 16.915 23.785\n",
+ "const 64.3500 0.874 73.606 0.000 62.556 66.144\n",
+ "x1 19.0500 1.674 11.380 0.000 15.615 22.485\n",
+ "x2 24.9500 1.674 14.904 0.000 21.515 28.385\n",
+ "x3 20.3500 1.674 12.156 0.000 16.915 23.785\n",
"==============================================================================\n",
"Omnibus: 0.758 Durbin-Watson: 1.379\n",
"Prob(Omnibus): 0.684 Jarque-Bera (JB): 0.665\n",
"Skew: 0.336 Prob(JB): 0.717\n",
- "Kurtosis: 2.715 Cond. No. 2.39e+16\n",
+ "Kurtosis: 2.715 Cond. No. 2.43e+16\n",
"==============================================================================\n",
"\n",
"Notes:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
- "[2] The smallest eigenvalue is 6.98e-32. This might indicate that there are\n",
+ "[2] The smallest eigenvalue is 6.79e-32. This might indicate that there are\n",
"strong multicollinearity problems or that the design matrix is singular.\n",
"\"\"\""
]
},
+ "execution_count": 27,
"metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
- "overdefined_model = sm.OLS(endog, exog).fit()\n",
- "display(overdefined_model.summary())\n",
- "sns.boxplot(x='Group', y='Y', data=dataframe);"
+ "overdefined_model = sm.OLS(endog, overdefined_exog).fit()\n",
+ "overdefined_model.summary()"
]
},
{
@@ -1503,9 +1728,9 @@
"source": [
"Wilkinson formulae were introduced in the S language and popularized with the advent of the R language.\n",
"\n",
- "In Python, this formalism is implemented by the [`patsy`](https://patsy.readthedocs.io/en/latest/formulas.html) package, required by `statsmodels`, with [minor differences](https://patsy.readthedocs.io/en/latest/R-comparison.html#r-comparison) with R.\n",
+ "In Python, this formalism is implemented by the [patsy](https://patsy.readthedocs.io/en/latest/formulas.html) package, required by statsmodels, with [minor differences](https://patsy.readthedocs.io/en/latest/R-comparison.html#r-comparison) with R.\n",
"\n",
- "As categorical variables may be encoded as numerical values -- in which case `patsy` cannot guess these variables are categorical, it is good practice to always tag these variables as categorical with the `C()` function in the formula, *e.g.* `C(Group)`.\n",
+ "As categorical variables may be encoded as numerical values -- in which case patsy cannot guess these variables are categorical, it is good practice to always tag these variables as categorical with the `C()` function in the formula, *e.g.* `C(Group)`.\n",
"\n",
"The intercept is implicit; `Y ~ X` and `Y ~ 1 + X` are equivalent formulae. The intercept can be excluded making its contribution negative or representing it with an explicit zero: `Y ~ X - 1` or `Y ~ 0 + X`.\n",
"\n",
@@ -1514,7 +1739,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 28,
"id": "d3de4753-0da7-4fe9-8412-eff18d66accf",
"metadata": {
"hidden": true,
@@ -1530,8 +1755,8 @@
"Dep. Variable: Y R-squared: 0.149\n",
"Model: OLS Adj. R-squared: 0.086\n",
"Method: Least Squares F-statistic: 2.358\n",
- "Date: Mon, 26 Sep 2022 Prob (F-statistic): 0.114\n",
- "Time: 01:44:32 Log-Likelihood: -96.604\n",
+ "Date: Mon, 21 Aug 2023 Prob (F-statistic): 0.114\n",
+ "Time: 16:37:44 Log-Likelihood: -96.604\n",
"No. Observations: 30 AIC: 199.2\n",
"Df Residuals: 27 BIC: 203.4\n",
"Df Model: 2 \n",
@@ -1576,12 +1801,12 @@
"\n",
"`A*B` is a common short-hand for `A + B + A:B`.\n",
"\n",
- "How `patsy` translates a formula can be checked as follows:"
+ "How patsy translates a formula can be checked as follows:"
]
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 29,
"id": "44932add-b649-4c6d-a88f-421549bc9481",
"metadata": {
"hidden": true
@@ -1618,7 +1843,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 30,
"id": "b3fdcfa3-fe7a-48f3-9a2f-b01723a6e440",
"metadata": {
"hidden": true,
@@ -1646,7 +1871,7 @@
"tags": []
},
"source": [
- "Per default, `patsy` evaluates the terms in the caller namespace (here the global namespace), so that we can apply local functions to variables right in the formulae.\n",
+ "Per default, patsy evaluates the terms in the caller namespace (here the global namespace), so that we can apply local functions to variables right in the formulae.\n",
"A common usage consists of calling `np.log`.\n",
"\n",
"However, this may conflict with previously defined object names, especially in sandbox environment such as a notebook...\n",
@@ -1656,7 +1881,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 31,
"id": "e3e4e71e-27a4-4c96-ab41-050f4f7597d6",
"metadata": {
"hidden": true,
@@ -1667,74 +1892,6 @@
"fitted_model = smf.ols('Y ~ C(Group)', data=dataframe, eval_env=-1).fit()"
]
},
- {
- "cell_type": "markdown",
- "id": "4d5ea642-c933-44d8-9995-e1fe1a2d71d4",
- "metadata": {
- "hidden": true,
- "tags": []
- },
- "source": [
- "Advice: encapsulate the call to `smf.ols` so that `eval_env` is `-1` per default, and you have to explicitly set `eval_env=0` to make user-defined functions available:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "552fc5c1-3c95-445b-a91a-41e85d606a41",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": [
- "def ols(*args, eval_env=-1, **kwargs):\n",
- " return smf.ols(*args, eval_env=eval_env+1, **kwargs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "6768710f-e796-4452-8c52-1ca6f484f170",
- "metadata": {
- "hidden": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "C = lambda x: undefined_symbol\n",
- "\n",
- "ols('Y ~ C(Group)', data=dataframe);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "4be7d27c-bfdf-42c5-8820-62bc0ed65ca8",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": [
- "ols('np.log(Y) ~ C(Group)', data=dataframe);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "id": "c55b7dd6-a22c-46af-aabb-6128441aa228",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": [
- "del C\n",
- "\n",
- "def my_function(x):\n",
- " return np.sqrt(x)\n",
- "\n",
- "ols('my_function(Y) ~ C(Group)', data=dataframe, eval_env=0); # crashes if C is defined"
- ]
- },
{
"cell_type": "markdown",
"id": "88c895a7",
@@ -1742,7 +1899,7 @@
"hidden": true
},
"source": [
- "Other issue: if a variable name is a Python reserved keyword (*e.g.* *yield*), the variable must be renamed."
+ "Other issue: if a variable name is a Python reserved keyword (*e.g.* `yield`), the variable must be renamed; there is no other workarounds."
]
},
{
@@ -1750,7 +1907,6 @@
"id": "59e28a7a-e821-480b-afd7-8604611f4185",
"metadata": {
"heading_collapsed": true,
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@@ -1769,7 +1925,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 32,
"id": "4d947388-de44-4f13-b2c0-e8b392856b79",
"metadata": {
"hidden": true
@@ -1785,6 +1941,30 @@
" ])})"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "9ea0c1f5-fb3f-4110-b824-9a7c6018c35e",
+ "metadata": {
+ "hidden": true,
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "statsmodels_material.illustration_2way_data(plant_data)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "5c99a247",
@@ -1797,14 +1977,14 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 34,
"id": "215e908f-2c5f-4ec3-9eb4-8cb67f75a2f6",
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
- "plant_model = ols('height ~ water + sun', data=plant_data).fit()"
+ "plant_model = smf.ols('height ~ water + sun', data=plant_data).fit()"
]
},
{
@@ -1819,38 +1999,103 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 35,
"id": "cc06b3ed-a066-4de3-884c-a7c82729c359",
"metadata": {
"hidden": true
},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " sum_sq df F PR(>F)\n",
- "Intercept 394.218750 1.0 406.443314 2.139588e-17\n",
- "water 15.552000 1.0 16.034261 4.623155e-04\n",
- "sun 21.424667 2.0 11.044518 3.373296e-04\n",
- "Residual 25.218000 26.0 NaN NaN\n"
- ]
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sum_sq</th>\n",
+ " <th>df</th>\n",
+ " <th>F</th>\n",
+ " <th>PR(>F)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Intercept</th>\n",
+ " <td>394.218750</td>\n",
+ " <td>1.0</td>\n",
+ " <td>406.443314</td>\n",
+ " <td>2.139588e-17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>water</th>\n",
+ " <td>15.552000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>16.034261</td>\n",
+ " <td>4.623155e-04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>sun</th>\n",
+ " <td>21.424667</td>\n",
+ " <td>2.0</td>\n",
+ " <td>11.044518</td>\n",
+ " <td>3.373296e-04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Residual</th>\n",
+ " <td>25.218000</td>\n",
+ " <td>26.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sum_sq df F PR(>F)\n",
+ "Intercept 394.218750 1.0 406.443314 2.139588e-17\n",
+ "water 15.552000 1.0 16.034261 4.623155e-04\n",
+ "sun 21.424667 2.0 11.044518 3.373296e-04\n",
+ "Residual 25.218000 26.0 NaN NaN"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "anova_table = sm.stats.anova_lm(plant_model, typ=3) # typ specifies the type of sum of squares\n",
- "print(anova_table)"
+ "sm.stats.anova_lm(plant_model, typ=3) # `typ` specifies the type of sum of squares"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "05bb6951-98ae-4c7b-8919-d9a9475271aa",
+ "metadata": {},
+ "source": [
+ "Here, `anova_lm` prints more useful information than the omnibus statistic given by `summary`:"
]
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 36,
"id": "ff91aa79-fb03-4b42-9129-8d5abd2add43",
"metadata": {
"hidden": true,
- "jupyter": {
- "source_hidden": true
- },
"tags": []
},
"outputs": [
@@ -1863,33 +2108,18 @@
"Dep. Variable: height R-squared: 0.595\n",
"Model: OLS Adj. R-squared: 0.548\n",
"Method: Least Squares F-statistic: 12.71\n",
- "Date: Mon, 26 Sep 2022 Prob (F-statistic): 2.64e-05\n",
- "Time: 01:44:33 Log-Likelihood: -39.964\n",
+ "Date: Mon, 21 Aug 2023 Prob (F-statistic): 2.64e-05\n",
+ "Time: 16:37:44 Log-Likelihood: -39.964\n",
"No. Observations: 30 AIC: 87.93\n",
"Df Residuals: 26 BIC: 93.53\n",
"Df Model: 3 \n",
"Covariance Type: nonrobust \n",
- "===================================================================================\n",
- " coef std err t P>|t| [0.025 0.975]\n",
- "-----------------------------------------------------------------------------------\n",
- "Intercept 7.2500 0.360 20.160 0.000 6.511 7.989\n",
- "water[T.weekly] -1.4400 0.360 -4.004 0.000 -2.179 -0.701\n",
- "sun[T.low] -1.9200 0.440 -4.359 0.000 -2.825 -1.015\n",
- "sun[T.med] -1.6300 0.440 -3.701 0.001 -2.535 -0.725\n",
- "==============================================================================\n",
- "Omnibus: 0.747 Durbin-Watson: 1.546\n",
- "Prob(Omnibus): 0.688 Jarque-Bera (JB): 0.794\n",
- "Skew: -0.226 Prob(JB): 0.672\n",
- "Kurtosis: 2.343 Cond. No. 4.22\n",
- "==============================================================================\n",
- "\n",
- "Notes:\n",
- "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+ "==============================================================================\n"
]
}
],
"source": [
- "print(plant_model.summary())"
+ "print(plant_model.summary().tables[0])"
]
},
{
@@ -1899,12 +2129,12 @@
"hidden": true
},
"source": [
- "...and have a look at the coefficients:"
+ "If we look at the coefficients:"
]
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 37,
"id": "36130c37-c833-4953-9983-7c92bfdbe2e6",
"metadata": {
"hidden": true
@@ -1920,7 +2150,7 @@
"dtype: float64"
]
},
- "execution_count": 35,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
@@ -1936,58 +2166,23 @@
"hidden": true
},
"source": [
- "If we plot them together with the data...:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 99,
- "id": "9ea0c1f5-fb3f-4110-b824-9a7c6018c35e",
- "metadata": {
- "hidden": true,
- "jupyter": {
- "source_hidden": true
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.boxplot(data=plant_data, x='sun', y='height', hue='water')\n",
- "ax = sns.swarmplot(data=plant_data, x='sun', y='height', hue='water', dodge=True, palette='dark:k');\n",
- "#ax.legend([], frameon=False);\n",
+ "We can see the intercept now represents the `water=daily,sun=high` group, and -- for example -- coefficent `water[T.weekly]` encodes the difference between the intercept and the `water=weekly,sun=high` group.\n",
"\n",
- "# redraw the legend\n",
- "import matplotlib\n",
- "colored_patches = [ child for child in ax.get_children() if isinstance(child, matplotlib.patches.Rectangle) ][:-1]\n",
- "ax.legend(colored_patches, [ patch.get_label() for patch in colored_patches ], title='water');"
+ "Let us reconstruct the group means using the coefficients:"
]
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 38,
"id": "4f4785b3-b798-416a-9a53-1d5b873117ce",
"metadata": {
"hidden": true,
- "jupyter": {
- "source_hidden": true
- },
"tags": []
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -2023,7 +2218,7 @@
"hidden": true
},
"source": [
- "...we can appreciate the equal daily-weekly differences do not quite match the variability across the levels of the `sun` factor.\n",
+ "We can appreciate the equal daily-weekly differences do not quite match the variability across the levels of the `sun` factor. As a result, the group means are not well represented, including that of the group the intercept is supposed to represent.\n",
"\n",
"This inter-factor dependence is called an *interaction*.\n",
"\n",
@@ -2034,7 +2229,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 39,
"id": "60de13e5-b798-4312-8d06-0ef79f480760",
"metadata": {
"hidden": true
@@ -2044,34 +2239,23 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " sum_sq df F PR(>F)\n",
- "Intercept 246.402000 1.0 302.891211 4.094979e-15\n",
- "water 2.401000 1.0 2.951444 9.867817e-02\n",
- "sun 8.041333 2.0 4.942430 1.593920e-02\n",
- "water:sun 5.694000 2.0 3.499693 4.637649e-02\n",
- "Residual 19.524000 24.0 NaN NaN\n"
+ " df sum_sq mean_sq F PR(>F)\n",
+ "water 1.0 15.552000 15.552000 19.117394 0.000205\n",
+ "sun 2.0 21.424667 10.712333 13.168203 0.000138\n",
+ "water:sun 2.0 5.694000 2.847000 3.499693 0.046376\n",
+ "Residual 24.0 19.524000 0.813500 NaN NaN\n"
]
}
],
"source": [
- "model_with_interaction = ols('height ~ water * sun', data=plant_data).fit()\n",
+ "model_with_interaction = smf.ols('height ~ water * sun', data=plant_data).fit()\n",
"# remember `water * sun` is equivalent to `water + sun + water:sun`\n",
- "print(sm.stats.anova_lm(model_with_interaction, typ=3))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bec6f02b",
- "metadata": {
- "hidden": true
- },
- "source": [
- "Argument `typ` specifies the type of sum of squares. Type 3 is often used for ANOVA because it does not depend on the order of the factors."
+ "print(sm.stats.anova_lm(model_with_interaction))"
]
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 40,
"id": "6141c9fa-aab9-45d5-91e3-a41e8fe8587d",
"metadata": {
"hidden": true
@@ -2089,7 +2273,7 @@
"dtype: float64"
]
},
- "execution_count": 39,
+ "execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
@@ -2100,7 +2284,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 41,
"id": "b4de4c0a-ada5-4df1-98bc-43bc7f038cc4",
"metadata": {
"hidden": true
@@ -2108,7 +2292,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -2144,166 +2328,380 @@
"hidden": true
},
"source": [
- "`statsmodels` features a `interaction_plot` helper function.\n",
- "\n",
- "We still need quite some boilerplate to make it play nicely with `swarmplot`, but the code below is easier to generalize and wrap into a function:"
+ "statsmodels features an `interaction_plot` helper function, but it does not play nicely with seaborn's `swarmplot` for example and, as a result, we wrap it into another function (see the *statsmodels_material.py* file):"
]
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 42,
"id": "811df467-f3ec-4ae4-939c-a8917c2f9d04",
"metadata": {
"hidden": true,
- "jupyter": {
- "source_hidden": true
- },
"tags": []
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "statsmodels_material.interaction_plot(plant_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "846a640e",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "### Treating interaction"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2a6451f",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "As we found significant interaction, we should rerun the ANOVA in the shape of one-way ANOVA, with one factor, for each level of the other factor, and possibly vice-versa."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3fe8ac1f",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "<table><tr><td><img src=\"img/two-way-anova-interaction-significant-flowchart.png\" /></td></tr>\n",
+ "<tr><td><a href=\"https://www.spss-tutorials.com/spss-two-way-anova-interaction-significant/\">SPSS recommendation for two-way ANOVA interaction</a></td></tr></table>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "c380d42c",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": [
+ "daily_water_model = smf.ols('height ~ sun', data=plant_data[plant_data['water']=='daily']).fit()\n",
+ "weekly_water_model = smf.ols('height ~ sun', data=plant_data[plant_data['water']=='weekly']).fit()\n",
+ "low_sun_model = smf.ols('height ~ water', data=plant_data[plant_data['sun']=='low']).fit()\n",
+ "med_sun_model = smf.ols('height ~ water', data=plant_data[plant_data['sun']=='med']).fit()\n",
+ "high_sun_model = smf.ols('height ~ water', data=plant_data[plant_data['sun']=='high']).fit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a39e6bd5",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "If main effects are found to be significant, we can proceed to performing post-hoc tests."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "93ccdd87",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.03098093333325329"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "daily_water_model.f_pvalue"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "d1392464",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>coef</th>\n",
+ " <th>std err</th>\n",
+ " <th>t</th>\n",
+ " <th>P>|t|</th>\n",
+ " <th>Conf. Int. Low</th>\n",
+ " <th>Conf. Int. Upp.</th>\n",
+ " <th>pvalue-hs</th>\n",
+ " <th>reject-hs</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>low-high</th>\n",
+ " <td>-1.08</td>\n",
+ " <td>0.58458</td>\n",
+ " <td>-1.847481</td>\n",
+ " <td>0.089466</td>\n",
+ " <td>-2.35369</td>\n",
+ " <td>0.19369</td>\n",
+ " <td>0.170928</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>med-high</th>\n",
+ " <td>-1.78</td>\n",
+ " <td>0.58458</td>\n",
+ " <td>-3.044923</td>\n",
+ " <td>0.010180</td>\n",
+ " <td>-3.05369</td>\n",
+ " <td>-0.50631</td>\n",
+ " <td>0.030231</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>med-low</th>\n",
+ " <td>-0.70</td>\n",
+ " <td>0.58458</td>\n",
+ " <td>-1.197442</td>\n",
+ " <td>0.254253</td>\n",
+ " <td>-1.97369</td>\n",
+ " <td>0.57369</td>\n",
+ " <td>0.254253</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " coef std err t P>|t| Conf. Int. Low Conf. Int. Upp. \\\n",
+ "low-high -1.08 0.58458 -1.847481 0.089466 -2.35369 0.19369 \n",
+ "med-high -1.78 0.58458 -3.044923 0.010180 -3.05369 -0.50631 \n",
+ "med-low -0.70 0.58458 -1.197442 0.254253 -1.97369 0.57369 \n",
+ "\n",
+ " pvalue-hs reject-hs \n",
+ "low-high 0.170928 False \n",
+ "med-high 0.030231 True \n",
+ "med-low 0.254253 False "
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "daily_water_posthoc = daily_water_model.t_test_pairwise('sun')\n",
+ "daily_water_posthoc.result_frame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "8724bf04",
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
"text/plain": [
- "<Figure size 640x480 with 1 Axes>"
+ "0.001224005685747233"
]
},
+ "execution_count": 46,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
- "ax = sns.swarmplot(data=plant_data, x='water', y='height', hue='sun');\n",
- "\n",
- "# get the colors used by swarmplot, to tell interaction_plot which colors to use;\n",
- "# and get the objects drawn by swarmplot to redraw the legend after calling interaction_plot\n",
- "sun_levels = np.unique(plant_data['sun'])\n",
- "colors = {}\n",
- "colored_points = []\n",
- "for child in ax.get_children():\n",
- " if child.get_label() in sun_levels:\n",
- " colors[child.get_label()] = child.get_facecolor()\n",
- " colored_points.append(child)\n",
- "\n",
- "# interaction plot\n",
- "from statsmodels.graphics.factorplots import interaction_plot\n",
- "colors = [ colors[sun] for sun in np.sort(sun_levels) ]\n",
- "interaction_plot(x=plant_data['water'], trace=plant_data['sun'], response=plant_data['height'],\n",
- " ax=ax, colors=colors, markers='d'*len(sun_levels), markerfacecolor='w');\n",
- "\n",
- "# redraw the legend to remove the duplicates from interaction_plot\n",
- "ax.legend(colored_points, [ points.get_label() for points in colored_points ], title='sun');"
+ "weekly_water_model.f_pvalue"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "6dc3a0ad",
+ "metadata": {
+ "hidden": true,
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "weekly_water_posthoc = weekly_water_model.t_test_pairwise('sun').result_frame"
]
},
{
"cell_type": "markdown",
- "id": "846a640e",
+ "id": "98126f2f",
"metadata": {
"hidden": true
},
"source": [
- "### Treating interaction"
+ "Problem: although `t_test_pairwise` includes a correction for multiple comparisons, this correction does not account for the multiple calls to `t_test_pairwise` we perform on the same data."
]
},
{
"cell_type": "markdown",
- "id": "c2a6451f",
+ "id": "48953eb2-9d8b-42e4-b086-144f07a54359",
"metadata": {
- "hidden": true
+ "heading_collapsed": true,
+ "tags": []
},
"source": [
- "As we found significant interaction, we should rerun the ANOVA in the shape of one-way ANOVA, with one factor, for each level of the other factor, and possibly vice-versa."
+ "## Post-hoc tests and multiple comparisons"
]
},
{
"cell_type": "markdown",
- "id": "3fe8ac1f",
+ "id": "62d4382a",
"metadata": {
+ "heading_collapsed": true,
"hidden": true
},
"source": [
- "<table><tr><td><img src=\"img/two-way-anova-interaction-significant-flowchart.png\" /></td></tr>\n",
- "<tr><td><a href=\"https://www.spss-tutorials.com/spss-two-way-anova-interaction-significant/\">SPSS recommendation for two-way ANOVA interaction</a></td></tr></table>"
+ "### The multiple comparisons problem"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c1f10e1-d677-4ef6-b53c-7c80f8cf64ea",
+ "metadata": {},
+ "source": [
+ "Let us perform 1,200 tests whom 100 should lead to a significant different. The test used features the following properties:"
]
},
{
"cell_type": "code",
- "execution_count": 42,
- "id": "c380d42c",
+ "execution_count": 48,
+ "id": "7f395588",
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
- "daily_water_model = ols('height ~ sun', data=plant_data[plant_data['water']=='daily']).fit()\n",
- "weekly_water_model = ols('height ~ sun', data=plant_data[plant_data['water']=='weekly']).fit()\n",
- "low_sun_model = ols('height ~ water', data=plant_data[plant_data['sun']=='low']).fit()\n",
- "med_sun_model = ols('height ~ water', data=plant_data[plant_data['sun']=='med']).fit()\n",
- "high_sun_model = ols('height ~ water', data=plant_data[plant_data['sun']=='high']).fit()"
+ "power = 0.8\n",
+ "type1_error_rate = 0.05"
]
},
{
"cell_type": "markdown",
- "id": "a39e6bd5",
+ "id": "167951da",
"metadata": {
"hidden": true
},
"source": [
- "If main effects are found to be significant, we can proceed to performing post-hoc tests."
+ "Red pixels represent the tests (comparisons) for which $H_0$ is false (right figure) or rejected (left figure):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "8642f0e6",
+ "metadata": {
+ "hidden": true,
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1330x410 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "statsmodels_material.illustration_multiple_comparisons(power, type1_error_rate)"
]
},
{
"cell_type": "markdown",
- "id": "5ec1da0e-1bd1-42af-8a6e-c4effe40cef0",
+ "id": "6bdba34b",
"metadata": {
"hidden": true
},
"source": [
- "### Types of sums of squares\n",
- "\n",
- "To quantify the contribution of each term to the model, we can choose between three ways of decomposing the total variance or sum of squares.\n",
- "\n",
- "Let us consider the following model: `Y ~ A + B + A:B`\n",
- "\n",
- "#### Type-1\n",
- "\n",
- "* A's contribution will evaluated comparing `Y ~ A` vs `Y ~ 1`\n",
- "* B: `Y ~ A + B` vs `Y ~ A`\n",
- "* A:B (interaction term): `Y ~ A + B + A:B` vs `Y ~ A + B`\n",
- "\n",
- "#### Type-2\n",
- "\n",
- "* A: `Y ~ A + B` vs `Y ~ B`\n",
- "* B: `Y ~ A + B` vs `Y ~ A`\n",
- "* A:B: `Y ~ A + B + A:B` vs `Y ~ A + B`\n",
- "\n",
- "Type-2 is often chosen for regression problems (with continuous predictors).\n",
+ "The top right group of tests with false $H_0$ is affected by type-2 errors, or equivalently by the power of the test (`power = 1 - type2_error_rate`).\n",
"\n",
- "#### Type-3\n",
+ "The original $5%$ significance level of the test translates into $5%$ type-1 errors that become visible when the test is applied many times as we did above.\n",
"\n",
- "* A: `Y ~ A + B + A:B` vs `Y ~ B + A:B`\n",
- "* B: `Y ~ A + B + A:B` vs `Y ~ A + A:B`\n",
- "* A:B: `Y ~ A + B + A:B` vs `Y ~ A + B`\n",
+ "We want the significance level to apply to the \"whole picture\", and to control the *family-wise error rate*. Basically, we want our $5%$ level to upper-bound the risk of erroneously rejecting any single $H_0$ (or more).\n",
"\n",
- "Type-3 is suitable for multi-factorial designs (with several categorical factors)."
+ "This can be done with a procedure called *correction for multiple comparisons*."
]
},
{
"cell_type": "markdown",
- "id": "48953eb2-9d8b-42e4-b086-144f07a54359",
+ "id": "48d0f081",
"metadata": {
"heading_collapsed": true,
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
+ "hidden": true
},
"source": [
- "## Post-hoc tests and multiple comparisons"
+ "### multipletests"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b7ea24c3",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "If we consider all 5 factored models, we may proceed to performing up to 9 comparisons, but again `t_test_pairwise` would not properly take this into account.\n",
+ "\n",
+ "Note that you do not need to perform all possible comparisons. Choose what comparisons you are interested in, but do so prior to performing them.\n",
+ "\n",
+ "We should use [multipletests](https://www.statsmodels.org/stable/generated/statsmodels.stats.multitest.multipletests.html) instead, for the purpose of correcting the $p$-values:"
]
},
{
"cell_type": "code",
- "execution_count": 43,
- "id": "93ccdd87",
+ "execution_count": 50,
+ "id": "1b9e153d",
"metadata": {
"hidden": true
},
@@ -2311,22 +2709,37 @@
{
"data": {
"text/plain": [
- "0.03098093333325329"
+ "7"
]
},
- "execution_count": 43,
+ "execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "daily_water_model.f_pvalue"
+ "from statsmodels.stats.multitest import multipletests\n",
+ "\n",
+ "significance_level = 0.05\n",
+ "\n",
+ "all_comparisons = []\n",
+ "for factor1, factor2 in (('sun', 'water'), ('water', 'sun')):\n",
+ " for f2_level in np.unique(plant_data[factor2]):\n",
+ " model = smf.ols(f'height ~ {factor1}', data=plant_data[plant_data[factor2]==f2_level]).fit()\n",
+ " if model.f_pvalue <= significance_level:\n",
+ " model_name = f'{factor2}={f2_level}'\n",
+ " pairwise_tests = model.t_test_pairwise(factor1).result_frame\n",
+ " pairwise_tests.index = [ f'{comparison}[{model_name}]' for comparison in pairwise_tests.index ]\n",
+ " all_comparisons.append(pairwise_tests)\n",
+ "all_comparisons = pd.concat(all_comparisons)\n",
+ "\n",
+ "len(all_comparisons)"
]
},
{
"cell_type": "code",
- "execution_count": 44,
- "id": "d1392464",
+ "execution_count": 51,
+ "id": "2fe99f26",
"metadata": {
"hidden": true
},
@@ -2364,94 +2777,244 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>low-high</th>\n",
+ " <th>low-high[water=daily]</th>\n",
" <td>-1.08</td>\n",
- " <td>0.58458</td>\n",
+ " <td>0.584580</td>\n",
" <td>-1.847481</td>\n",
" <td>0.089466</td>\n",
- " <td>-2.35369</td>\n",
- " <td>0.19369</td>\n",
+ " <td>-2.353690</td>\n",
+ " <td>0.193690</td>\n",
" <td>0.170928</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>med-high</th>\n",
+ " <th>med-high[water=daily]</th>\n",
" <td>-1.78</td>\n",
- " <td>0.58458</td>\n",
+ " <td>0.584580</td>\n",
" <td>-3.044923</td>\n",
" <td>0.010180</td>\n",
- " <td>-3.05369</td>\n",
- " <td>-0.50631</td>\n",
- " <td>0.030231</td>\n",
+ " <td>-3.053690</td>\n",
+ " <td>-0.506310</td>\n",
+ " <td>0.049876</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>med-low</th>\n",
+ " <th>med-low[water=daily]</th>\n",
" <td>-0.70</td>\n",
- " <td>0.58458</td>\n",
+ " <td>0.584580</td>\n",
" <td>-1.197442</td>\n",
" <td>0.254253</td>\n",
- " <td>-1.97369</td>\n",
- " <td>0.57369</td>\n",
+ " <td>-1.973690</td>\n",
+ " <td>0.573690</td>\n",
" <td>0.254253</td>\n",
" <td>False</td>\n",
" </tr>\n",
+ " <tr>\n",
+ " <th>low-high[water=weekly]</th>\n",
+ " <td>-2.76</td>\n",
+ " <td>0.555938</td>\n",
+ " <td>-4.964586</td>\n",
+ " <td>0.000328</td>\n",
+ " <td>-3.971284</td>\n",
+ " <td>-1.548716</td>\n",
+ " <td>0.002279</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>med-high[water=weekly]</th>\n",
+ " <td>-1.48</td>\n",
+ " <td>0.555938</td>\n",
+ " <td>-2.662169</td>\n",
+ " <td>0.020708</td>\n",
+ " <td>-2.691284</td>\n",
+ " <td>-0.268716</td>\n",
+ " <td>0.080295</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>med-low[water=weekly]</th>\n",
+ " <td>1.28</td>\n",
+ " <td>0.555938</td>\n",
+ " <td>2.302416</td>\n",
+ " <td>0.040022</td>\n",
+ " <td>0.068716</td>\n",
+ " <td>2.491284</td>\n",
+ " <td>0.115325</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>weekly-daily[sun=low]</th>\n",
+ " <td>-2.66</td>\n",
+ " <td>0.443847</td>\n",
+ " <td>-5.993059</td>\n",
+ " <td>0.000326</td>\n",
+ " <td>-3.683513</td>\n",
+ " <td>-1.636487</td>\n",
+ " <td>0.002279</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " coef std err t P>|t| Conf. Int. Low Conf. Int. Upp. \\\n",
- "low-high -1.08 0.58458 -1.847481 0.089466 -2.35369 0.19369 \n",
- "med-high -1.78 0.58458 -3.044923 0.010180 -3.05369 -0.50631 \n",
- "med-low -0.70 0.58458 -1.197442 0.254253 -1.97369 0.57369 \n",
+ " coef std err t P>|t| Conf. Int. Low \\\n",
+ "low-high[water=daily] -1.08 0.584580 -1.847481 0.089466 -2.353690 \n",
+ "med-high[water=daily] -1.78 0.584580 -3.044923 0.010180 -3.053690 \n",
+ "med-low[water=daily] -0.70 0.584580 -1.197442 0.254253 -1.973690 \n",
+ "low-high[water=weekly] -2.76 0.555938 -4.964586 0.000328 -3.971284 \n",
+ "med-high[water=weekly] -1.48 0.555938 -2.662169 0.020708 -2.691284 \n",
+ "med-low[water=weekly] 1.28 0.555938 2.302416 0.040022 0.068716 \n",
+ "weekly-daily[sun=low] -2.66 0.443847 -5.993059 0.000326 -3.683513 \n",
"\n",
- " pvalue-hs reject-hs \n",
- "low-high 0.170928 False \n",
- "med-high 0.030231 True \n",
- "med-low 0.254253 False "
+ " Conf. Int. Upp. pvalue-hs reject-hs \n",
+ "low-high[water=daily] 0.193690 0.170928 False \n",
+ "med-high[water=daily] -0.506310 0.049876 True \n",
+ "med-low[water=daily] 0.573690 0.254253 False \n",
+ "low-high[water=weekly] -1.548716 0.002279 True \n",
+ "med-high[water=weekly] -0.268716 0.080295 False \n",
+ "med-low[water=weekly] 2.491284 0.115325 False \n",
+ "weekly-daily[sun=low] -1.636487 0.002279 True "
]
},
- "execution_count": 44,
+ "execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "daily_water_posthoc = daily_water_model.t_test_pairwise('sun')\n",
- "daily_water_posthoc.result_frame"
+ "all_comparisons['reject-hs'], all_comparisons['pvalue-hs'], _, _ = multipletests(all_comparisons['P>|t|'], alpha=significance_level)\n",
+ "all_comparisons"
]
},
{
"cell_type": "code",
- "execution_count": 45,
- "id": "8724bf04",
+ "execution_count": 52,
+ "id": "25b21f28-f70d-466e-bbb6-8a6fc86b3696",
"metadata": {
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"outputs": [
{
"data": {
+ "image/png": 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",
"text/plain": [
- "0.001224005685747233"
+ "<Figure size 640x480 with 1 Axes>"
]
},
- "execution_count": 45,
"metadata": {},
- "output_type": "execute_result"
+ "output_type": "display_data"
}
],
"source": [
- "weekly_water_model.f_pvalue"
+ "statsmodels_material.confidence_intervals(all_comparisons)"
]
},
{
- "cell_type": "code",
- "execution_count": 46,
- "id": "6dc3a0ad",
+ "cell_type": "markdown",
+ "id": "748acdc4",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "Beware: the confidence intervals from [t_test_pairwise](https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.OLSResults.t_test_pairwise.html)'s table are not corrected for multiple comparisons."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f436837b",
+ "metadata": {
+ "heading_collapsed": true,
+ "hidden": true
+ },
+ "source": [
+ "### Bonferroni, Šidák and Holm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aecb5d85-20d7-4261-a572-b6c891cb8aff",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "[`multipletests`](https://www.statsmodels.org/stable/generated/statsmodels.stats.multitest.multipletests.html) implements several correction procedures. The Holm correction with Šidák adjustments is the default method (`holm-sidak`).\n",
+ "\n",
+ "If we perform $n$ tests, the $p$-value for each test can be adjusted as follows:\n",
+ "\n",
+ "* Bonferroni adjustement : $p_{corrected} = np$\n",
+ "* Šidák adjustment : $p_{corrected} = 1 - ( 1 - p )^n$\n",
+ "\n",
+ "In the Holm's procedure, we sequentially consider each $p$-value, starting from the smallest one, and adjust them on basis of the number of remaining $p$-values to adjust.\n",
+ "Basically:\n",
+ "\n",
+ "1. the smallest $p$-value is adjusted considering $n$ multiple comparisons (because we have not adjusted any $p$-value yet),\n",
+ "2. the second smallest $p$-value is adjusted considering $n-1$ multiple comparisons (because we have already adjusted one $p$-value),\n",
+ "3. and so on.\n",
+ "\n",
+ "Compared with pingouin's [`multicomp`](https://pingouin-stats.org/build/html/generated/pingouin.multicomp.html#pingouin.multicomp), statsmodels' `multipletests` includes more False Discovery Rate (FDR)-based correction methods."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5ec1da0e-1bd1-42af-8a6e-c4effe40cef0",
"metadata": {
"hidden": true
},
+ "source": [
+ "## Types of sums of squares\n",
+ "\n",
+ "`anova_lm` takes an argument `typ` that can be any of `1`, `2` and `3`.\n",
+ "\n",
+ "Indeed, to quantify the contribution of each term to the model, we can choose between three ways of decomposing the total variance or sum of squares.\n",
+ "\n",
+ "Let us consider the following model: `Y ~ A + B + A:B`\n",
+ "\n",
+ "#### Type-1\n",
+ "\n",
+ "* A's contribution will evaluated comparing `Y ~ A` vs `Y ~ 1`\n",
+ "* B: `Y ~ A + B` vs `Y ~ A`\n",
+ "* A:B (interaction term): `Y ~ A + B + A:B` vs `Y ~ A + B`\n",
+ "\n",
+ "#### Type-2\n",
+ "\n",
+ "* A: `Y ~ A + B` vs `Y ~ B`\n",
+ "* B: `Y ~ A + B` vs `Y ~ A`\n",
+ "* A:B: `Y ~ A + B + A:B` vs `Y ~ A + B`\n",
+ "\n",
+ "Type-2 is often chosen for regression problems (with continuous predictors).\n",
+ "\n",
+ "#### Type-3\n",
+ "\n",
+ "* A: `Y ~ A + B + A:B` vs `Y ~ B + A:B`\n",
+ "* B: `Y ~ A + B + A:B` vs `Y ~ A + A:B`\n",
+ "* A:B: `Y ~ A + B + A:B` vs `Y ~ A + B`\n",
+ "\n",
+ "Type-3 is suitable for multi-factorial designs (with several categorical factors) and unbalanced groups."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fcc143f7-d3dd-4b19-ae05-9b639b1baf7c",
+ "metadata": {},
+ "source": [
+ "## Mixed-effects models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d2911be-e687-4170-8422-75a195b1cf84",
+ "metadata": {},
+ "source": [
+ "Let us consider the reaction-time dataset available at [osf.io/asq8n](https://osf.io/asq8n):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "4e203a8d-a9d5-43e0-a437-918d7e036aed",
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2474,551 +3037,1092 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>coef</th>\n",
- " <th>std err</th>\n",
- " <th>t</th>\n",
- " <th>P>|t|</th>\n",
- " <th>Conf. Int. Low</th>\n",
- " <th>Conf. Int. Upp.</th>\n",
- " <th>pvalue-hs</th>\n",
- " <th>reject-hs</th>\n",
+ " <th>0</th>\n",
+ " <th>1</th>\n",
+ " <th>2</th>\n",
+ " <th>3</th>\n",
+ " <th>4</th>\n",
+ " <th>5</th>\n",
+ " <th>6</th>\n",
+ " <th>7</th>\n",
+ " <th>8</th>\n",
+ " <th>9</th>\n",
+ " <th>...</th>\n",
+ " <th>740</th>\n",
+ " <th>741</th>\n",
+ " <th>742</th>\n",
+ " <th>743</th>\n",
+ " <th>744</th>\n",
+ " <th>745</th>\n",
+ " <th>746</th>\n",
+ " <th>747</th>\n",
+ " <th>748</th>\n",
+ " <th>749</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>subject</th>\n",
+ " <td>15</td>\n",
+ " <td>15</td>\n",
+ " <td>15</td>\n",
+ " <td>15</td>\n",
+ " <td>15</td>\n",
+ " <td>15</td>\n",
+ " <td>15</td>\n",
+ " <td>15</td>\n",
+ " <td>15</td>\n",
+ " <td>15</td>\n",
+ " <td>...</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>subject_name</th>\n",
+ " <td>Alex</td>\n",
+ " <td>Alex</td>\n",
+ " <td>Alex</td>\n",
+ " <td>Alex</td>\n",
+ " <td>Alex</td>\n",
+ " <td>Alex</td>\n",
+ " <td>Alex</td>\n",
+ " <td>Alex</td>\n",
+ " <td>Alex</td>\n",
+ " <td>Alex</td>\n",
+ " <td>...</td>\n",
+ " <td>Timo</td>\n",
+ " <td>Timo</td>\n",
+ " <td>Timo</td>\n",
+ " <td>Timo</td>\n",
+ " <td>Timo</td>\n",
+ " <td>Timo</td>\n",
+ " <td>Timo</td>\n",
+ " <td>Timo</td>\n",
+ " <td>Timo</td>\n",
+ " <td>Timo</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>gender</th>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>...</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " <td>male</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>item</th>\n",
+ " <td>stauge</td>\n",
+ " <td>roke</td>\n",
+ " <td>schuke</td>\n",
+ " <td>quade</td>\n",
+ " <td>jiete</td>\n",
+ " <td>gaude</td>\n",
+ " <td>flape</td>\n",
+ " <td>quope</td>\n",
+ " <td>priege</td>\n",
+ " <td>mube</td>\n",
+ " <td>...</td>\n",
+ " <td>blote</td>\n",
+ " <td>wiebe</td>\n",
+ " <td>drute</td>\n",
+ " <td>frade</td>\n",
+ " <td>gage</td>\n",
+ " <td>griede</td>\n",
+ " <td>nauge</td>\n",
+ " <td>schrieke</td>\n",
+ " <td>klape</td>\n",
+ " <td>gobe</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>item_singular</th>\n",
+ " <td>staug</td>\n",
+ " <td>rok</td>\n",
+ " <td>schuk</td>\n",
+ " <td>quad</td>\n",
+ " <td>jiet</td>\n",
+ " <td>gaud</td>\n",
+ " <td>flap</td>\n",
+ " <td>quop</td>\n",
+ " <td>prieg</td>\n",
+ " <td>mub</td>\n",
+ " <td>...</td>\n",
+ " <td>blot</td>\n",
+ " <td>wieb</td>\n",
+ " <td>drut</td>\n",
+ " <td>frad</td>\n",
+ " <td>gag</td>\n",
+ " <td>gried</td>\n",
+ " <td>naug</td>\n",
+ " <td>schriek</td>\n",
+ " <td>klap</td>\n",
+ " <td>gob</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>voicing</th>\n",
+ " <td>voiced</td>\n",
+ " <td>voiceless</td>\n",
+ " <td>voiceless</td>\n",
+ " <td>voiced</td>\n",
+ " <td>voiceless</td>\n",
+ " <td>voiced</td>\n",
+ " <td>voiceless</td>\n",
+ " <td>voiceless</td>\n",
+ " <td>voiced</td>\n",
+ " <td>voiced</td>\n",
+ " <td>...</td>\n",
+ " <td>voiceless</td>\n",
+ " <td>voiced</td>\n",
+ " <td>voiceless</td>\n",
+ " <td>voiced</td>\n",
+ " <td>voiced</td>\n",
+ " <td>voiced</td>\n",
+ " <td>voiced</td>\n",
+ " <td>voiceless</td>\n",
+ " <td>voiceless</td>\n",
+ " <td>voiced</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>item_pair</th>\n",
+ " <td>24</td>\n",
+ " <td>22</td>\n",
+ " <td>23</td>\n",
+ " <td>20</td>\n",
+ " <td>16</td>\n",
+ " <td>15</td>\n",
+ " <td>13</td>\n",
+ " <td>21</td>\n",
+ " <td>18</td>\n",
+ " <td>17</td>\n",
+ " <td>...</td>\n",
+ " <td>1</td>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>5</td>\n",
+ " <td>7</td>\n",
+ " <td>9</td>\n",
+ " <td>11</td>\n",
+ " <td>8</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>order</th>\n",
+ " <td>1+0i</td>\n",
+ " <td>8+0i</td>\n",
+ " <td>9+0i</td>\n",
+ " <td>10+0i</td>\n",
+ " <td>12+0i</td>\n",
+ " <td>13+0i</td>\n",
+ " <td>19+0i</td>\n",
+ " <td>20+0i</td>\n",
+ " <td>23+0i</td>\n",
+ " <td>25+0i</td>\n",
+ " <td>...</td>\n",
+ " <td>114+0i</td>\n",
+ " <td>122+0i</td>\n",
+ " <td>124+0i</td>\n",
+ " <td>127+0i</td>\n",
+ " <td>129+0i</td>\n",
+ " <td>130+0i</td>\n",
+ " <td>132+0i</td>\n",
+ " <td>135+0i</td>\n",
+ " <td>141+0i</td>\n",
+ " <td>144+0i</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>vowel</th>\n",
+ " <td>au</td>\n",
+ " <td>o</td>\n",
+ " <td>u</td>\n",
+ " <td>a</td>\n",
+ " <td>i</td>\n",
+ " <td>au</td>\n",
+ " <td>a</td>\n",
+ " <td>o</td>\n",
+ " <td>i</td>\n",
+ " <td>u</td>\n",
+ " <td>...</td>\n",
+ " <td>o</td>\n",
+ " <td>i</td>\n",
+ " <td>u</td>\n",
+ " <td>a</td>\n",
+ " <td>a</td>\n",
+ " <td>i</td>\n",
+ " <td>au</td>\n",
+ " <td>i</td>\n",
+ " <td>a</td>\n",
+ " <td>o</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>stop</th>\n",
+ " <td>g</td>\n",
+ " <td>k</td>\n",
+ " <td>k</td>\n",
+ " <td>d</td>\n",
+ " <td>t</td>\n",
+ " <td>d</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>g</td>\n",
+ " <td>b</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>b</td>\n",
+ " <td>t</td>\n",
+ " <td>d</td>\n",
+ " <td>g</td>\n",
+ " <td>d</td>\n",
+ " <td>g</td>\n",
+ " <td>k</td>\n",
+ " <td>p</td>\n",
+ " <td>b</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>place</th>\n",
+ " <td>velar</td>\n",
+ " <td>velar</td>\n",
+ " <td>velar</td>\n",
+ " <td>alveolar</td>\n",
+ " <td>alveolar</td>\n",
+ " <td>alveolar</td>\n",
+ " <td>labial</td>\n",
+ " <td>labial</td>\n",
+ " <td>velar</td>\n",
+ " <td>labial</td>\n",
+ " <td>...</td>\n",
+ " <td>alveolar</td>\n",
+ " <td>labial</td>\n",
+ " <td>alveolar</td>\n",
+ " <td>alveolar</td>\n",
+ " <td>velar</td>\n",
+ " <td>alveolar</td>\n",
+ " <td>velar</td>\n",
+ " <td>velar</td>\n",
+ " <td>labial</td>\n",
+ " <td>labial</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>utterancelength</th>\n",
+ " <td>1.784</td>\n",
+ " <td>1.408</td>\n",
+ " <td>1.448</td>\n",
+ " <td>1.472</td>\n",
+ " <td>1.704</td>\n",
+ " <td>1.528</td>\n",
+ " <td>1.6</td>\n",
+ " <td>1.608</td>\n",
+ " <td>1.512</td>\n",
+ " <td>1.568</td>\n",
+ " <td>...</td>\n",
+ " <td>1.472</td>\n",
+ " <td>1.472</td>\n",
+ " <td>1.512</td>\n",
+ " <td>1.512</td>\n",
+ " <td>1.616</td>\n",
+ " <td>1.456</td>\n",
+ " <td>1.544</td>\n",
+ " <td>1.568</td>\n",
+ " <td>1.728</td>\n",
+ " <td>1.672</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>accent_type</th>\n",
+ " <td>nuclear</td>\n",
+ " <td>nuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>nuclear</td>\n",
+ " <td>nuclear</td>\n",
+ " <td>nuclear</td>\n",
+ " <td>nuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>...</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " <td>prenuclear</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>prosodic_boundary</th>\n",
+ " <td>yes</td>\n",
+ " <td>yes</td>\n",
+ " <td>no</td>\n",
+ " <td>yes</td>\n",
+ " <td>yes</td>\n",
+ " <td>yes</td>\n",
+ " <td>yes</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>...</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>norming_voiceless_count</th>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>norming_schwa_total</th>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>5</td>\n",
+ " <td>2</td>\n",
+ " <td>5</td>\n",
+ " <td>4</td>\n",
+ " <td>...</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>4</td>\n",
+ " <td>5</td>\n",
+ " <td>4</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>norming_schwa_pure</th>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>5</td>\n",
+ " <td>2</td>\n",
+ " <td>5</td>\n",
+ " <td>4</td>\n",
+ " <td>...</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>4</td>\n",
+ " <td>5</td>\n",
+ " <td>4</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
" </tr>\n",
- " </thead>\n",
- " <tbody>\n",
" <tr>\n",
- " <th>low-high</th>\n",
- " <td>-2.76</td>\n",
- " <td>0.555938</td>\n",
- " <td>-4.964586</td>\n",
- " <td>0.000328</td>\n",
- " <td>-3.971284</td>\n",
- " <td>-1.548716</td>\n",
- " <td>0.000984</td>\n",
- " <td>True</td>\n",
+ " <th>usable</th>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>not_usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>not_usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>...</td>\n",
+ " <td>not_usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>usable</td>\n",
+ " <td>not_usable</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>med-high</th>\n",
- " <td>-1.48</td>\n",
- " <td>0.555938</td>\n",
- " <td>-2.662169</td>\n",
- " <td>0.020708</td>\n",
- " <td>-2.691284</td>\n",
- " <td>-0.268716</td>\n",
- " <td>0.040988</td>\n",
- " <td>True</td>\n",
+ " <th>item_vowel_dur</th>\n",
+ " <td>169.76</td>\n",
+ " <td>155.85</td>\n",
+ " <td>113.14</td>\n",
+ " <td>190.37</td>\n",
+ " <td>140.71</td>\n",
+ " <td>213.2</td>\n",
+ " <td>109.48</td>\n",
+ " <td>146.96</td>\n",
+ " <td>142.02</td>\n",
+ " <td>190.24</td>\n",
+ " <td>...</td>\n",
+ " <td>153.82</td>\n",
+ " <td>115.02</td>\n",
+ " <td>111.7</td>\n",
+ " <td>198.19</td>\n",
+ " <td>220.77</td>\n",
+ " <td>116.35</td>\n",
+ " <td>206.35</td>\n",
+ " <td>124.84</td>\n",
+ " <td>152.21</td>\n",
+ " <td>153.2</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>med-low</th>\n",
- " <td>1.28</td>\n",
- " <td>0.555938</td>\n",
- " <td>2.302416</td>\n",
- " <td>0.040022</td>\n",
- " <td>0.068716</td>\n",
- " <td>2.491284</td>\n",
- " <td>0.040988</td>\n",
- " <td>True</td>\n",
+ " <th>vowel_dur</th>\n",
+ " <td>175.348118</td>\n",
+ " <td>142.93876</td>\n",
+ " <td>103.278765</td>\n",
+ " <td>198.189655</td>\n",
+ " <td>134.959224</td>\n",
+ " <td>200.714031</td>\n",
+ " <td>170.130933</td>\n",
+ " <td>146.76317</td>\n",
+ " <td>151.648318</td>\n",
+ " <td>107.40031</td>\n",
+ " <td>...</td>\n",
+ " <td>184.023194</td>\n",
+ " <td>113.494857</td>\n",
+ " <td>110.395575</td>\n",
+ " <td>210.267881</td>\n",
+ " <td>254.574444</td>\n",
+ " <td>136.924026</td>\n",
+ " <td>225.882315</td>\n",
+ " <td>146.512138</td>\n",
+ " <td>228.258654</td>\n",
+ " <td>175.189705</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>comment</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>[kw] nicht nativer kluster</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>VQ e; deaccented?</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>sounds english ou</td>\n",
+ " <td>NO RELEASE</td>\n",
+ " <td>NaN</td>\n",
+ " <td>flapped R</td>\n",
+ " <td>NaN</td>\n",
+ " <td>flapped R</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>sounds english ou</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>21 rows × 750 columns</p>\n",
"</div>"
],
"text/plain": [
- " coef std err t P>|t| Conf. Int. Low Conf. Int. Upp. \\\n",
- "low-high -2.76 0.555938 -4.964586 0.000328 -3.971284 -1.548716 \n",
- "med-high -1.48 0.555938 -2.662169 0.020708 -2.691284 -0.268716 \n",
- "med-low 1.28 0.555938 2.302416 0.040022 0.068716 2.491284 \n",
+ " 0 1 2 \\\n",
+ "subject 15 15 15 \n",
+ "subject_name Alex Alex Alex \n",
+ "gender male male male \n",
+ "item stauge roke schuke \n",
+ "item_singular staug rok schuk \n",
+ "voicing voiced voiceless voiceless \n",
+ "item_pair 24 22 23 \n",
+ "order 1+0i 8+0i 9+0i \n",
+ "vowel au o u \n",
+ "stop g k k \n",
+ "place velar velar velar \n",
+ "utterancelength 1.784 1.408 1.448 \n",
+ "accent_type nuclear nuclear prenuclear \n",
+ "prosodic_boundary yes yes no \n",
+ "norming_voiceless_count 0 1 1 \n",
+ "norming_schwa_total 3 2 3 \n",
+ "norming_schwa_pure 3 2 3 \n",
+ "usable usable usable usable \n",
+ "item_vowel_dur 169.76 155.85 113.14 \n",
+ "vowel_dur 175.348118 142.93876 103.278765 \n",
+ "comment NaN NaN NaN \n",
"\n",
- " pvalue-hs reject-hs \n",
- "low-high 0.000984 True \n",
- "med-high 0.040988 True \n",
- "med-low 0.040988 True "
+ " 3 4 5 \\\n",
+ "subject 15 15 15 \n",
+ "subject_name Alex Alex Alex \n",
+ "gender male male male \n",
+ "item quade jiete gaude \n",
+ "item_singular quad jiet gaud \n",
+ "voicing voiced voiceless voiced \n",
+ "item_pair 20 16 15 \n",
+ "order 10+0i 12+0i 13+0i \n",
+ "vowel a i au \n",
+ "stop d t d \n",
+ "place alveolar alveolar alveolar \n",
+ "utterancelength 1.472 1.704 1.528 \n",
+ "accent_type nuclear nuclear nuclear \n",
+ "prosodic_boundary yes yes yes \n",
+ "norming_voiceless_count 1 2 0 \n",
+ "norming_schwa_total 3 3 3 \n",
+ "norming_schwa_pure 3 3 3 \n",
+ "usable not_usable usable usable \n",
+ "item_vowel_dur 190.37 140.71 213.2 \n",
+ "vowel_dur 198.189655 134.959224 200.714031 \n",
+ "comment [kw] nicht nativer kluster NaN NaN \n",
+ "\n",
+ " 6 7 8 \\\n",
+ "subject 15 15 15 \n",
+ "subject_name Alex Alex Alex \n",
+ "gender male male male \n",
+ "item flape quope priege \n",
+ "item_singular flap quop prieg \n",
+ "voicing voiceless voiceless voiced \n",
+ "item_pair 13 21 18 \n",
+ "order 19+0i 20+0i 23+0i \n",
+ "vowel a o i \n",
+ "stop p p g \n",
+ "place labial labial velar \n",
+ "utterancelength 1.6 1.608 1.512 \n",
+ "accent_type nuclear prenuclear prenuclear \n",
+ "prosodic_boundary yes no no \n",
+ "norming_voiceless_count 0 1 0 \n",
+ "norming_schwa_total 5 2 5 \n",
+ "norming_schwa_pure 5 2 5 \n",
+ "usable usable usable not_usable \n",
+ "item_vowel_dur 109.48 146.96 142.02 \n",
+ "vowel_dur 170.130933 146.76317 151.648318 \n",
+ "comment NaN NaN VQ e; deaccented? \n",
+ "\n",
+ " 9 ... 740 741 \\\n",
+ "subject 15 ... 13 13 \n",
+ "subject_name Alex ... Timo Timo \n",
+ "gender male ... male male \n",
+ "item mube ... blote wiebe \n",
+ "item_singular mub ... blot wieb \n",
+ "voicing voiced ... voiceless voiced \n",
+ "item_pair 17 ... 1 12 \n",
+ "order 25+0i ... 114+0i 122+0i \n",
+ "vowel u ... o i \n",
+ "stop b ... t b \n",
+ "place labial ... alveolar labial \n",
+ "utterancelength 1.568 ... 1.472 1.472 \n",
+ "accent_type prenuclear ... prenuclear prenuclear \n",
+ "prosodic_boundary no ... no no \n",
+ "norming_voiceless_count 1 ... 2 0 \n",
+ "norming_schwa_total 4 ... 2 4 \n",
+ "norming_schwa_pure 4 ... 2 4 \n",
+ "usable usable ... not_usable usable \n",
+ "item_vowel_dur 190.24 ... 153.82 115.02 \n",
+ "vowel_dur 107.40031 ... 184.023194 113.494857 \n",
+ "comment NaN ... sounds english ou NO RELEASE \n",
+ "\n",
+ " 742 743 744 745 \\\n",
+ "subject 13 13 13 13 \n",
+ "subject_name Timo Timo Timo Timo \n",
+ "gender male male male male \n",
+ "item drute frade gage griede \n",
+ "item_singular drut frad gag gried \n",
+ "voicing voiceless voiced voiced voiced \n",
+ "item_pair 2 3 5 7 \n",
+ "order 124+0i 127+0i 129+0i 130+0i \n",
+ "vowel u a a i \n",
+ "stop t d g d \n",
+ "place alveolar alveolar velar alveolar \n",
+ "utterancelength 1.512 1.512 1.616 1.456 \n",
+ "accent_type prenuclear prenuclear prenuclear prenuclear \n",
+ "prosodic_boundary no no no no \n",
+ "norming_voiceless_count 0 1 1 0 \n",
+ "norming_schwa_total 2 3 4 5 \n",
+ "norming_schwa_pure 2 3 4 5 \n",
+ "usable usable usable usable usable \n",
+ "item_vowel_dur 111.7 198.19 220.77 116.35 \n",
+ "vowel_dur 110.395575 210.267881 254.574444 136.924026 \n",
+ "comment NaN flapped R NaN flapped R \n",
+ "\n",
+ " 746 747 748 749 \n",
+ "subject 13 13 13 13 \n",
+ "subject_name Timo Timo Timo Timo \n",
+ "gender male male male male \n",
+ "item nauge schrieke klape gobe \n",
+ "item_singular naug schriek klap gob \n",
+ "voicing voiced voiceless voiceless voiced \n",
+ "item_pair 9 11 8 6 \n",
+ "order 132+0i 135+0i 141+0i 144+0i \n",
+ "vowel au i a o \n",
+ "stop g k p b \n",
+ "place velar velar labial labial \n",
+ "utterancelength 1.544 1.568 1.728 1.672 \n",
+ "accent_type prenuclear prenuclear prenuclear prenuclear \n",
+ "prosodic_boundary no no no no \n",
+ "norming_voiceless_count 0 1 1 1 \n",
+ "norming_schwa_total 4 3 2 3 \n",
+ "norming_schwa_pure 4 3 2 3 \n",
+ "usable usable usable usable not_usable \n",
+ "item_vowel_dur 206.35 124.84 152.21 153.2 \n",
+ "vowel_dur 225.882315 146.512138 228.258654 175.189705 \n",
+ "comment NaN NaN NaN sounds english ou \n",
+ "\n",
+ "[21 rows x 750 columns]"
]
},
- "execution_count": 46,
+ "execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "weekly_water_posthoc = weekly_water_model.t_test_pairwise('sun')\n",
- "weekly_water_posthoc.result_frame"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "98126f2f",
- "metadata": {
- "hidden": true
- },
- "source": [
- "Problem: with `t_test_pairwise`, the correction operates per factor. We already performed a total of 6 comparisons, therefore we should correct considering this number. We may even perform more comparisons..."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "62d4382a",
- "metadata": {
- "heading_collapsed": true,
- "hidden": true
- },
- "source": [
- "### The multiple comparisons problem"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "id": "7f395588",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": [
- "power = 0.8\n",
- "type1_error_rate = 0.05"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "167951da",
- "metadata": {
- "hidden": true
- },
- "source": [
- "Red pixels represent the tests (comparisons) for which $H_0$ is false (right figure) or rejected (left figure):"
+ "rt_data = pd.read_csv('https://osf.io/download/asq8n/')\n",
+ "rt_data.T"
]
},
{
"cell_type": "code",
- "execution_count": 48,
- "id": "8642f0e6",
- "metadata": {
- "hidden": true,
- "tags": []
- },
+ "execution_count": 54,
+ "id": "399ab879-04f4-440d-85c3-c3e6d773b33a",
+ "metadata": {},
"outputs": [
{
"data": {
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+ "text/html": [
+ "<table class=\"simpletable\">\n",
+ "<tr>\n",
+ " <td>Model:</td> <td>MixedLM</td> <td>Dependent Variable:</td> <td>utterancelength</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>No. Observations:</td> <td>750</td> <td>Method:</td> <td>REML</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>No. Groups:</td> <td>16</td> <td>Scale:</td> <td>0.0141</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>Min. group size:</td> <td>44</td> <td>Log-Likelihood:</td> <td>482.5110</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>Max. group size:</td> <td>48</td> <td>Converged:</td> <td>Yes</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>Mean group size:</td> <td>46.9</td> <td></td> <td></td> \n",
+ "</tr>\n",
+ "</table>\n",
+ "<table class=\"simpletable\">\n",
+ "<tr>\n",
+ " <td></td> <th>Coef.</th> <th>Std.Err.</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Intercept</th> <td>1.576</td> <td>0.061</td> <td>25.905</td> <td>0.000</td> <td>1.457</td> <td>1.695</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>place[T.labial]</th> <td>0.005</td> <td>0.015</td> <td>0.363</td> <td>0.716</td> <td>-0.023</td> <td>0.034</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>place[T.velar]</th> <td>0.029</td> <td>0.014</td> <td>1.996</td> <td>0.046</td> <td>0.001</td> <td>0.057</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>gender[T.male]</th> <td>-0.072</td> <td>0.092</td> <td>-0.783</td> <td>0.434</td> <td>-0.252</td> <td>0.108</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>place[T.labial]:gender[T.male]</th> <td>-0.006</td> <td>0.022</td> <td>-0.292</td> <td>0.770</td> <td>-0.050</td> <td>0.037</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>place[T.velar]:gender[T.male]</th> <td>-0.016</td> <td>0.022</td> <td>-0.735</td> <td>0.463</td> <td>-0.058</td> <td>0.026</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>subject Var</th> <td>0.032</td> <td>0.105</td> <td></td> <td></td> <td></td> <td></td> \n",
+ "</tr>\n",
+ "</table><br/>\n"
+ ],
+ "text/latex": [
+ "\\begin{table}\n",
+ "\\caption{Mixed Linear Model Regression Results}\n",
+ "\\label{}\n",
+ "\\begin{center}\n",
+ "\\begin{tabular}{llll}\n",
+ "\\hline\n",
+ "Model: & MixedLM & Dependent Variable: & utterancelength \\\\\n",
+ "No. Observations: & 750 & Method: & REML \\\\\n",
+ "No. Groups: & 16 & Scale: & 0.0141 \\\\\n",
+ "Min. group size: & 44 & Log-Likelihood: & 482.5110 \\\\\n",
+ "Max. group size: & 48 & Converged: & Yes \\\\\n",
+ "Mean group size: & 46.9 & & \\\\\n",
+ "\\hline\n",
+ "\\end{tabular}\n",
+ "\\end{center}\n",
+ "\n",
+ "\\begin{center}\n",
+ "\\begin{tabular}{lrrrrrr}\n",
+ "\\hline\n",
+ " & Coef. & Std.Err. & z & P$> |$z$|$ & [0.025 & 0.975] \\\\\n",
+ "\\hline\n",
+ "Intercept & 1.576 & 0.061 & 25.905 & 0.000 & 1.457 & 1.695 \\\\\n",
+ "place[T.labial] & 0.005 & 0.015 & 0.363 & 0.716 & -0.023 & 0.034 \\\\\n",
+ "place[T.velar] & 0.029 & 0.014 & 1.996 & 0.046 & 0.001 & 0.057 \\\\\n",
+ "gender[T.male] & -0.072 & 0.092 & -0.783 & 0.434 & -0.252 & 0.108 \\\\\n",
+ "place[T.labial]:gender[T.male] & -0.006 & 0.022 & -0.292 & 0.770 & -0.050 & 0.037 \\\\\n",
+ "place[T.velar]:gender[T.male] & -0.016 & 0.022 & -0.735 & 0.463 & -0.058 & 0.026 \\\\\n",
+ "subject Var & 0.032 & 0.105 & & & & \\\\\n",
+ "\\hline\n",
+ "\\end{tabular}\n",
+ "\\end{center}\n",
+ "\\end{table}\n",
+ "\\bigskip\n"
+ ],
"text/plain": [
- "<Figure size 1330x410 with 2 Axes>"
+ "<class 'statsmodels.iolib.summary2.Summary'>\n",
+ "\"\"\"\n",
+ " Mixed Linear Model Regression Results\n",
+ "=========================================================================\n",
+ "Model: MixedLM Dependent Variable: utterancelength\n",
+ "No. Observations: 750 Method: REML \n",
+ "No. Groups: 16 Scale: 0.0141 \n",
+ "Min. group size: 44 Log-Likelihood: 482.5110 \n",
+ "Max. group size: 48 Converged: Yes \n",
+ "Mean group size: 46.9 \n",
+ "-------------------------------------------------------------------------\n",
+ " Coef. Std.Err. z P>|z| [0.025 0.975]\n",
+ "-------------------------------------------------------------------------\n",
+ "Intercept 1.576 0.061 25.905 0.000 1.457 1.695\n",
+ "place[T.labial] 0.005 0.015 0.363 0.716 -0.023 0.034\n",
+ "place[T.velar] 0.029 0.014 1.996 0.046 0.001 0.057\n",
+ "gender[T.male] -0.072 0.092 -0.783 0.434 -0.252 0.108\n",
+ "place[T.labial]:gender[T.male] -0.006 0.022 -0.292 0.770 -0.050 0.037\n",
+ "place[T.velar]:gender[T.male] -0.016 0.022 -0.735 0.463 -0.058 0.026\n",
+ "subject Var 0.032 0.105 \n",
+ "=========================================================================\n",
+ "\n",
+ "\"\"\""
]
},
+ "execution_count": 54,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
- "true_grid = np.zeros((20, 60), dtype=bool)\n",
- "true_grid[:10,-10:] = True\n",
- "\n",
- "rejection_grid = np.array([[ np.random.rand() <= type1_error_rate for _ in range(60) ] for _ in range(20)])\n",
- "rejection_grid[:10,-10:] = [[ np.random.rand() <= power for _ in range(10)] for _ in range(10)]\n",
- "\n",
- "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
- "for ax, title, grid in zip(axes[::-1], ('true', 'observed (actual test results)'), (true_grid, rejection_grid)):\n",
- " ax.imshow(grid, cmap='seismic')\n",
- " ax.set_title(title)\n",
- " ax.axis(\"off\");"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6bdba34b",
- "metadata": {
- "hidden": true
- },
- "source": [
- "The top right group of tests with false $H_0$ is affected by type-2 errors, or equivalently by the power of the test (`power = 1 - type2_error_rate`).\n",
- "\n",
- "The original $5%$ significance level of the test translates into $5%$ type-1 errors that become visible when the test is applied many times as we did above.\n",
- "\n",
- "We want the significance level to apply to the \"whole picture\", and to control the *family-wise error rate*. Basically, we want our $5%$ level to upper-bound the risk of erroneously rejecting any single $H_0$ (or more).\n",
- "\n",
- "This can be done with a procedure called *correction for multiple comparisons*."
+ "model = smf.mixedlm('utterancelength ~ place * gender', rt_data, groups='subject').fit()\n",
+ "model.summary()"
]
},
{
"cell_type": "code",
- "execution_count": 49,
- "id": "e6c92946",
- "metadata": {
- "hidden": true
- },
+ "execution_count": 55,
+ "id": "efbc0d33-5d7f-4942-817f-47faf5c5ca50",
+ "metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/flaurent/Boxes/jammy-1/Projects/scientific_python/lib/python3.10/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
+ " warnings.warn(msg, ConvergenceWarning)\n"
+ ]
+ },
{
"data": {
"text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
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- " .dataframe tbody tr th {\n",
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- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sum_sq</th>\n",
- " <th>df</th>\n",
- " <th>F</th>\n",
- " <th>PR(>F)</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>Intercept</th>\n",
- " <td>246.402000</td>\n",
- " <td>1.0</td>\n",
- " <td>302.891211</td>\n",
- " <td>4.094979e-15</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>water</th>\n",
- " <td>2.401000</td>\n",
- " <td>1.0</td>\n",
- " <td>2.951444</td>\n",
- " <td>9.867817e-02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>sun</th>\n",
- " <td>8.041333</td>\n",
- " <td>2.0</td>\n",
- " <td>4.942430</td>\n",
- " <td>1.593920e-02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>water:sun</th>\n",
- " <td>5.694000</td>\n",
- " <td>2.0</td>\n",
- " <td>3.499693</td>\n",
- " <td>4.637649e-02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Residual</th>\n",
- " <td>19.524000</td>\n",
- " <td>24.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " </tbody>\n",
+ "<table class=\"simpletable\">\n",
+ "<tr>\n",
+ " <td>Model:</td> <td>MixedLM</td> <td>Dependent Variable:</td> <td>utterancelength</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>No. Observations:</td> <td>750</td> <td>Method:</td> <td>REML</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>No. Groups:</td> <td>16</td> <td>Scale:</td> <td>0.0105</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>Min. group size:</td> <td>44</td> <td>Log-Likelihood:</td> <td>482.5110</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>Max. group size:</td> <td>48</td> <td>Converged:</td> <td>Yes</td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <td>Mean group size:</td> <td>46.9</td> <td></td> <td></td> \n",
+ "</tr>\n",
"</table>\n",
- "</div>"
+ "<table class=\"simpletable\">\n",
+ "<tr>\n",
+ " <td></td> <th>Coef.</th> <th>Std.Err.</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>Intercept</th> <td>1.576</td> <td>0.061</td> <td>25.902</td> <td>0.000</td> <td>1.457</td> <td>1.695</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>place[T.labial]</th> <td>0.005</td> <td>0.015</td> <td>0.363</td> <td>0.716</td> <td>-0.023</td> <td>0.034</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>place[T.velar]</th> <td>0.029</td> <td>0.014</td> <td>1.996</td> <td>0.046</td> <td>0.001</td> <td>0.057</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>gender[T.male]</th> <td>-0.072</td> <td>0.092</td> <td>-0.783</td> <td>0.434</td> <td>-0.252</td> <td>0.108</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>place[T.labial]:gender[T.male]</th> <td>-0.006</td> <td>0.022</td> <td>-0.292</td> <td>0.770</td> <td>-0.050</td> <td>0.037</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>place[T.velar]:gender[T.male]</th> <td>-0.016</td> <td>0.022</td> <td>-0.735</td> <td>0.463</td> <td>-0.058</td> <td>0.026</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>subject Var</th> <td>0.032</td> <td></td> <td></td> <td></td> <td></td> <td></td> \n",
+ "</tr>\n",
+ "<tr>\n",
+ " <th>item Var</th> <td>0.004</td> <td></td> <td></td> <td></td> <td></td> <td></td> \n",
+ "</tr>\n",
+ "</table><br/>\n"
+ ],
+ "text/latex": [
+ "\\begin{table}\n",
+ "\\caption{Mixed Linear Model Regression Results}\n",
+ "\\label{}\n",
+ "\\begin{center}\n",
+ "\\begin{tabular}{llll}\n",
+ "\\hline\n",
+ "Model: & MixedLM & Dependent Variable: & utterancelength \\\\\n",
+ "No. Observations: & 750 & Method: & REML \\\\\n",
+ "No. Groups: & 16 & Scale: & 0.0105 \\\\\n",
+ "Min. group size: & 44 & Log-Likelihood: & 482.5110 \\\\\n",
+ "Max. group size: & 48 & Converged: & Yes \\\\\n",
+ "Mean group size: & 46.9 & & \\\\\n",
+ "\\hline\n",
+ "\\end{tabular}\n",
+ "\\end{center}\n",
+ "\n",
+ "\\begin{center}\n",
+ "\\begin{tabular}{lrrrrrr}\n",
+ "\\hline\n",
+ " & Coef. & Std.Err. & z & P$> |$z$|$ & [0.025 & 0.975] \\\\\n",
+ "\\hline\n",
+ "Intercept & 1.576 & 0.061 & 25.902 & 0.000 & 1.457 & 1.695 \\\\\n",
+ "place[T.labial] & 0.005 & 0.015 & 0.363 & 0.716 & -0.023 & 0.034 \\\\\n",
+ "place[T.velar] & 0.029 & 0.014 & 1.996 & 0.046 & 0.001 & 0.057 \\\\\n",
+ "gender[T.male] & -0.072 & 0.092 & -0.783 & 0.434 & -0.252 & 0.108 \\\\\n",
+ "place[T.labial]:gender[T.male] & -0.006 & 0.022 & -0.292 & 0.770 & -0.050 & 0.037 \\\\\n",
+ "place[T.velar]:gender[T.male] & -0.016 & 0.022 & -0.735 & 0.463 & -0.058 & 0.026 \\\\\n",
+ "subject Var & 0.032 & & & & & \\\\\n",
+ "item Var & 0.004 & & & & & \\\\\n",
+ "\\hline\n",
+ "\\end{tabular}\n",
+ "\\end{center}\n",
+ "\\end{table}\n",
+ "\\bigskip\n"
],
"text/plain": [
- " sum_sq df F PR(>F)\n",
- "Intercept 246.402000 1.0 302.891211 4.094979e-15\n",
- "water 2.401000 1.0 2.951444 9.867817e-02\n",
- "sun 8.041333 2.0 4.942430 1.593920e-02\n",
- "water:sun 5.694000 2.0 3.499693 4.637649e-02\n",
- "Residual 19.524000 24.0 NaN NaN"
+ "<class 'statsmodels.iolib.summary2.Summary'>\n",
+ "\"\"\"\n",
+ " Mixed Linear Model Regression Results\n",
+ "=========================================================================\n",
+ "Model: MixedLM Dependent Variable: utterancelength\n",
+ "No. Observations: 750 Method: REML \n",
+ "No. Groups: 16 Scale: 0.0105 \n",
+ "Min. group size: 44 Log-Likelihood: 482.5110 \n",
+ "Max. group size: 48 Converged: Yes \n",
+ "Mean group size: 46.9 \n",
+ "-------------------------------------------------------------------------\n",
+ " Coef. Std.Err. z P>|z| [0.025 0.975]\n",
+ "-------------------------------------------------------------------------\n",
+ "Intercept 1.576 0.061 25.902 0.000 1.457 1.695\n",
+ "place[T.labial] 0.005 0.015 0.363 0.716 -0.023 0.034\n",
+ "place[T.velar] 0.029 0.014 1.996 0.046 0.001 0.057\n",
+ "gender[T.male] -0.072 0.092 -0.783 0.434 -0.252 0.108\n",
+ "place[T.labial]:gender[T.male] -0.006 0.022 -0.292 0.770 -0.050 0.037\n",
+ "place[T.velar]:gender[T.male] -0.016 0.022 -0.735 0.463 -0.058 0.026\n",
+ "subject Var 0.032 \n",
+ "item Var 0.004 \n",
+ "=========================================================================\n",
+ "\n",
+ "\"\"\""
]
},
- "execution_count": 49,
+ "execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "sm.stats.anova_lm(model_with_interaction, typ=3)"
+ "model = smf.mixedlm('utterancelength ~ place * gender', rt_data, groups='subject', vc_formula={'item': '0 + item'}, re_formula='1').fit()\n",
+ "model.summary()"
]
},
{
- "cell_type": "markdown",
- "id": "48d0f081",
- "metadata": {
- "heading_collapsed": true,
- "hidden": true
- },
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "31ea99ae-96a7-45cd-98fe-1ad67b131978",
+ "metadata": {},
+ "outputs": [],
"source": [
- "### multipletests"
+ "#sm.stats.anova_lm(model) # does not work!"
]
},
{
"cell_type": "markdown",
- "id": "b7ea24c3",
- "metadata": {
- "hidden": true
- },
+ "id": "d87ffec5-0127-45f2-8d73-75819c1cd3ff",
+ "metadata": {},
"source": [
- "If we consider all 5 factored models, we may proceed to performing up to 9 comparisons, but again `t_test_pairwise` would not properly take this into account.\n",
- "\n",
- "Note that you do not need to perform all possible comparisons. Choose what comparisons you are interested in, but do so prior to performing them.\n",
+ "Instead of the traditional sums-of-squares and ANOVA, we test for main effects by comparing models using the Wald test.\n",
"\n",
- "We should use [multipletests](https://www.statsmodels.org/stable/generated/statsmodels.stats.multitest.multipletests.html) instead, for the purpose of correcting the $p$-values:"
+ "We compare the full model with a model without the effect of interest. This second model is specified as a constraint on one or more model coefficients. For example:"
]
},
{
"cell_type": "code",
- "execution_count": 50,
- "id": "1b9e153d",
- "metadata": {
- "hidden": true
- },
+ "execution_count": 57,
+ "id": "4fac0c5c-93d6-4221-9be4-c601583ba295",
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "7"
+ "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
+ "<Wald test (chi2): statistic=0.6127919202597879, p-value=0.4337385352487245, df_denom=1>"
]
},
- "execution_count": 50,
+ "execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "from statsmodels.stats.multitest import multipletests\n",
- "\n",
- "significance_level = 0.05\n",
+ "model.wald_test('gender[T.male] = 0', scalar=True, use_f=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81507156-c9c7-4d20-b48d-5f1669ec21b4",
+ "metadata": {},
+ "source": [
+ "In the above example, we have established that the `gender` factor has no effects (`p-value>0.05`).\n",
"\n",
- "all_comparisons = []\n",
- "for factor1, factor2 in (('sun', 'water'), ('water', 'sun')):\n",
- " for f2_level in np.unique(plant_data[factor2]):\n",
- " model = ols(f'height ~ {factor1}', data=plant_data[plant_data[factor2]==f2_level]).fit()\n",
- " if model.f_pvalue <= significance_level:\n",
- " model_name = f'{factor2}={f2_level}'\n",
- " pairwise_tests = model.t_test_pairwise(factor1).result_frame\n",
- " pairwise_tests.index = [ f'{comparison}[{model_name}]' for comparison in pairwise_tests.index ]\n",
- " all_comparisons.append(pairwise_tests)\n",
- "all_comparisons = pd.concat(all_comparisons)\n",
+ "The $p$-values reported by `summary` on coefficients associated with binary factors readily inform about the factor's effect.\n",
"\n",
- "len(all_comparisons)"
+ "Multi-level main effects can be tested with null hypothesis: *all related coefficients equal to $0$*."
]
},
{
"cell_type": "code",
- "execution_count": 51,
- "id": "2fe99f26",
- "metadata": {
- "hidden": true
- },
+ "execution_count": 58,
+ "id": "b122d238-a7a3-4203-86c5-db96eb229fb4",
+ "metadata": {},
"outputs": [
{
"data": {
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>coef</th>\n",
- " <th>std err</th>\n",
- " <th>t</th>\n",
- " <th>P>|t|</th>\n",
- " <th>Conf. Int. Low</th>\n",
- " <th>Conf. Int. Upp.</th>\n",
- " <th>pvalue-hs</th>\n",
- " <th>reject-hs</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>low-high[water=daily]</th>\n",
- " <td>-1.08</td>\n",
- " <td>0.584580</td>\n",
- " <td>-1.847481</td>\n",
- " <td>0.089466</td>\n",
- " <td>-2.353690</td>\n",
- " <td>0.193690</td>\n",
- " <td>0.170928</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>med-high[water=daily]</th>\n",
- " <td>-1.78</td>\n",
- " <td>0.584580</td>\n",
- " <td>-3.044923</td>\n",
- " <td>0.010180</td>\n",
- " <td>-3.053690</td>\n",
- " <td>-0.506310</td>\n",
- " <td>0.049876</td>\n",
- " <td>True</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>med-low[water=daily]</th>\n",
- " <td>-0.70</td>\n",
- " <td>0.584580</td>\n",
- " <td>-1.197442</td>\n",
- " <td>0.254253</td>\n",
- " <td>-1.973690</td>\n",
- " <td>0.573690</td>\n",
- " <td>0.254253</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>low-high[water=weekly]</th>\n",
- " <td>-2.76</td>\n",
- " <td>0.555938</td>\n",
- " <td>-4.964586</td>\n",
- " <td>0.000328</td>\n",
- " <td>-3.971284</td>\n",
- " <td>-1.548716</td>\n",
- " <td>0.002279</td>\n",
- " <td>True</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>med-high[water=weekly]</th>\n",
- " <td>-1.48</td>\n",
- " <td>0.555938</td>\n",
- " <td>-2.662169</td>\n",
- " <td>0.020708</td>\n",
- " <td>-2.691284</td>\n",
- " <td>-0.268716</td>\n",
- " <td>0.080295</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>med-low[water=weekly]</th>\n",
- " <td>1.28</td>\n",
- " <td>0.555938</td>\n",
- " <td>2.302416</td>\n",
- " <td>0.040022</td>\n",
- " <td>0.068716</td>\n",
- " <td>2.491284</td>\n",
- " <td>0.115325</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>weekly-daily[sun=low]</th>\n",
- " <td>-2.66</td>\n",
- " <td>0.443847</td>\n",
- " <td>-5.993059</td>\n",
- " <td>0.000326</td>\n",
- " <td>-3.683513</td>\n",
- " <td>-1.636487</td>\n",
- " <td>0.002279</td>\n",
- " <td>True</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
"text/plain": [
- " coef std err t P>|t| Conf. Int. Low \\\n",
- "low-high[water=daily] -1.08 0.584580 -1.847481 0.089466 -2.353690 \n",
- "med-high[water=daily] -1.78 0.584580 -3.044923 0.010180 -3.053690 \n",
- "med-low[water=daily] -0.70 0.584580 -1.197442 0.254253 -1.973690 \n",
- "low-high[water=weekly] -2.76 0.555938 -4.964586 0.000328 -3.971284 \n",
- "med-high[water=weekly] -1.48 0.555938 -2.662169 0.020708 -2.691284 \n",
- "med-low[water=weekly] 1.28 0.555938 2.302416 0.040022 0.068716 \n",
- "weekly-daily[sun=low] -2.66 0.443847 -5.993059 0.000326 -3.683513 \n",
- "\n",
- " Conf. Int. Upp. pvalue-hs reject-hs \n",
- "low-high[water=daily] 0.193690 0.170928 False \n",
- "med-high[water=daily] -0.506310 0.049876 True \n",
- "med-low[water=daily] 0.573690 0.254253 False \n",
- "low-high[water=weekly] -1.548716 0.002279 True \n",
- "med-high[water=weekly] -0.268716 0.080295 False \n",
- "med-low[water=weekly] 2.491284 0.115325 False \n",
- "weekly-daily[sun=low] -1.636487 0.002279 True "
+ "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
+ "<Wald test (chi2): statistic=4.750900500325523, p-value=0.09297261884853324, df_denom=2>"
]
},
- "execution_count": 51,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "all_comparisons['reject-hs'], all_comparisons['pvalue-hs'], _, _ = multipletests(all_comparisons['P>|t|'], alpha=significance_level)\n",
- "all_comparisons"
+ "model.wald_test('place[T.velar] = place[T.labial] = 0', scalar=True, use_f=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "258bc481-0296-4493-8e6e-ebd17c8886e7",
+ "metadata": {},
+ "source": [
+ "If a factor or interaction term does not exhibit an effect, it can be removed from the model. This is often done for interaction terms:"
]
},
{
"cell_type": "code",
- "execution_count": 52,
- "id": "25b21f28-f70d-466e-bbb6-8a6fc86b3696",
- "metadata": {
- "hidden": true,
- "tags": []
- },
+ "execution_count": 59,
+ "id": "4afad7ba-e03b-47cf-8d60-4f2cd9dacfe6",
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
"text/plain": [
- "<Figure size 640x480 with 1 Axes>"
+ "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
+ "<Wald test (chi2): statistic=0.557156630905794, p-value=0.7568589916741078, df_denom=2>"
]
},
+ "execution_count": 59,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
- "y = 0\n",
- "post_hoc_tests = all_comparisons[['coef', 'Conf. Int. Low', 'Conf. Int. Upp.', 'reject-hs']]\n",
- "for y, contrast in enumerate(post_hoc_tests.index):\n",
- " mean, lower_bound, upper_bound, reject = post_hoc_tests.loc[contrast]\n",
- " plt.errorbar(mean, -y, lolims=True, xerr=[[mean-lower_bound], [upper_bound-mean]], yerr=0, linestyle='', c='red' if reject else 'black')\n",
- " plt.text(mean, -y, contrast, ha='center', va='top')\n",
- "plt.axvline(0, color='darkorange')\n",
- "plt.yticks([]);"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "748acdc4",
- "metadata": {
- "hidden": true
- },
- "source": [
- "Beware: the confidence intervals from [t_test_pairwise](https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.OLSResults.t_test_pairwise.html)'s table are not corrected for multiple comparisons."
+ "model.wald_test('place[T.labial]:gender[T.male] = place[T.velar]:gender[T.male] = 0', scalar=True, use_f=False)"
]
},
{
"cell_type": "markdown",
- "id": "f436837b",
- "metadata": {
- "heading_collapsed": true,
- "hidden": true
- },
+ "id": "33b6b036-9062-4b27-8737-0f7e3d86ad9d",
+ "metadata": {},
"source": [
- "### Bonferroni, Šidák and Holm"
+ "Instead of posthoc tests, one can test individual pairwise differences:"
]
},
{
- "cell_type": "markdown",
- "id": "aecb5d85-20d7-4261-a572-b6c891cb8aff",
- "metadata": {
- "hidden": true
- },
+ "cell_type": "code",
+ "execution_count": 60,
+ "id": "af9058d6-6110-49e3-a3af-8af9d4c7cdcd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
+ "<Wald test (chi2): statistic=2.850158520213407, p-value=0.09136492767813915, df_denom=1>"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "[multipletests](https://www.statsmodels.org/stable/generated/statsmodels.stats.multitest.multipletests.html) implements several correction procedures. The Holm correction with Šidák adjustments is the default method (`holm-sidak`).\n",
- "\n",
- "If we perform $n$ tests, the $p$-value for each test can be adjusted as follows:\n",
- "\n",
- "* Bonferroni adjustement : $p_{corrected} = np$\n",
- "* Šidák adjustment : $p_{corrected} = 1 - ( 1 - p )^n$\n",
- "\n",
- "In the Holm's procedure, we sequentially consider each $p$-value, starting from the smallest one, and adjust them on basis of the number of remaining $p$-values to adjust.\n",
- "Basically:\n",
- "\n",
- "1. the smallest $p$-value is adjusted considering $n$ multiple comparisons (because we have not adjusted any $p$-value yet),\n",
- "2. the second smallest $p$-value is adjusted considering $n-1$ multiple comparisons (because we have already adjusted one $p$-value),\n",
- "3. and so on.\n",
- "\n",
- "`statsmodels` also implements the Turkey \"Honest Significant Differences\" post-hoc test as [MultiComparison.turkeyhsd](https://www.statsmodels.org/stable/generated/statsmodels.sandbox.stats.multicomp.MultiComparison.html)."
+ "model.wald_test('place[T.velar] = place[T.labial]', scalar=True, use_f=False)"
]
},
{
@@ -3026,7 +4130,6 @@
"id": "f8cd7dd0-9c93-427f-b254-8098bd95e3c3",
"metadata": {
"heading_collapsed": true,
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@@ -3057,7 +4160,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 61,
"id": "b350dec1",
"metadata": {
"hidden": true
@@ -3258,7 +4361,7 @@
"[5 rows x 31 columns]"
]
},
- "execution_count": 53,
+ "execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
@@ -3270,7 +4373,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 62,
"id": "67e86aec",
"metadata": {
"hidden": true
@@ -3278,7 +4381,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -3293,7 +4396,7 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 63,
"id": "8bd68586",
"metadata": {
"hidden": true
@@ -3303,18 +4406,6 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " OLS Regression Results \n",
- "==============================================================================\n",
- "Dep. Variable: Response R-squared: 0.642\n",
- "Model: OLS Adj. R-squared: 0.640\n",
- "Method: Least Squares F-statistic: 354.9\n",
- "Date: Mon, 26 Sep 2022 Prob (F-statistic): 4.97e-46\n",
- "Time: 01:44:36 Log-Likelihood: -103.52\n",
- "No. Observations: 200 AIC: 211.0\n",
- "Df Residuals: 198 BIC: 217.6\n",
- "Df Model: 1 \n",
- "Covariance Type: nonrobust \n",
- "==============================================================================\n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
@@ -3326,7 +4417,7 @@
],
"source": [
"model = smf.ols('Response ~ CHUK', patients).fit()\n",
- "print(model.summary().tables[0])\n",
+ "#print(model.summary().tables[0])\n",
"print(model.summary().tables[1])"
]
},
@@ -3340,20 +4431,9 @@
"<p style=\"font-size: x-small;\">Data set and choice of an explanatory variable inspired by the RS3 session about linear models on <a href=\"https://moodle01.hosting.pasteur.fr\">Institut Pasteur's Moodle</a></p>"
]
},
- {
- "cell_type": "markdown",
- "id": "0c7020dd",
- "metadata": {
- "heading_collapsed": true,
- "hidden": true
- },
- "source": [
- "### Regression plot"
- ]
- },
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 64,
"id": "50155198",
"metadata": {
"hidden": true
@@ -3361,7 +4441,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -3371,21 +4451,12 @@
}
],
"source": [
- "ax = sns.scatterplot(data=patients, x='CHUK', y='Response', label='Patient')\n",
- "sm.graphics.abline_plot(model_results=model, ax=ax, label='Model prediction')\n",
- "plt.legend()\n",
- "\n",
- "example_patient = 173#np.argmax(model.resid)\n",
- "x = patients.loc[example_patient, 'CHUK']\n",
- "expected_value = patients.loc[example_patient, 'Response']\n",
- "predicted_value = model.fittedvalues[example_patient]\n",
- "ax.plot([x, x], [predicted_value, expected_value], 'r-', zorder=0)\n",
- "ax.plot(patients['CHUK'], model.fittedvalues, 'g+');"
+ "statsmodels_material.illustration_regression(patients, model)"
]
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 65,
"id": "215fb86c-3a14-45b7-ad65-9cfeafc410d4",
"metadata": {
"hidden": true
@@ -3399,7 +4470,7 @@
"dtype: float64"
]
},
- "execution_count": 57,
+ "execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
@@ -3432,7 +4503,7 @@
"hidden": true
},
"source": [
- "### Residual plot"
+ "### Residual plots"
]
},
{
@@ -3448,7 +4519,39 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 66,
+ "id": "95319ec8-320b-4fbb-a62e-a3665b40b043",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 0.012319\n",
+ "1 -0.041911\n",
+ "2 -0.390476\n",
+ "3 1.557927\n",
+ "4 0.379972\n",
+ " ... \n",
+ "195 -0.342436\n",
+ "196 -0.236740\n",
+ "197 0.096321\n",
+ "198 -0.123735\n",
+ "199 0.584860\n",
+ "Length: 200, dtype: float64"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.resid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
"id": "bc471543",
"metadata": {
"hidden": true
@@ -3456,7 +4559,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -3466,13 +4569,7 @@
}
],
"source": [
- "ax = sns.scatterplot(x='CHUK', y='residuals', label='Patient',\n",
- " data={'CHUK': patients['CHUK'], 'residuals': model.resid})\n",
- "ax.axhline(0, linestyle=':', lw=1, label='Model prediction')\n",
- "plt.legend()\n",
- "\n",
- "example_patient_residual = model.resid[example_patient]\n",
- "ax.plot([x, x], [0, example_patient_residual], 'r-', zorder=0);"
+ "statsmodels_material.illustration_regression_residuals(patients, model)"
]
},
{
@@ -3511,20 +4608,9 @@
"Criterion: the residuals should be normally distributed."
]
},
- {
- "cell_type": "markdown",
- "id": "3ad9d670",
- "metadata": {
- "heading_collapsed": true,
- "hidden": true
- },
- "source": [
- "### QQ plot"
- ]
- },
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 68,
"id": "0d7780a9",
"metadata": {
"hidden": true
@@ -3532,7 +4618,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -3552,23 +4638,15 @@
"hidden": true
},
"source": [
- "Note: `statsmodels`'s `qqplot` compares with R's qqplot as long as `fit=True` to standardize the residuals.\n",
- "`fit=True` makes `qqplot` differs from `scipy`'s `probplot`."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "58d0ea09",
- "metadata": {
- "hidden": true
- },
- "source": [
- "Complete OLS output:"
+ "Note: statsmodels's `qqplot` compares with R's qqplot as long as `fit=True` to standardize the residuals.\n",
+ "`fit=True` makes `qqplot` differs from scipy's `probplot`.\n",
+ "\n",
+ "In what refers to normality of the residuals, the last summary table is also informative."
]
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 69,
"id": "5492f4e8-2ac8-4ba7-9a6c-241f33994998",
"metadata": {
"hidden": true
@@ -3578,36 +4656,17 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " OLS Regression Results \n",
- "==============================================================================\n",
- "Dep. Variable: Response R-squared: 0.642\n",
- "Model: OLS Adj. R-squared: 0.640\n",
- "Method: Least Squares F-statistic: 354.9\n",
- "Date: Mon, 26 Sep 2022 Prob (F-statistic): 4.97e-46\n",
- "Time: 01:44:37 Log-Likelihood: -103.52\n",
- "No. Observations: 200 AIC: 211.0\n",
- "Df Residuals: 198 BIC: 217.6\n",
- "Df Model: 1 \n",
- "Covariance Type: nonrobust \n",
- "==============================================================================\n",
- " coef std err t P>|t| [0.025 0.975]\n",
- "------------------------------------------------------------------------------\n",
- "Intercept -11.2792 0.599 -18.838 0.000 -12.460 -10.098\n",
- "CHUK 0.9727 0.052 18.839 0.000 0.871 1.075\n",
"==============================================================================\n",
"Omnibus: 11.549 Durbin-Watson: 2.042\n",
"Prob(Omnibus): 0.003 Jarque-Bera (JB): 11.949\n",
"Skew: 0.586 Prob(JB): 0.00254\n",
"Kurtosis: 3.245 Cond. No. 242.\n",
- "==============================================================================\n",
- "\n",
- "Notes:\n",
- "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+ "==============================================================================\n"
]
}
],
"source": [
- "print(model.summary())"
+ "print(model.summary().tables[-1])"
]
},
{
@@ -3638,12 +4697,12 @@
"hidden": true
},
"source": [
- "To identify outliers and influential points, `statsmodels` features more [diagnostic measures and plots](https://www.statsmodels.org/stable/generated/statsmodels.stats.outliers_influence.OLSInfluence.html):"
+ "To identify outliers and influential points, statsmodels features more [diagnostic measures and plots](https://www.statsmodels.org/stable/generated/statsmodels.stats.outliers_influence.OLSInfluence.html):"
]
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 70,
"id": "e79b930e",
"metadata": {
"hidden": true
@@ -3656,7 +4715,7 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 71,
"id": "3cab691b",
"metadata": {
"hidden": true
@@ -3850,7 +4909,7 @@
"[200 rows x 8 columns]"
]
},
- "execution_count": 62,
+ "execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
@@ -3861,7 +4920,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 72,
"id": "7f143230",
"metadata": {
"hidden": true
@@ -3869,7 +4928,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 1330x410 with 2 Axes>"
]
@@ -3911,7 +4970,7 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 73,
"id": "bc655fc0",
"metadata": {
"hidden": true
@@ -3925,7 +4984,7 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": 74,
"id": "b5b46df6",
"metadata": {
"hidden": true
@@ -3933,7 +4992,7 @@
"outputs": [
{
"data": {
- "image/png": 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iokLl5eWdP4fDYeXl5SWq5EHxZjkvzHyVSb1QAABncFS4aW9vV0pKip577jl5vV5J0kMPPaQf/vCHWrVqVY+9N+np6UpPT090qfgvpvRCAQCcwVHhZuzYsTr77LM7g40knXfeeYrFYvrXv/6l/Px8C6tDb0zohQIAOIOj9rmZNm2a6uvrFYlEOq/985//VGpqqsaNG2dhZfbHOVUAgGRhac9NJBLRvn37On8+ePCgampqNGrUKI0fP14VFRU6fPiwnnnmGUnSjTfeqHvvvVe33HKLli5dqmAwqLvuukvz588/7YRi2HuHYAAA4s3Snpvq6mr5/X75/X5JUnl5ufx+v5YsWSJJamho0KFDhzrvd7vdqqqq0okTJzR16lTddNNNmjlzpv74xz9aUr8TOGGHYAAA4omDMw23/1hEVz60/bTt28ov06QcdwIrAgBg4Aby/HbUnBsMHDsEAwCSDeHGcOwQDABINoQbw2Wkpep753ytxzZ2CAYAmMhR+9xgYELNUd3z6ke6edpExSS9ve+zzrbvnfM13f+/F9hy75lQc1TBSFThljZ5MtPkG8EeOQCA/iPcGCwYiepv/++Y3tn/meZ/b6LmT5uo1i/alT48VbvrTih6yn6nqbNsHQAwWIQbg3VMJm6OntJjb+zr1j7jWzmJLqlXfS1bXzHHTw8OAKBPA55zM2/ePAUCgaGoBXHmtMnEwUi0W7DpEKgNKhhhTx4AQN8GHG5CoZBmzJih/Px83X///Tp8+PBQ1IU48LldKs739dhmx8nELFsHAMTDgMPNpk2bdPjwYf385z/XCy+8oAkTJqikpETr169XWxsPHzvxZrm0rLSgW8ApzvdpeWmB7YZ4nNbTBACwp0HvUPzBBx9ozZo1euqpp+R2u/XjH/9Yd9xxh21P6E62HYql/6w+amxpU3ZGmnxue64+CjVH9YvndyvQw9BUcb6POTcAkMQStkNxQ0ODqqqqVFVVpWHDhunaa6/V3r17df755+vhhx8ezB+NOPJmuTQpx60p48/SpBy3bQOC03qaAAD2NOCem7a2Nr366qtas2aNXn/9dRUUFOi2227TjTfe2JmkNm7cqPnz5+vzzz8fkqIHIxl7bpzGKT1NAIDEGcjze8BLwceOHav29nbNmTNH7733nqZMmdLtnunTp2vkyJED/aMBSV/24BBmAABnasDh5uGHH9aPfvQjZWRknPaekSNH6uDBg4MqDAAA4EwMONz85Cc/GYo6AAAA4oKDMwEAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjDLgpeCwj46dfMMtbfJkpsk3gs3vAAAg3DhU/YmTunvDHu34r0Mmi/N9WlZaoNyRmRZWBgCAtRiWcqBQc7RbsJGkQG1QizfsUag5alFlSDah5qj2H4to96HPtf94hO8eAFug58aBgpFot2DTIVAbVDASNX54iiE569F7CMCuCDcOFG5p67W9sY92p+Ohar2+eg9XzPETNgFYhmEpB/JkpPXant1Hu5MxJGcP/ek9BACrEG4cyOd2qTjf12Nbcb5PPre5vzHzULWHZO89BGBvhBsH8ma5tKy0oFvAKc73aXlpgdHDATxU7SGZew8B2B9zbhwqd2SmVszxKxiJqrGlTdkZafK5zZ9Uy0PVHjp6DwM99KKZ3nsIwP7ouXEwb5ZLk3LcmjL+LE3KcRsfbKTkHpKzk2TuPQRgfymxWCxmdRGJFA6H5fV6FQqF5PF4rC4HZ6D+xEkt3rCnS69Bx0N1LKulEqpjSX4y9R4CsMZAnt8MS8FxknVIzo68Wfx3B2A/hBs4Eg9VAMDpMOcGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIzC8QsO0nFIYbilTZ7MNPlGcAQBAABfRbhxiPoTJ3X3hj3a8ZWTsJeVFiiXk7ABiF+AgA6EGwcINUe7BRtJCtQGtXjDHq2Y4+d/YECS4xcg4D+Yc+MAwUi0W7DpEKgNKhiJJrgiAHbS1y9AoWb+H4HkQrhxgHBLW6/tjX20AzAbvwABXRFuHMCTkdZre3Yf7QDMxi9AQFeEGwfwuV0qzvf12Fac75PPzXwbIJnxCxDQFeHGAbxZLi0rLegWcIrzfVpeWsBkYiDJ8QsQ0FVKLBaLWV1EIoXDYXm9XoVCIXk8HqvLGZCOZZ6NLW3KzkiTz80yTwBfqj9xUos37FHgK6ullpcWaCyrpWCAgTy/LV0KHggE9MADD2jXrl1qaGjQxo0bNWvWrH699+2339Zll12myZMnq6amZkjrtAtvFmEGQM9yR2ZqxRw/vwABsnhYqqmpSYWFhVq5cuWA3nfixAnNnTtXV1555RBVBgDO481yaVKOW1PGn6VJOW6CDZKWpT03JSUlKikpGfD7br/9dt14440aNmyYNm3a1Ou9ra2tam1t7fw5HA4P+O8DAADO4bgJxWvWrNGBAwd0zz339Ov+yspKeb3ezldeXt4QVwjThZqj2n8sot2HPtf+4xE2SAMAm3HU8Qu1tbVavHixduzYoeHD+1d6RUWFysvLO38Oh8MEHJwxtrgHAPtzTM/NqVOndOONN2rp0qX65je/2e/3paeny+PxdHkBZ4It7gHAGRzTc9PY2Kjq6mrt3r1bCxYskCS1t7crFotp+PDhev3113XFFVdYXCVM1p8t7pnACQDWc0y48Xg82rt3b5drq1at0htvvKH169dr4sSJFlXWXcd+NOGWNnky0+QbwXJME7DFPQA4g6XhJhKJaN++fZ0/Hzx4UDU1NRo1apTGjx+viooKHT58WM8884xSU1M1efLkLu/PyclRRkZGt+tWYk6GudjiHgCcwdI5N9XV1fL7/fL7/ZKk8vJy+f1+LVmyRJLU0NCgQ4cOWVnigDAnw2xscQ8AzsDxC3G0/1hEVz60/bTt28ov06Qcd1z/TiQWW9wDgDUcc/yCaZiTYT62uAcA+yPcxBFzMpIDZ3wBgL05Zp8bJ2BOBgAA1iPcxJE3y6VlpQXdAk7HnAx+2wcAYOgxLBVnzMkAAMBahJshwJwMAACsw7AUAAAwCj03iBuOnQAA2AHhBnHBsRMAALtgWAqDxrETAAA7Idxg0IKRaLdg0yFQG1QwQrgBACQO4QaDxrETAAA7Idxg0Dh2AgBgJ4QbnJFQc1T7j0W0+9DnGj4shWMnAAC2wWopDNhXV0ZluYbpzzdfrJjUbbUUx04AABItJRaLxawuIpHC4bC8Xq9CoZA8Ho/V5ThOqDmqBc/v7jaBOMs1TP/3/5yvqd84S02tX3DsBAAgrgby/GZYCgNyupVRzdFTqnh5r1JTUjRl/FmalOMm2AAALEG4wYCwMgoAYHeEGwwIK6MAAHZHuMGA+NwuVkYBAGyNcIMB8Wa5tKy0oFvAYWUUAMAuWAqOAcsdmakVc/wKRqJqbGljZRQAwFYINzgj3izCDADAnhiWAgAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwiqXhJhAIaObMmcrNzVVKSoo2bdrU6/0vv/yyrrrqKn3961+Xx+NRUVGRtm7dmphiAQCAI1gabpqamlRYWKiVK1f26/5AIKCrrrpKW7Zs0a5duzR9+nTNnDlTu3fvHuJKAQCAU6TEYrGY1UVIUkpKijZu3KhZs2YN6H3f/va3NXv2bC1ZsqRf94fDYXm9XoVCIXk8njOoFAAAJNpAnt/DE1TTkGhvb1djY6NGjRp12ntaW1vV2tra+XM4HE5EaQkRao4qGIkq3NImT2aafCNc8ma5rC4LAABLOTrc/OEPf1AkEtENN9xw2nsqKyu1dOnSBFaVGPUnTuruDXu0ozbYea0436dlpQXKHZlpYWUAAFjLsaul1q5dq6VLl+rFF19UTk7Oae+rqKhQKBTqfNXV1SWwyqERao52CzaSFKgNavGGPQo1Ry2qDAAA6zmy52bdunW67bbb9NJLL2nGjBm93puenq709PQEVZYYwUi0W7DpEKgNKhiJMjwFAEhajuu5ef7553XLLbfo+eef13XXXWd1OZYIt7T12t7YRzsAACaztOcmEolo3759nT8fPHhQNTU1GjVqlMaPH6+KigodPnxYzzzzjKQvh6LmzZunRx99VJdeeqmOHDkiScrMzJTX67Xk32AFT0Zar+3ZfbQDAGAyS3tuqqur5ff75ff7JUnl5eXy+/2dy7obGhp06NChzvuffPJJffHFFyorK9PYsWM7XwsXLrSkfqv43C4V5/t6bCvO98nnZkgKAJC8bLPPTaKYss9N/YmTWrxhjwJfWS21vLRAY1ktBQAwTNLsc5PMckdmasUcv4KRqBpb2pSdkSafm31uAAAg3DiYN4swAwDAVzlutRQAAEBvCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFGGW12AyULNUQUjUYVb2uTJTJNvhEveLJfVZQEAYDTCzRCpP3FSd2/Yox21wc5rxfk+LSstUO7ITAsrAwDAbAxLDYFQc7RbsJGkQG1QizfsUag5alFlAACYj3AzBIKRaLdg0yFQG1QwQrgBAGCoEG6GQLilrdf2xj7aAQDAmWPOzRDwZKT12p7dR7sVmPwMADAF4WYI+NwuFef7FOhhaKo43yef216hgcnPAACTMCw1BLxZLi0rLVBxvq/L9eJ8n5aXFtiqR4TJzwAA09BzM0RyR2ZqxRy/gpGoGlvalJ2RJp/bfkM9/Zn8bLeaAQDoDeFmCHmz7BNmTjenhsnPAADTEG6SQG9zaryZzpv8DABAb5hzY7i+5tSMSB/ebW5QBztOfgYAoC+EG8P1Nacm0vKFYyY/AwDQHwxLGa4/c2om5bgdMfkZAID+INwYrr8bCtpp8jMAAIPBsJThOjYU7AlzagAAJiLcGM5JGwoCABAPDEslAadsKAgAQDxY2nMTCAQ0c+ZM5ebmKiUlRZs2berzPW+++aYuvPBCpaen65xzztHTTz895HWawJvl0qQct6aMP0uTctwEGwCAsSwNN01NTSosLNTKlSv7df/Bgwd13XXXafr06aqpqdGiRYt02223aevWrUNcKQAAcApLh6VKSkpUUlLS7/sff/xxTZw4UQ8++KAk6bzzztNbb72lhx9+WNdcc81QlQkAABzEUROKd+7cqRkzZnS5ds0112jnzp2nfU9ra6vC4XCXFwAAMJejws2RI0c0evToLtdGjx6tcDiskydP9vieyspKeb3ezldeXl4iSgUAABZxVLg5ExUVFQqFQp2vuro6q0sCAABDyFFLwceMGaOjR492uXb06FF5PB5lZmb2+J709HSlp6cnojwAAGADjuq5KSoq0rZt27pcq6qqUlFRkUUVAQAAu7E03EQiEdXU1KimpkbSl0u9a2pqdOjQIUlfDinNnTu38/7bb79dBw4c0K9//Wt9/PHHWrVqlV588UX98pe/tKJ8AABgQ5aGm+rqavn9fvn9fklSeXm5/H6/lixZIklqaGjoDDqSNHHiRP3lL39RVVWVCgsL9eCDD+qpp55iGTgAAOiUEovFYlYXkUjhcFher1ehUEgej8fqcgAAQD8M5PntqAnF8dCR5djvBgAA5+h4bvenTybpwk1jY6Mksd8NAAAO1NjYKK/X2+s9STcs1d7ervr6emVnZyslJWVA7w2Hw8rLy1NdXR1DWjbC52JffDb2xOdiT3wuvYvFYmpsbFRubq5SU3ufMpx0PTepqakaN27coP4Mj8fDF8+G+Fzsi8/Gnvhc7InP5fT66rHp4Kh9bgAAAPpCuAEAAEYh3AxAenq67rnnHo5zsBk+F/vis7EnPhd74nOJn6SbUAwAAMxGzw0AADAK4QYAABiFcAMAAIxCuAEAAEYh3PTTypUrNWHCBGVkZOjSSy/Ve++9Z3VJSS8QCGjmzJnKzc1VSkqKNm3aZHVJkFRZWamLL75Y2dnZysnJ0axZs/TJJ59YXRYkrV69WgUFBZ2bxBUVFemvf/2r1WXhK5YtW6aUlBQtWrTI6lIci3DTDy+88ILKy8t1zz336IMPPlBhYaGuueYaHTt2zOrSklpTU5MKCwu1cuVKq0vBf9m+fbvKysr07rvvqqqqSm1tbbr66qvV1NRkdWlJb9y4cVq2bJl27dql6upqXXHFFbr++uv10UcfWV0a/u3999/XE088oYKCAqtLcTSWgvfDpZdeqosvvliPPfaYpC/Pp8rLy9MvfvELLV682OLqIEkpKSnauHGjZs2aZXUp+Irjx48rJydH27dvV3FxsdXl4CtGjRqlBx54QLfeeqvVpSS9SCSiCy+8UKtWrdJ9992nKVOm6JFHHrG6LEei56YP0WhUu3bt0owZMzqvpaamasaMGdq5c6eFlQHOEAqFJH35EIV9nDp1SuvWrVNTU5OKioqsLgeSysrKdN1113V53uDMJN3BmQMVDAZ16tQpjR49usv10aNH6+OPP7aoKsAZ2tvbtWjRIk2bNk2TJ0+2uhxI2rt3r4qKitTS0iK3262NGzfq/PPPt7qspLdu3Tp98MEHev/9960uxQiEGwBDpqysTB9++KHeeustq0vBv5177rmqqalRKBTS+vXrNW/ePG3fvp2AY6G6ujotXLhQVVVVysjIsLocIxBu+uDz+TRs2DAdPXq0y/WjR49qzJgxFlUF2N+CBQu0efNmBQIBjRs3zupy8G8ul0vnnHOOJOmiiy7S+++/r0cffVRPPPGExZUlr127dunYsWO68MILO6+dOnVKgUBAjz32mFpbWzVs2DALK3Qe5tz0weVy6aKLLtK2bds6r7W3t2vbtm2MUwM9iMViWrBggTZu3Kg33nhDEydOtLok9KK9vV2tra1Wl5HUrrzySu3du1c1NTWdr6lTp+qmm25STU0NweYM0HPTD+Xl5Zo3b56mTp2qSy65RI888oiampp0yy23WF1aUotEItq3b1/nzwcPHlRNTY1GjRql8ePHW1hZcisrK9PatWv1yiuvKDs7W0eOHJEkeb1eZWZmWlxdcquoqFBJSYnGjx+vxsZGrV27Vm+++aa2bt1qdWlJLTs7u9uctBEjRuhrX/sac9XOEOGmH2bPnq3jx49ryZIlOnLkiKZMmaLXXnut2yRjJFZ1dbWmT5/e+XN5ebkkad68eXr66actqgqrV6+WJF1++eVdrq9Zs0Y333xz4gtCp2PHjmnu3LlqaGiQ1+tVQUGBtm7dqquuusrq0oC4Yp8bAABgFObcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAjnf8+HGNGTNG999/f+e1d955Ry6XS9u2bbOwMgBW4OBMAEbYsmWLZs2apXfeeUfnnnuupkyZouuvv14PPfSQ1aUBSDDCDQBjlJWV6W9/+5umTp2qvXv36v3331d6errVZQFIMMINAGOcPHlSkydPVl1dnXbt2qULLrjA6pIAWIA5NwCMsX//ftXX16u9vV2ffvqp1eUAsAg9NwCMEI1Gdckll2jKlCk699xz9cgjj2jv3r3KycmxujQACUa4AWCEu+66S+vXr9ff//53ud1uXXbZZfJ6vdq8ebPVpQFIMIalADjem2++qUceeUTPPvusPB6PUlNT9eyzz2rHjh1avXq11eUBSDB6bgAAgFHouQEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUf4/YGNcfUAfFQIAAAAASUVORK5CYII=\n",
+ "image/png": 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iokLl5eWdP4fDYeXl5SWq5EHxZjkvzHyVSb1QAABncFS4aW9vV0pKip577jl5vV5J0kMPPaQf/vCHWrVqVY+9N+np6UpPT090qfgvpvRCAQCcwVHhZuzYsTr77LM7g40knXfeeYrFYvrXv/6l/Px8C6tDb0zohQIAOIOj9rmZNm2a6uvrFYlEOq/985//VGpqqsaNG2dhZfbHOVUAgGRhac9NJBLRvn37On8+ePCgampqNGrUKI0fP14VFRU6fPiwnnnmGUnSjTfeqHvvvVe33HKLli5dqmAwqLvuukvz588/7YRi2HuHYAAA4s3Snpvq6mr5/X75/X5JUnl5ufx+v5YsWSJJamho0KFDhzrvd7vdqqqq0okTJzR16lTddNNNmjlzpv74xz9aUr8TOGGHYAAA4omDMw23/1hEVz60/bTt28ov06QcdwIrAgBg4Aby/HbUnBsMHDsEAwCSDeHGcOwQDABINoQbw2Wkpep753ytxzZ2CAYAmMhR+9xgYELNUd3z6ke6edpExSS9ve+zzrbvnfM13f+/F9hy75lQc1TBSFThljZ5MtPkG8EeOQCA/iPcGCwYiepv/++Y3tn/meZ/b6LmT5uo1i/alT48VbvrTih6yn6nqbNsHQAwWIQbg3VMJm6OntJjb+zr1j7jWzmJLqlXfS1bXzHHTw8OAKBPA55zM2/ePAUCgaGoBXHmtMnEwUi0W7DpEKgNKhhhTx4AQN8GHG5CoZBmzJih/Px83X///Tp8+PBQ1IU48LldKs739dhmx8nELFsHAMTDgMPNpk2bdPjwYf385z/XCy+8oAkTJqikpETr169XWxsPHzvxZrm0rLSgW8ApzvdpeWmB7YZ4nNbTBACwp0HvUPzBBx9ozZo1euqpp+R2u/XjH/9Yd9xxh21P6E62HYql/6w+amxpU3ZGmnxue64+CjVH9YvndyvQw9BUcb6POTcAkMQStkNxQ0ODqqqqVFVVpWHDhunaa6/V3r17df755+vhhx8ezB+NOPJmuTQpx60p48/SpBy3bQOC03qaAAD2NOCem7a2Nr366qtas2aNXn/9dRUUFOi2227TjTfe2JmkNm7cqPnz5+vzzz8fkqIHIxl7bpzGKT1NAIDEGcjze8BLwceOHav29nbNmTNH7733nqZMmdLtnunTp2vkyJED/aMBSV/24BBmAABnasDh5uGHH9aPfvQjZWRknPaekSNH6uDBg4MqDAAA4EwMONz85Cc/GYo6AAAA4oKDMwEAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjDLgpeCwj46dfMMtbfJkpsk3gs3vAAAg3DhU/YmTunvDHu34r0Mmi/N9WlZaoNyRmRZWBgCAtRiWcqBQc7RbsJGkQG1QizfsUag5alFlSDah5qj2H4to96HPtf94hO8eAFug58aBgpFot2DTIVAbVDASNX54iiE569F7CMCuCDcOFG5p67W9sY92p+Ohar2+eg9XzPETNgFYhmEpB/JkpPXant1Hu5MxJGcP/ek9BACrEG4cyOd2qTjf12Nbcb5PPre5vzHzULWHZO89BGBvhBsH8ma5tKy0oFvAKc73aXlpgdHDATxU7SGZew8B2B9zbhwqd2SmVszxKxiJqrGlTdkZafK5zZ9Uy0PVHjp6DwM99KKZ3nsIwP7ouXEwb5ZLk3LcmjL+LE3KcRsfbKTkHpKzk2TuPQRgfymxWCxmdRGJFA6H5fV6FQqF5PF4rC4HZ6D+xEkt3rCnS69Bx0N1LKulEqpjSX4y9R4CsMZAnt8MS8FxknVIzo68Wfx3B2A/hBs4Eg9VAMDpMOcGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIzC8QsO0nFIYbilTZ7MNPlGcAQBAABfRbhxiPoTJ3X3hj3a8ZWTsJeVFiiXk7ABiF+AgA6EGwcINUe7BRtJCtQGtXjDHq2Y4+d/YECS4xcg4D+Yc+MAwUi0W7DpEKgNKhiJJrgiAHbS1y9AoWb+H4HkQrhxgHBLW6/tjX20AzAbvwABXRFuHMCTkdZre3Yf7QDMxi9AQFeEGwfwuV0qzvf12Fac75PPzXwbIJnxCxDQFeHGAbxZLi0rLegWcIrzfVpeWsBkYiDJ8QsQ0FVKLBaLWV1EIoXDYXm9XoVCIXk8HqvLGZCOZZ6NLW3KzkiTz80yTwBfqj9xUos37FHgK6ullpcWaCyrpWCAgTy/LV0KHggE9MADD2jXrl1qaGjQxo0bNWvWrH699+2339Zll12myZMnq6amZkjrtAtvFmEGQM9yR2ZqxRw/vwABsnhYqqmpSYWFhVq5cuWA3nfixAnNnTtXV1555RBVBgDO481yaVKOW1PGn6VJOW6CDZKWpT03JSUlKikpGfD7br/9dt14440aNmyYNm3a1Ou9ra2tam1t7fw5HA4P+O8DAADO4bgJxWvWrNGBAwd0zz339Ov+yspKeb3ezldeXt4QVwjThZqj2n8sot2HPtf+4xE2SAMAm3HU8Qu1tbVavHixduzYoeHD+1d6RUWFysvLO38Oh8MEHJwxtrgHAPtzTM/NqVOndOONN2rp0qX65je/2e/3paeny+PxdHkBZ4It7gHAGRzTc9PY2Kjq6mrt3r1bCxYskCS1t7crFotp+PDhev3113XFFVdYXCVM1p8t7pnACQDWc0y48Xg82rt3b5drq1at0htvvKH169dr4sSJFlXWXcd+NOGWNnky0+QbwXJME7DFPQA4g6XhJhKJaN++fZ0/Hzx4UDU1NRo1apTGjx+viooKHT58WM8884xSU1M1efLkLu/PyclRRkZGt+tWYk6GudjiHgCcwdI5N9XV1fL7/fL7/ZKk8vJy+f1+LVmyRJLU0NCgQ4cOWVnigDAnw2xscQ8AzsDxC3G0/1hEVz60/bTt28ov06Qcd1z/TiQWW9wDgDUcc/yCaZiTYT62uAcA+yPcxBFzMpIDZ3wBgL05Zp8bJ2BOBgAA1iPcxJE3y6VlpQXdAk7HnAx+2wcAYOgxLBVnzMkAAMBahJshwJwMAACsw7AUAAAwCj03iBuOnQAA2AHhBnHBsRMAALtgWAqDxrETAAA7Idxg0IKRaLdg0yFQG1QwQrgBACQO4QaDxrETAAA7Idxg0Dh2AgBgJ4QbnJFQc1T7j0W0+9DnGj4shWMnAAC2wWopDNhXV0ZluYbpzzdfrJjUbbUUx04AABItJRaLxawuIpHC4bC8Xq9CoZA8Ho/V5ThOqDmqBc/v7jaBOMs1TP/3/5yvqd84S02tX3DsBAAgrgby/GZYCgNyupVRzdFTqnh5r1JTUjRl/FmalOMm2AAALEG4wYCwMgoAYHeEGwwIK6MAAHZHuMGA+NwuVkYBAGyNcIMB8Wa5tKy0oFvAYWUUAMAuWAqOAcsdmakVc/wKRqJqbGljZRQAwFYINzgj3izCDADAnhiWAgAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwiqXhJhAIaObMmcrNzVVKSoo2bdrU6/0vv/yyrrrqKn3961+Xx+NRUVGRtm7dmphiAQCAI1gabpqamlRYWKiVK1f26/5AIKCrrrpKW7Zs0a5duzR9+nTNnDlTu3fvHuJKAQCAU6TEYrGY1UVIUkpKijZu3KhZs2YN6H3f/va3NXv2bC1ZsqRf94fDYXm9XoVCIXk8njOoFAAAJNpAnt/DE1TTkGhvb1djY6NGjRp12ntaW1vV2tra+XM4HE5EaQkRao4qGIkq3NImT2aafCNc8ma5rC4LAABLOTrc/OEPf1AkEtENN9xw2nsqKyu1dOnSBFaVGPUnTuruDXu0ozbYea0436dlpQXKHZlpYWUAAFjLsaul1q5dq6VLl+rFF19UTk7Oae+rqKhQKBTqfNXV1SWwyqERao52CzaSFKgNavGGPQo1Ry2qDAAA6zmy52bdunW67bbb9NJLL2nGjBm93puenq709PQEVZYYwUi0W7DpEKgNKhiJMjwFAEhajuu5ef7553XLLbfo+eef13XXXWd1OZYIt7T12t7YRzsAACaztOcmEolo3759nT8fPHhQNTU1GjVqlMaPH6+KigodPnxYzzzzjKQvh6LmzZunRx99VJdeeqmOHDkiScrMzJTX67Xk32AFT0Zar+3ZfbQDAGAyS3tuqqur5ff75ff7JUnl5eXy+/2dy7obGhp06NChzvuffPJJffHFFyorK9PYsWM7XwsXLrSkfqv43C4V5/t6bCvO98nnZkgKAJC8bLPPTaKYss9N/YmTWrxhjwJfWS21vLRAY1ktBQAwTNLsc5PMckdmasUcv4KRqBpb2pSdkSafm31uAAAg3DiYN4swAwDAVzlutRQAAEBvCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFGGW12AyULNUQUjUYVb2uTJTJNvhEveLJfVZQEAYDTCzRCpP3FSd2/Yox21wc5rxfk+LSstUO7ITAsrAwDAbAxLDYFQc7RbsJGkQG1QizfsUag5alFlAACYj3AzBIKRaLdg0yFQG1QwQrgBAGCoEG6GQLilrdf2xj7aAQDAmWPOzRDwZKT12p7dR7sVmPwMADAF4WYI+NwuFef7FOhhaKo43yef216hgcnPAACTMCw1BLxZLi0rLVBxvq/L9eJ8n5aXFtiqR4TJzwAA09BzM0RyR2ZqxRy/gpGoGlvalJ2RJp/bfkM9/Zn8bLeaAQDoDeFmCHmz7BNmTjenhsnPAADTEG6SQG9zaryZzpv8DABAb5hzY7i+5tSMSB/ebW5QBztOfgYAoC+EG8P1Nacm0vKFYyY/AwDQHwxLGa4/c2om5bgdMfkZAID+INwYrr8bCtpp8jMAAIPBsJThOjYU7AlzagAAJiLcGM5JGwoCABAPDEslAadsKAgAQDxY2nMTCAQ0c+ZM5ebmKiUlRZs2berzPW+++aYuvPBCpaen65xzztHTTz895HWawJvl0qQct6aMP0uTctwEGwCAsSwNN01NTSosLNTKlSv7df/Bgwd13XXXafr06aqpqdGiRYt02223aevWrUNcKQAAcApLh6VKSkpUUlLS7/sff/xxTZw4UQ8++KAk6bzzztNbb72lhx9+WNdcc81QlQkAABzEUROKd+7cqRkzZnS5ds0112jnzp2nfU9ra6vC4XCXFwAAMJejws2RI0c0evToLtdGjx6tcDiskydP9vieyspKeb3ezldeXl4iSgUAABZxVLg5ExUVFQqFQp2vuro6q0sCAABDyFFLwceMGaOjR492uXb06FF5PB5lZmb2+J709HSlp6cnojwAAGADjuq5KSoq0rZt27pcq6qqUlFRkUUVAQAAu7E03EQiEdXU1KimpkbSl0u9a2pqdOjQIUlfDinNnTu38/7bb79dBw4c0K9//Wt9/PHHWrVqlV588UX98pe/tKJ8AABgQ5aGm+rqavn9fvn9fklSeXm5/H6/lixZIklqaGjoDDqSNHHiRP3lL39RVVWVCgsL9eCDD+qpp55iGTgAAOiUEovFYlYXkUjhcFher1ehUEgej8fqcgAAQD8M5PntqAnF8dCR5djvBgAA5+h4bvenTybpwk1jY6Mksd8NAAAO1NjYKK/X2+s9STcs1d7ervr6emVnZyslJWVA7w2Hw8rLy1NdXR1DWjbC52JffDb2xOdiT3wuvYvFYmpsbFRubq5SU3ufMpx0PTepqakaN27coP4Mj8fDF8+G+Fzsi8/Gnvhc7InP5fT66rHp4Kh9bgAAAPpCuAEAAEYh3AxAenq67rnnHo5zsBk+F/vis7EnPhd74nOJn6SbUAwAAMxGzw0AADAK4QYAABiFcAMAAIxCuAEAAEYh3PTTypUrNWHCBGVkZOjSSy/Ve++9Z3VJSS8QCGjmzJnKzc1VSkqKNm3aZHVJkFRZWamLL75Y2dnZysnJ0axZs/TJJ59YXRYkrV69WgUFBZ2bxBUVFemvf/2r1WXhK5YtW6aUlBQtWrTI6lIci3DTDy+88ILKy8t1zz336IMPPlBhYaGuueYaHTt2zOrSklpTU5MKCwu1cuVKq0vBf9m+fbvKysr07rvvqqqqSm1tbbr66qvV1NRkdWlJb9y4cVq2bJl27dql6upqXXHFFbr++uv10UcfWV0a/u3999/XE088oYKCAqtLcTSWgvfDpZdeqosvvliPPfaYpC/Pp8rLy9MvfvELLV682OLqIEkpKSnauHGjZs2aZXUp+Irjx48rJydH27dvV3FxsdXl4CtGjRqlBx54QLfeeqvVpSS9SCSiCy+8UKtWrdJ9992nKVOm6JFHHrG6LEei56YP0WhUu3bt0owZMzqvpaamasaMGdq5c6eFlQHOEAqFJH35EIV9nDp1SuvWrVNTU5OKioqsLgeSysrKdN1113V53uDMJN3BmQMVDAZ16tQpjR49usv10aNH6+OPP7aoKsAZ2tvbtWjRIk2bNk2TJ0+2uhxI2rt3r4qKitTS0iK3262NGzfq/PPPt7qspLdu3Tp98MEHev/9960uxQiEGwBDpqysTB9++KHeeustq0vBv5177rmqqalRKBTS+vXrNW/ePG3fvp2AY6G6ujotXLhQVVVVysjIsLocIxBu+uDz+TRs2DAdPXq0y/WjR49qzJgxFlUF2N+CBQu0efNmBQIBjRs3zupy8G8ul0vnnHOOJOmiiy7S+++/r0cffVRPPPGExZUlr127dunYsWO68MILO6+dOnVKgUBAjz32mFpbWzVs2DALK3Qe5tz0weVy6aKLLtK2bds6r7W3t2vbtm2MUwM9iMViWrBggTZu3Kg33nhDEydOtLok9KK9vV2tra1Wl5HUrrzySu3du1c1NTWdr6lTp+qmm25STU0NweYM0HPTD+Xl5Zo3b56mTp2qSy65RI888oiampp0yy23WF1aUotEItq3b1/nzwcPHlRNTY1GjRql8ePHW1hZcisrK9PatWv1yiuvKDs7W0eOHJEkeb1eZWZmWlxdcquoqFBJSYnGjx+vxsZGrV27Vm+++aa2bt1qdWlJLTs7u9uctBEjRuhrX/sac9XOEOGmH2bPnq3jx49ryZIlOnLkiKZMmaLXXnut2yRjJFZ1dbWmT5/e+XN5ebkkad68eXr66actqgqrV6+WJF1++eVdrq9Zs0Y333xz4gtCp2PHjmnu3LlqaGiQ1+tVQUGBtm7dqquuusrq0oC4Yp8bAABgFObcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAjnf8+HGNGTNG999/f+e1d955Ry6XS9u2bbOwMgBW4OBMAEbYsmWLZs2apXfeeUfnnnuupkyZouuvv14PPfSQ1aUBSDDCDQBjlJWV6W9/+5umTp2qvXv36v3331d6errVZQFIMMINAGOcPHlSkydPVl1dnXbt2qULLrjA6pIAWIA5NwCMsX//ftXX16u9vV2ffvqp1eUAsAg9NwCMEI1Gdckll2jKlCk699xz9cgjj2jv3r3KycmxujQACUa4AWCEu+66S+vXr9ff//53ud1uXXbZZfJ6vdq8ebPVpQFIMIalADjem2++qUceeUTPPvusPB6PUlNT9eyzz2rHjh1avXq11eUBSDB6bgAAgFHouQEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUf4/YGNcfUAfFQIAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -3950,7 +5009,7 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 75,
"id": "a0b5ffc9",
"metadata": {
"hidden": true
@@ -3958,7 +5017,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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8Bdu3b8ff/M3f4FOf+hRefvllnDx5En//93+/yJ8KFYttrXV47uEN031GQpoOsyjA1+hknxEqG6wLKR+RpIKecyPo9gfw+8shZPuOiwKwqbkK7T4v7llTh0prefUkndPW3lyNUl588UV84QtfAADs2LEDK1euxEsvvTT95z/+8Y/xV3/1V9NNz775zW/OqekZt/aWJnZgpXKUVFSMRFM8UbfEyWkNxy+MossfwImLY1DU7I/atV4H2n1e7FpXj1qHceUJRm/tXVCfkXxhGCGiYqdqOsZiMiLJ7F0yqfipmo7fDUzgkD+AN86PIJajQV2j25Zpye7zYkWNPc+jzM7oMFJe80BERAbgkkzp0nUd54NRdPuDONwbxGgse1uCqgozdqyrR4fPC1+jsyBbshuJYYSIaIkkFRWjMRmpHIeWUfG6OpFAd28Q3f4gLo3Fs15jM4v4ZGsdOnxefPyWwm/JbiSGESKiRcYlmdI0EZdx5OwwuvxBvDeYvTO4JAq4a2U12tu82NZai4o5tGQvZwwjRESLiEsypSUhq/h1/wi6/EGcfH8sZ0v2W5tcaPd5sWNtPdz24mzJbiSGESKiRcAlmdKRVjWc/GAc3f4gft03gmSOnU+31NrR4fNgV5sHje6KPI+ytDCMEBEtAJdkSoOu63hvMIwufxBHzg4jlMj+/axzWCZbsnvRUl/JQtRFwjBCRDQPuq4jnEhjPC5DK/wOCZTDpdE4unoD6PYHMRhKZr2m0iph+5pMS/bbllexEHUJMIwQEc1RNJXGeIwH2hWrkWgKr/UG0eUP4nwwmvUasyRgy+patPs82LKqtixashuJYYSIaJbichrjcYV1IUUomkrj9fOZluzvXJrI2pJdALCxuQodPg/uXVMPh42PyHzhV5qI6CZiqcxyDFu4Fxc5reG3F8fQ7Q/g+IXRnC3ZW+sdaJ8sRK138sR4IzCMEBHlwOWY4qPpOn5/OYQufwBHz40gmkpnva7BZUO7z4N2nwcrayvzPEr6KIYRIqJr6LqOaCqNibjCEFIkdF3HheEYuvwBHO4dxnA0lfU6l82EHes86PB5sL7JxZ0wBYRhhIgImQdaJJVGiCGkaAyFkzjsD6LLH8D7ozlasptEbGutQ4fPgztvqYZJYiFqIWIYIaKyx+WY4hFKKOg5N4xufwCnr2RvyS4KwJ23VKPd58UnW+tQYWFL9kLHMEJEZWu+XVM1XUdfIIZQUobbZkGrtxIip/yXTFJRcax/FF3+AN58fzxnq31foxPtbV7sWFePmkpLnkdJC8EwQkRlR05rGI/LiOUobryRdy6N4+CJAQyMxqBoOsyigObaSjyyuRmbVlQvwWjLk6rpePvSOLr8QbxxfgSJHIFxeXUFOnwetLd5sayaLdmLFcMIEZUNVdMxHpcRSaahz6Nr6juXxvHCoXOIyypcNjNckgBF1XFhOIoXDp3D3vvWMpAsgK7r6B2KoNsfxGtngxiPZ2/JXlNpwa62enT4vFjjcbAQtQQwjBBRydN1HaGEgom4Mu/W7Zqu4+CJAcRlFXUOCwRkHoBWk4A6hwUjURkHTwxgY3MVl2zm6PJ4HF3+ILr9QVyZSGS9xm6RcM+aOnT4vLi9mS3ZSw3DCBGVtMUqTu0LxDAwGoPLZp4OIlMECHDazBgYjaEvEMPaBseCXqscjMVkvHY205L97FAk6zUmUcAnVtWg3efF1tU1sJpZiFqqGEaIqCTNtzg1l1BShqLpcEnZfyO3SAIiuo5QUl6U1ytFcTmNN86PoMsfxNuXxpGjDhW3LXdPt2R3VZjzO0gyBMMIEZWUtKphLCbn7Lw5X26bBWYxUyNiNV0fSGRVh1kQ4LZxF8e1FFXDm++PodsfxLH+UaRytNRfXV+J9rZMS3avy5bnUZLRGEaIqGSE4grG4/K860JupNVbiebaSlwYjs6oGQEAHToiSQWr6x1o9bK1uKbrePdKCN29QfScHUY4mT0YepxWtPs86PB5saqOX7dyxjBCC6ZpOs5cDWMsLqPGbsH6JhdEFpdRHiUVFSPR1JIeZCcKAh7Z3IwXDp3DSFSG02aGRRIgq5kgYrdIeGRzc1kXr14cmWrJHkQgnL0lu9Nmwo619Wj3eXDrMndZf73oQwwjtCDH+kZwoKcf/cEoFFWHWRLQ4nHgie0t2NZaZ/TwqMSlVQ3jcQWRZPYtoItt04pq7L1v7XSfkYieWZpZXe8o2z4jwXASh3uD6OoN4sJwLOs1FpOIratr0eHzYPOqGpjZkp0+QtDns9k+z8LhMNxuN0KhEFwul9HDoUnH+kbwdOdpRFNpVNstsEgi5MmHg8Mq4bmHNzCQ0JLQdR0TcQWhxPy36i5EuXdgjSQV9JwbQbc/gN9fDiHbd0AUMuGtw+fBJ1vrUGnl776FrMFtg92y+N+j2T6/+beD5kXTdBzo6Uc0lUaDyzbddMgmSmhwiRgKp3Cgpx9bVtdyyYYWVSSpYDymIK0Zd46MKAhlt303pag4fmEM3f4AfntxDOkcW2HWeZ1o93mwc109ah3WPI+SihXDCM3Lmath9AejqLZbrut+KAgCquxm9AejOHM1jA3L3QaNkkpJLJXGGA+zyytV03FqYAJd/gDeOD+CmJx9m/Syqgq0+zxob/Ogucae51FSKWAYoXkZi8tQVB2WHGu/VklESNMxFmfPBVqYhKxiLL54/ULoxnRdx/lgFN3+IA73BjEay/5vuNpuxs51HrT7PGhrcLIlOy0IwwjNS43dArMkQFY12MTruyKmVA1mUUCNfel6LnAXT2lTVA2jURlxeXH7heRS7nUgVycS6PYH0eUPYGA8e0v2CrOET66pQ4fPgztWVLMlOy0ahhGal/VNLrR4HPAPRtDgEmf8VjRVXOhrdGJ909IUHHMXT+ma+vszkVDmdZjdfJTrSbzjcRlHzg6j2x/Ae4PZW7JLooC7Vlajw+fFtpZa2NiSnZYAd9PQvH24m0ZFld0MqyQipWqYWOLdNNzFU7richqj0fzWhXz0JF7z5Em84cneIaV2Em9CVvHr/kxL9pPvj+VsyX5rkwvtPi92rK2H286W7KWOu2moaG1rrcNzD2+YnqEITf5G6Wt0LtkMBXfxlCY5nWnhnq8lmSnlchJvWtVw8oNxdPmDONY3gmSO5nC31NjR8TEP2tu8aHCzJTvlD8MILci21jpsWV2bt9oN7uIpLaqmYzwuI5JM521J5lqlfBKvrmdqqrr9QRw5N4xQIntjuDqHBbvaMi3ZW+orWYhKhmAYoQUTRSFvD37u4ikNuq4jnEgv2Tkys1WKJ/F+MBpD1+ROmMFQMus1lVYJ29dkWrLftryKhahkOIYRKiqFsIuH5k/XdYSTaYTixjYtm1IqJ/EOR1J47WwQXf4g+oLRrNeYJQFbVtei3efBllW1sJjYkp0KB8MIFRWjd/HQ/EWSCibiSkE1LSvmk3ijqTRePzeMrt4gTl2ayNqSXQBw+4oqdLR5cM+aejhs/JFPhYl/M6moiKKAJ7a34OnO0xgKp7Lu4nliewuLVwtIIYaQKcV2Eq+c1vDbi5mW7McvjEJRsy9xtXoc6PB5sHOdB/VOtmSnwsetvVSUZvQZmdzFwz4jhSWaSmO8SNq3z+gzMnkSb6H0GdF0Hb+/HEKXP4Cj50YQTWXfcdTotk0WonpwS23hzeRQYePWXqJ5yPcuHpq9uJw5Q0bOsX20EG1aUY2NzVUF04FV13X0D8fQ5Q/gcG8QI9HsBbTuCjN2rM0Uoq5vcnEnDBUthhEqWvncxUM3l0qrGIvJSOQ4TK3QFcJJvEOhJLp7A+jyB/HBaDzrNVaTiLtbMy3Z77ylGqYcO8uIignDCBEtSFrVMBaXEU3mt2FZqQjFFRw5l2nJ/u7VcNZrRAG485ZqtPu8+GRrHSosbMlOpWXOYeTo0aP41re+hbfeeguDg4Po7OzE7t27b3jPD3/4Q3zzm9/E+fPn4Xa78eCDD+Jb3/oWamtr5ztuIjKYpukIJRSEEoqhvUKKUVJR8eu+UXT3BvDm++NQc/Rk9zU60d7mxc62elRzuzqVsDmHkVgsho0bN+Lxxx/Hpz/96Zte/+tf/xqPPvoo/tt/+2946KGHcOXKFXzpS1/Cn/3Zn+GnP/3pvAZNRMYKJxVMxAqjV0ixUDUdb30wji5/AG/0jSCpZP/aNVdXoMPnxS6fB8uqKvI8SiJjzDmMPPjgg3jwwQdnff3x48excuVK/If/8B8AAKtWrcK/+3f/Dt/4xjfm+tJEZLCErGI0liqq4lQj6bqO3qEIuvxBHDkbxHg8e0v22koLdrbVo8PnxRqPg4WoVHaWvGZk69atePrpp/HLX/4SDz74IILBIH7yk5/gD/7gD3Lek0qlkEqlpt8Ph7OvoxJRfqTSKsZjSt4PsitWA2NxdPuD6O4N4spEIus1douEe9bUocPnxe3NbMlO5W3Jw8jdd9+NH/7wh/jc5z6HZDKJdDqNhx56CN/97ndz3rN//348++yzSz00IrqJtKphPK4gksz+Gz19aCwm43BvEN3+IM4GIlmvMYkCPrG6Bh0+L7asqoHVzEJUImCBTc8EQbhpAet7772Hjo4O/Pmf/zkeeOABDA4O4i/+4i9w11134R//8R+z3pNtZqS5uZlNz4jyZKq1PotTbyyWSuONvhF0+YN459I4ctShYuNyN9p9XmxfWwenzZzfQRLNQsk3Pdu/fz/uvvtu/MVf/AUA4LbbbkNlZSXuuecefP3rX0djY+N191itVlitbGFMZIRIUsE4i1NzUlQNJy6OodsfxLELoznrZ1bXV6KjzYNdbR54XLY8j5KouCx5GInH4zCZZr6MJGWmJougEz1R2UgqKkaiLE7NRtN1nL4SwmF/ED3nhhHO0VPF47Si3edBh8+LVXVsyU40W3MOI9FoFH19fdPvX7x4EadOnUJNTQ1WrFiBffv24cqVK/jBD34AAHjooYfwZ3/2Zzhw4MD0Ms1TTz2FzZs3o6mpafE+EyKal7SqYSwm5zzzpJxdGI6iyx/E4d4ggpFU1mtcNhO2T7Zkv3WZu2AO1SMqJnMOIydPnsTOnTun39+7dy8A4LHHHsNLL72EwcFBXLp0afrPv/CFLyASieA73/kO/uN//I+oqqrCrl27uLWXyGC6nmlaNhFnXci1AuHkdCHqhZFY1mssJhHbVtei3efB5lU1MLMlO9GC8NReojIUS2UOsyuGE3XzIZxQcPT8MLr8Qfz+cijrNaIA3LGiGu0+Dz7ZWodKK0/ToNJR8gWsRFQ4kkrmMLuk8uFhdpquF8xptfmUUlQcvzCGbn8Av704hnSOrTDrGpzo8Hmwc50HNZVsyU60FBhGiMpAKq1iIq4g9pG6kHcujePgiQEMjMagaDrMooDm2ko8srkZm1ZUGzTapaNqOk4NTKDLH8Dr50cQz3HC8LKqCrT7PGhv86C5xp7nURKVH4YRohKWVFSEEteHECATRF44dA5xWYXLZoZLEqCoOi4MR/HCoXPYe9/akggkuq7jfDCKLn8Ar/UOYzQmZ72u2m7GznUetPs8aGtwsiU7UR4xjFBR0zQdZ66GMRaXUWO3YH2TCyLbaiMhq5hIyEjk+M1f03UcPDGAuKyizmGBgMzXzGoSUOewYCQq4+CJAWxsriraJZsrEwl0+wPo9gcxMJ69JXuFOdOSvd3nwR0rqtmSncggDCNUtI71jeBATz/6g1Eoqg6zJKDF48AT21uwrbXO6OEZIiGrGI/PrAnJpi8Qw8BoDC6beTqITBEgwGkzY2A0hr5ADGsbHEs55EU1HpfxWu8wunsD8A9mb8kuiQI2r6xBh8+DrS21sLElO5HhGEaoKB3rG8HTnacRTaVRbbfAIomQVQ3+wQie7jyN5x7eUFaBJC6nMR5XkLpJCJkSSspQNB0uKftMgEUSENF1hJLZlzQKSUJW8UbfCLr9AZz8IHdL9lubXGj3ebFjbT3cdrZkJyokDCNUdDRNx4GefkRTaTS4bNNr+zZRQoNLxFA4hQM9/diyurbkl2zmGkKmuG0WmMVMjYjVdP3XSFZ1mAUBblth7h5JqxpOfjCOLn8Qx/pGkMzRNXZlrR0dPi92tXnQ4GZLdqJCxTBCRefM1TD6g1FU2y3XFRkKgoAquxn9wSjOXA1jw3K3QaNcWrFUGhOJuYeQKa3eSjTXVuLCcHRGzQgA6NARSSpYXe9Aq7dwWprreqY+qMsfxJGzwZwt2esdVuxqq0eHz4vV9ZUsRCUqAgwjVHTG4jIUVYclR9dLqyQipOkYixf+EsNcLdb5MaIg4JHNzXjh0DmMRGU4bWZYJAGymgkidouERzY3F0Tx6vujMXT7Mx1Rh8LJrNc4rCbcu7YOHT4vblvOluxExYZhhIpOjd0CsyRAVjXYxOuLD1OqBrMooMZemEsM86GoGsYX+fyYTSuqsfe+tdN9RiJ6Zmlmdb3D8D4jw5HUdEv2vuFo1mvMkoCtLbVob/PiE6tqYDGxJTtRsWIYoaKzvsmFFo8D/sEIGlzijGl4XdcxEVfga3RifVPxHx2gaTomEgpCCWVJTrnetKIaG5urCqIDazSZnm7J/ruBCWT7bAUAm1ZUod3nxT1r6uBgS3aiksB/yVR0RFHAE9tb8HTnaQyFU6iym2GVRKRUDRNxBQ6rhCe2txR18aqm6QgnMyFEzbU9ZJGIgmDY9l05reE3F0fR7Q/iNxdGoajZP9c1HkemJXubB3UOa55HSURLjWGEitK21jo89/CG6T4joclW5r5GZ1H3GclnCDGKqun4/eUJdPuD6Dk/jFgqexFuo9s23ZL9ltrCKaQlosXHMEJFa1trHbasri2JDqyqpiOcUBBOlmYI0XUdfcEouvxBvHY2iJFo9uJid4UZO9bVo8PnwccaXdwJQ1QmyjaMsI14aRBFoai376bSU2fHqEtSE2K0wVAisxOmN4gPRuNZr7GZRNzdmmnJfuct1TDl2CVFRKWrLMMI24iTkXRdR0xWEU4oN23bXoxCcQVHzgXR5Q/izNVw1mtEAbhzsiX73S11qLCwJTvRUhIEAZIgwCQJMIkCJFGASRRhkjL/P1erhHwpuzDCNuJkFEXVEEmmESnBpZiEouJY3wi6/EGc/GA85+f3sUYX2n0e7FhXj+oS2npNZCRBuDZgXB80TKJQ8DOOZRVG2EacjJBUppZiFq9HSCFQNR0nPxhDtz+IN/pGkFSyN2JbUWNHu8+DXW0eLKuqyPMoiYpb1qAhiTM+VuhBYzbKKoywjTjlU0JWMZGQkZBLZylG13X0DkVw6L0AjpwdxkRCyXpdbaUFu9o8aPd5sMbjYCEqURZTQePDGQxxRugwS5n3y0FZhZFybiNO+SGnNcRSaURTaSjqwlq2F5JLY3EcnixEvTKRyHpNpUXCPWsyO2E2NleVzQ9RolwEQYDFJMIsfRg0zB8JHpRRVmGkHNuI09JLpVXEU2rJBZDRaAqHzw6j2x/AuUDuluyfWFWLdp8HW1fXsiU7lS1JzAQPq0mCxSTCIon89zAHZRVGyqmNOC2tpKIiLquIlVgAiaXSeP38CLr9AbwzMIFsdagCgI3NbrS3eXHv2jo4bea8j5PIKGZJnNyRMjnjIYmwmkSYS6Buw0hlFUbKoY04LZ1UWkU0mUZcVksqgMhpDW++P4YufxDHL4zmPBG4pb4S7T4v2ts8qHeyJTuVFkmcuXwiXbc7pTQKRQtVWYURoHTbiNPSKNUaEE3XcfpyCF3+II6eH0YkmX2nj9dlRXubB+0+L1bVsSU7FR9JFCAKHxaJSsJk4JBmBg0WWRur7MIIUFptxGnxqZqO6GQASZVYU7L+4Si6/UEc7g0iGEllvcZlM2H7unp0tHmxfpnLkBN8ia41tetEFAWIAiAJmfAgCpmwce3/F4XM29TMBhWHsgwjwOzbiLNtfPmIy2mEE2kklNJqzR4IJ6dbsl8ciWW9xmoSsa0lU4h618oarn9T3lw7S2ESr5+1kASBP3PLQNmGkdlg2/jSp2k6Iqk0wgmlpJZhwgkFPeeG0eUP4vSVUNZrRAG4Y0U1OnwefHJNHewW/jigxSUIma2s5skmXdcFD4ZemsSfPjmwbXxpSyqZrbjRZBpaicyCpBQVxy+MossfxImLY0jnaMne1uBEh8+DHes8qKnkNnZauKkdJmZJhFkUYTZN/n+GDZolhpEs2Da+NE3NgkSSSs4dI8VG1XS8c2kc3b1BvH5+BPEc3V6XV1dMFqJ6sLzanudRUjHLVux5bXdQFn/SYmAYyYJt40uDpulIpTUkFBVJRUUqrZVELYiu6zgXiKLLH8BrZ4cxFsveMbjabsbONg86fB6s8zr5wKAZpoo8c7Ui5zIK5RPDSBZsG1+8dP3DnTBJpTTCx5Qr4wl09wbQ5Q/i8nj2luwVZgn3rKlDu8+DO1ZUczdBGRMFAebJVuQWSbzucDWGUyokDCNZsG28MRayc6kUa0AAYCwm48jZILr8QfQORbJeI4kCNq+sQYfPg60ttbCZr/87S6XPLImosEiwmSXYTCJnNaioMIxkwbbx+TefnUuapiMqpxFJllY/kLicxht9o+j2B/DWB+NZW7IDwIZlLrT7vNi+th7uCrZkLxemyQLRqXbkZkmEzSxxFoyKGsNIFmwbn19z3bmUSqsIxRW8c2kCEwkZbpsFrd7Kom7OpagaTr4/ji5/AMf6R5HKUWC7staODp8Xu9o8aHDb8jxKyoepBl+Z3SiZug3zNWehcHmFShHDSA5sG58fs925tHllDWKKikhSwW8vjOLgiQEMjMagTH5fmmsr8cjmZmxaUW3wZzR7mq7jzJUwunoD6Dk7jHCOluz1DivafZmdMKvrKvkwKiFTR8xXmCXYzOJ0bQdRuWEYuQG2jV96N9u55Kow4dxQBN29QazxOvDOpXG8cOgc4rIKl80MlyRAUXVcGI7ihUPnsPe+tQUfSC6OxNDtD6C7N4hAOHtLdofVhO1r69Hh82DDcndRz/rQhwRBgNWUWVaZCiAMl0QMIzc127bxND/Zdi5pug5N06HpgAhA1jRMJGRouo6DJwYQl1XUOSwQkPkhbjUJqHNYMBKVcfDEADY2VxXcw3s4ksLh3iC6/AH0D2dvyW6WBGxtqUVHmxebV9XAYuJvyIVOEjMzG9MnvQoCRDGzk0VHpsYs87+ARRIZPohyYBhZRDzHZu6mdi4l0yoskghNx4ztuLKqwywIcNss6AvEMDAag8tmng4iUwQIcNrMGBiNoS8Qw9oGR74/letEk2n0nBtGd28AvxsIIVsdqigAm5qrsMvnxT1r6uCw8p9koZqa1bCaRFjNEqwmdhglWiz8ybdIeI7N3Oi6joSiwuO0oqmqAv3D0RmzHQCgQ0ckqWB1vQOt3kq89cE4FE2HS8oe8CySgIiuI5Q0rv+LnNbwm8mW7L+9OApFzb4VZq3XgXafFzvX1aPOYc3zKOlmps5UsZokWM2TAcTELdNES4VhZBGU6jk2iz3To2o64nIacVlFXP7wZNzPb27GC4fOYSQqw2kzwyIJkNVMELFbJDyyuRni5OyIWczUiFhN14/j2lmUfFI1Hb+7PIFufxBHzw8jlsq+zbjRbUOHz4P2Ni9W1LIleyExS+Jk6JCmZz+4nEKUPwwjC1Sq59gs5kxPQs7sgoldE0CutWlFNfbet3Z6h0xEz4SK1fWOGTtkWr2VaK6txIVZzKIsNV3XcT4YRbc/iMNngxiNZp+NqaowY8e6enT4vPA1siW7kaban08f6iZmAohFEovq3yZRKZpzGDl69Ci+9a1v4a233sLg4CA6Ozuxe/fuG96TSqXw13/91/inf/onDA0NobGxEc888wwef/zx+Y67YJTiOTaLMdOjajqiyTTCSQWKevND6TatqMbG5ir0BWIIJbP3DhEFAY/MchZlqVydSKC7N4hufxCXxuJZr7GZRXyyNdOS/eMrqrlV0yAmUYTNMrVtVmJ9B1EBm3MYicVi2LhxIx5//HF8+tOfntU9n/3sZxEIBPCP//iPaG1txeDgIDStNE5NLbVzbBYy0yOntellmOQ8OqKKgnDTwtPZzqIspom4jCNnh9HlD+K9wXCOsQN3TbZk39Zahwq2ZF8y154WO3XAmzC5g0USBAgCpmdAiKg4zDmMPPjgg3jwwQdnff2vfvUr9PT04MKFC6ipqQEArFy5cq4vW7BK7Rybucz03LrMhaTyYQCZzQzIYpjNLMpCJRQVx/pG0OUP4s33x3K2ZF/f5EJ7mwc71tWjqki+x8VGEATYzCLsFhMqLRJnmohK0JLXjPyf//N/cOedd+Kb3/wm/tf/+l+orKzEH/7hH+K//Jf/goqKiqz3pFIppFIfNoMKh7P/NloISu0cm5vN9FhEAeOqhgsjUThtJsMOpZvNLMpcqZqOkx+ModsfxBt9I0gq2cPVLTX26Y6oje7sf4dp7qa6kZqnZjwm26DbTBJrOohK3JKHkQsXLuCNN96AzWZDZ2cnRkZG8O///b/H6OgoXnzxxaz37N+/H88+++xSD21RlNo5NtlmenQ904BM03XEZRUSAKsklcTpuLquwz8YQZc/gCNnhzGRULJeV+uwYNc6Dzp8HrR6HCxEXQRTO1hsZolbZ4nKnKBn294w25sF4aYFrPfffz9ef/11DA0Nwe3OFHD+9Kc/xWc+8xnEYrGssyPZZkaam5sRCoXgchXmDMOM3SeT56UUY58RTdPx2Isn8N7VMDxOK3RkQgj0zI6VkaiM1fUOfOP/2lBwXU7n4tJYfLol+9WJZNZrKq0S7l1Tj3afBxuXV/FU1AWaOl22wsIj7onKRTgchtvtvunze8lnRhobG7Fs2bLpIAIAPp8Puq7j8uXLWLNmzXX3WK1WWK3F1Qhqsc+xMaKbayqtIilr+Oydy/H8v/RiKJw0ZMfKUhmJpvBabxDdvUGcC0SzXmOWBGxZXYt2nwdbVtWyJfsCCIKAisnwYbdwNwsR5bbkYeTuu+/Gj3/8Y0SjUTgcmTX+c+fOQRRFLF++fKlfPq8W6xybfHVzTasa4oqKpKwioahQJ6s01ze5875jZalEU2m8fn4E3f4ATg1MZC1EFQBsbHajvc2Le9fWwWkz532cpcAsZZqFWSbfKswSl7OIaFbmvEwTjUbR19cHANi0aRNeeOEF7Ny5EzU1NVixYgX27duHK1eu4Ac/+MH09T6fD1u2bMGzzz6LkZERfPGLX8T27dvxD//wD7N6zdlO85SCXD0+xifrTxbazVXVdERTacRS6Ztuv9V0fUl3rCwVOa3hxMUxdPUGcLw/d0v2lvpKtPu8aG/zoN5ZXDNxRpsqNq2YPH3WamLjMCK63pIt05w8eRI7d+6cfn/v3r0AgMceewwvvfQSBgcHcenSpek/dzgcOHToEL785S/jzjvvRG1tLT772c/i61//+lxfuuRd2+PDbTNDUTXoOmCziGhwWefVzVXTdCTTKpKKhqQyt/4fS7FjZalouo7Tl0Po8gfRc24Y0VQ663UNLhvafR7savNgVd3Sd2otJSZRhN2aWXLhDhciWkwLKmDNl3KZGTl9OYQvvHgCcVlFWssEEUEArCYJ9U4rJFFAPJXG9/+fO3MuB6majqSSWXZJKirkdGk0l8tG13VcGI6hyx/A4d5hDEdTWa9z2UzYsc6D9jYPbl3m4tLBHFjNEuxTRads5EZEc1QwBaw0e2/0DWNislOrScp0ldR1IKmouDKeQGOVDcpHurlOhY+pAGJU+Mjnks5QOInD/iC6/AG8P5q9JbvVJGJbSy06fF7cubKaxZOzNLX0YptcfuHsBxHlA8NIgdA0Ha+eCUAHYJKE6Qe5IACCBKRVHYFQEtV2M2wmESPRFBJ57Hp6I+9cGp8udp3a1txcW7moxa6hhIKec8Po9gdw+kruluwfv6Ua7T4vPtlaC7uFf71vxCROnVSb2XLLA+OIyCj8aV0gzlwNIxBKwGaSkFI1CII2YzlBgI5UWofbbkGtw4JwjuZc+fbOpXG8cOgc4rIKl80MlyRAUXVcGI7ihUPnsPe+tfMOJElFxfH+0emW7OkcPdl9jc7Jluwe1FSyJXs2JjGzw8VqmgogEvumEFHBYBgpEMFoErKqo7rSjGA4BUXTIYmZbac6gKkNIfesqS+YHS2aruPgiQHEZRV1DgsEZMZlNQmoc1gwEpVx8MQANjZXzXrMqqbj7Uvj6PYH8fr5ESRyFNwur65Ah8+D9jYvllWzJfsUkyjCJGUOiZsKH1MHyxERFSqGEYNomo7EZJ1HQlahKDokIfMw8bptGIumIKsatMkiVrMkosIs4uMF1OOjLxDDwGgMLpt5OohMESDAaTNjYDSGvkDshrtydF3H2UAEXf4gXusNYjyefdanptKCnevq0eHzYq2XLdklMdNUzGbJLLFwmYWIihXDSB4lJ4NHXFGR+shv/K3eSjTXVuLCcBR1DguWVVcgpehQdQ2iICCSTKPF40Crt3C2o4aSMhRNh0vK/gC0SAIiuo5QUs7655fH4+jyB3G4N4jL44ms19gtEj7ZWocOnwebVlSX9W/4kijAdk1xKbvDElGpYBhZYklFRSyVRiyV2a6biygIeGRzM144dA4jUTnTht0kQFZFRJIKKq2F14bdbbPALGZqRKym68clq5nOrW7bh3UcYzEZr50NossfxNmhSNb/rkkUsHlVDTp8HmxdXQtrmW4plUQBVtNkU7HJA+WIiEoRw8gi0XUdiqpDUTUoqgY5rSGV1ua022XTiuqiasP+0dmca5dqdGTOslld70BTtRX/emYIXf4g3r40nrUlOwBsWOZGh8+De9fWw11Rfi3ZzZIIu0WC1cyD5IiovLDp2RylVQ2KqkOeDB3pawLIYimmNuzX7qa59lC9cEKGKAporrbDPxTJ2f9kVV0l2ts82OXzoMFly/PojWc1S6i0SLBbTFx2IaKSM9vnN8NIDlNBYyp0TM12qLl+rS9jU31GLo1EkVQ1pNM6ZE3P+bXyOK3Y1eZBh8+D1fXF0W5+sUwVnVZYMssvnP0golLGDqyzlEqrmeWUtIa0NjXLoaMIMlrBqLJbsNbrwMWRGCLJ7FtxnTYTtq+tR7vPgw3L3AU707PYBEGAdepAObZUJyLKquzDSDiRRiRZGA3EikkwnMThs5mOqP3DsazXWEwitq6uRYfPg7tW1pT8MoQgCDCJmdNszZIIm1nkgXJERLNQ9mGEZi+SVNBzbgSHewP43UAI2eaORCFTiNve5sE9a+pQaS3tv2JmSYTLZkaFRYJZEsq+9wkR0XyU9pOCFkxOazh+YRRd/gBOXByDomZfvlrndaLd58HOdfWodVjzPMr8EgUBlVYTnDYTl12IiBYBwwhdR9V0/G5gAl3+IF4/P4yYnL0OpKnKho42L3b5PFhRY8/zKPNLEATYLRIqrSZUWiTOgBARLSKGkTwq5C27uq7jfDCK7smOqKOx7F1TqyrM2DHZkt3X6Czph/JUALFbJFRaTKz9ICJaIgwjeTK1/XVgNAZF02EWBTTXVhrezOzKRAKH/UF0+QMYyNGS3WYWJ1uye/HxW0q7JbskCqiYDB/2EpwB0TQdZ66GMRaXUWO3YH2TiyGLiAzHMJIH1zYGc9nMcEmZFuoXhqN44dA57L1vbV4DyURcxmuTO2HeG8zekl0SBdy1shrtbV5sa61FRQnWRkxtu7WaRFjNk4fNlfCOn2N9IzjQ04/+YBSKqsMsCWjxOPDE9hZsa60zenhEVMYYRpaYpus4eGIAcVmd0TLdahJQ57BgJCrj4IkBbGyuWtIlm4Si4td9I+jyB3Hy/bGcLdnXN7nQ4fNgx1oP3PbFa8leKEtU4jW1H6U485HLsb4RPN15GtFUGtV2CyySCFnV4B+M4OnO03ju4Q0MJERkGIaRJdYXiGFgNAaXzTzj7BYAECDAaTNjYDSGvkAMaxsWtxtpWtVw8oNxdPuD+HXfCJI5WrLfUmtHh8+DXW0eNLorFnUMgPFLVIIgoNIiwWEzocJcPgFkiqbpONDTj2gqjQaXbfrzt4kSGlwihsIpHOjpx5bVtVyyISJDMIwssVBShqLpcEnZf8hbJAERXUcomb1gdK50Xcd7g2F0+4M4cnYYE4nsDd3qHJbJluxetNRXLtkD2sglKqtZgsNqgsNqKuk6l5s5czWM/mAU1XbLdd9nQRBQZTejPxjFmathbFjuNmiURFTOGEaWmNtmgVnMPICtpusfiLKaOZnXbbMs6HUujcbR1RtAtz+IwVAy6zWVVgn3rqlHh8+D25ZXLfkD2oglKkkUMgHEZoLVVHp1LvMxFpehqDosOc7BsUoiQpqOsfjiBGIiorliGFlird5KNNdW4sJwdMYDGQB06IgkFayud6DVWznn//ZINIXXeoPo8gdxPhjNeo1ZErBldS3afR5sWVWb1wLNfC5RVU7OgJRTHchs1dgtMEsCZFWDTbw+oKVUDWZRQI19YYGYiGi+GEaWmCgIeGRzM144dA4jURlOmxkWSYCsZoKI3SLhkc3Ns54ZiKbSeP3cMLp6gzh1aSJrS3YBwMbmKnT4PLh3TT0cNmO+zUu9RDU1C+KqMMPM029zWt/kQovHAf9gBA0ucUZY03UdE3EFvkYn1jfl50RsIqKPYhjJg00rqrH3vrXTRZwRPbM0s7reMasiTjmt4bcXx9DtD+D4hdGcLdlb6x1onyxErXca35J9qZaoLCYRrgoznFYTZ0FmQRQFPLG9BU93nsZQOIUquxlWSURK1TARV+CwSnhiewuLV4nIMAwjebJpRTU2NlfNenurpuv4/eUQuv1B9JwbRjSVznpdo9uGXW0etPs8WFk796WepbSYS1RWs4TKyS25nAWZu22tdXju4Q3TfUZCk7uafI1O9hkhIsMxjOSRKAg3rI3QdR0XhmPo8gdwuHcYw9FU1utcNhN2rssEkPVNroKdHVjoEpVJFOG0ZYpRGUAWbltrHbasrmUHViIqOAwjBWAonJxuyf7+aDzrNVaTiLtb69Dh8+DOW6phKpKH83yWqCosElw2M4tRl4AoCty+S0QFh2HEIKGEgp5zmZbsp6+Es14jCsCdt1Sj3efFJ1vrUGEpzq2qs1mimipGddrMJd2SnYiIrscwkkdJRcWx/lF0+QN48/1xqDl6svsanWhv82JnWz2qS2S7Za4lqgqLBKfNjErOghARlS2GkSWmajrevjSOLn8Qb5wfQUJRs163vLoCHT4P2tu8WFa9+C3ZC4lZmqwFsZqKZrmJiIiWDsPIEtB1Hb1DEXT7g3jtbBDj8ewt2WsqLdi5rh4dPi/Weh0lPTMgicJ0YzJbCZ4ATERE88cwsoguj8fR5Q/icG8Ql8cTWa+xWyTcs6YOHT4vbm9e+pbsRpJEAXZLJoAUa70LEREtPYaRBRqLyXjtbKYl+9mhSNZrJEHArctc+MPbm7BtdS2sJT4zwN0wREQ0Fwwj8xCX03j9/Ai6/EG8c2kcOepQ4bCaIECHKAoYnEjgl6eHUFVhXrJTao0kCgIcNhNc3A1DNGuaprPvCxEYRmZNUTWcuDiGbn8Qxy6MQk5rWa9bXV8JX4MTJz8Yh5zW4LJlDilTVB0XhqN44dA57L1vbckEEptZgsNmgsNi4g9Rojk41jcy3RFXUXWYJQEtHgc74lJZYhi5AU3X8e6VD1uyh5PZW7J7nFa0+zxob/NgZV0lvvr/noac1ma0QLeaBNQ5LBiJyjh4YgAbm6tmfTheoTGJYiaAWE2cBSGah2N9I3i68zSiqTSq7RZYJBGyqsE/GMHTnafx3MMbGEiorDCMZHFxJNOSvdsfRDCSvSW702bCjrX1aPd5cOsy93SwODcUxcBoDC6becZZLAAgQIDTZsbAaAx9gdgNW8MXGkEQUGnJzILYLfxrQzRfmqbjQE8/oqk0Gly26boqmyihwSViKJzCgZ5+bFldy9lGKht8qkwKhpM43BtEV28QF4ZjWa+xmERsW12Ldp8Hm1fVZD0vJZSUoWg6XFL2HyIWSUBE1xFKyos6/qViM0vTW3JLeecPUb6cuRpGfzCKarvlugJvQRBQZTejPxjFmathtu6nslHWYSQUV9D5zhX84vdX8fvLIWSrQxWFTDvz9jYP7llTh0rrjb9kbpsFZjFTI2I1Xf/wltXM2SxuW+F2Vp0KIJUWiU3JiBbZWFyGouqw5Pi3ZZVEhDQdY/Hi+IWFaDGUbRj50ZsD+KtX3oWsZi9EXed1ot3nwc519ah1WGf93231VqK5thIXhqMzakYAQEfmtNrV9Q60eisX/DksJknMLCE5eUIu0ZKqsWeK2mVVg028fpt/StVgFgXUlMhREESzMeenztGjR/HQQw+hqakJgiDglVdemfW9v/71r2EymXD77bfP9WUXna/RdV0Qaaqy4dEtt+ClP7kLB/7vO/CZjy+fUxABMltcH9ncDLtFwkhURjKtQdN1JNMaRqIy7BYJj2xuLpjiVatZQr3TihU1dtRUWhhEiJbY+iYXWjwOjMcV6PrM+Vhd1zERV9DicWB9k8ugERLl35xnRmKxGDZu3IjHH38cn/70p2d938TEBB599FG0t7cjEAjM9WUX3a3LXFhdX4mJuILta+vR4fOgrcG5KE26Nq2oxt771uLgiQEMjMYQ0TNLM6vrHXhkc7Ph23oFQUClNdOYjK3ZifJLFAU8sb0FT3eexlA4hSq7GVZJRErVMBFX4LBKeGJ7C4tXqawI+kej+VxuFgR0dnZi9+7dN732j//4j7FmzRpIkoRXXnkFp06dmvXrhMNhuN1uhEIhuFyL99vCwFgcJklAQs5+eN1CabqOvkAMoaQMt82CVm+loTMiJlGEq8IEp81cVsWobCxFhWhGnxFNh1lknxEqPbN9fuelZuTFF1/EhQsX8E//9E/4+te/ftPrU6kUUqkPt9SGw+ElGVdzjR3DkRSApQkjoiAUxPZdsySiym7OdIQtkOWhfGFjKSpU21rrsGV1LYMyEfIQRs6fP4+//Mu/xOuvvw6TaXYvt3//fjz77LNLPLLSZzVLqKow33QHUKliYykqdKIocPsuEeZRwDoXqqrikUcewbPPPou1a9fO+r59+/YhFApNvw0MDCzhKEuPxSSiwW3DsqqKsg0iH20sZTNLEEUBNrOEBpcV0ZSKAz390HIdLERERHmzpE+qSCSCkydP4p133sGTTz4JANA0Dbquw2Qy4V//9V+xa9eu6+6zWq2wWue2i2WhCq2+Yz5MoojqSjOcNrPRQzEcG0sRERWPJQ0jLpcLp0+fnvGx733vezh8+DB+8pOfYNWqVUv58rP2zqXx6Z0vU4VkzbWVBbHzZTZMogh3hRmuivKrCcmFjaWIiIrHnMNINBpFX1/f9PsXL17EqVOnUFNTgxUrVmDfvn24cuUKfvCDH0AURdx6660z7vd4PLDZbNd93CgnL47hhUPnEJdVuGxmuIrohF2rWYK7woxKi8QQ8hFsLEVEVDzmXDNy8uRJbNq0CZs2bQIA7N27F5s2bcIzzzwDABgcHMSlS5cWd5RLRNN0vHT8fcRlFXUOC6wmEaIgwGoSUeewIC6rOHhiANr8dz8vCYfVhKaqCiyrqijLHTKzwcZSRETFY0F9RvJlqfqMnL4cwhf/55uwmSVYTdfnsmRaQ1JO46//aIPhW3QFQYDDakKV3cwuqbP04W4aNWtjKe6mISJaWrN9fpf1U20sLiM92XsiG4skQDH4hF1JFFBlt2BFjR31TiuDyBxsa63Dcw9vgK/RiXgqjWA0hXgqDV+jk0GEiKiAlOe+z0k1dgtMUmGesGuWRLgqzHBaTWyCtABsLEVEVPjKOoysb3JhZV0lzgUiBXPCbrk3KlsKbCxFRFTYynrOXxQFfGHryoI4Yddu+bAoNV9BRNN0nL4cQs+5YZy+HGIDMCIiMkTZ//p956oaw07YnTo9111hhtWU39NzeWYLEREVirLeTQMAw5EUIkklrx1Yjd4Zk+vMlnHuMiEiokVUUKf2FoN8nLArCAJcNhPcFWaYDNoV89EzW6Z6lNhECQ0uEUPhFA709GPL6loWeRIRUV6Udc1IvkiigJrKzPbcWofVsCACzO3MFiIionzgzMgSMksi3PbM9lyju6Rqmo4zV8PoORdEQsk0AcuGZ7YQEVG+MYwsAbMkoqbSUjDbc68tVo3LKiJJBUlFQ4PbBsdHxsgzW4iIKN8K42lZIsySiOpKy3UPeCN9tFi1qsKM99MaEoqKy2NxLK+xT4936swWX6OTZ7YQEVHesGZkEUiigDqnFc3XPNgLwUeLVW1mCZIkwuu2wSwJSGs6hkJJqFomnAyFU3BYJTyxvYXFq0RElDeF8+QsUg6bCbWVVkgF+PDOVazqsJqwrNqOoVASclrFYCiJCrMEX6OTfUaIiCjvGEbmySyJqHNYUWHJb7OyuRiLy1BUHZYsu3ccVhNW1dpxNZzEn35yFbav9fDMFiIiMgTDyByJk9tf3RVmw3fI3EyN3QKzJEBWNdjE60OTrOmwmyVsX+vh2S1ERGQY1ozMkiAIcFWY0VxjR1WWHh2FaH2TCy0eB8bjCj7aaHeqWLXF42CxKhERGYphZBYqrSYsq6pAnaMwa0NyEUUBT2xvgcMqYSicQkJRoWk6i1WJiKigMIzcQIVFQlNVBbwuGyym4vxSbWutw3MPb4Cv0Yl4Ko1gNIV4Kg1fo5Nn0BARUUFgzUgWVrOEGruloItT52Jbax22rK7FmathjMVl1NgtLFYlIqKCwTByjULrnLqYRFFgkSoRERWk0nvqzoMoCKi2W+CqMP4MGSIionJT9mGk0iqhptJSVIWpREREpaTsw4jdUvZfAiIiIkMV5xYRIiIiKhkMI0RERGQohhEiIiIyFMMIERERGYphhIiIiAzFMEJERESGYhghIiIiQzGMEBERkaEYRoiIiMhQDCNERERkKIYRIiIiMhTDCBERERmKYYSIiIgMxTBCREREhmIYISIiIkMxjBAREZGhGEaIiIjIUAwjREREZKg5h5GjR4/ioYceQlNTEwRBwCuvvHLD63/605/ivvvuQ319PVwuF7Zu3YpXX311vuMlIiKiEjPnMBKLxbBx40Z897vfndX1R48exX333Ydf/vKXeOutt7Bz50489NBDeOedd+Y8WCIiIio9gq7r+rxvFgR0dnZi9+7dc7pv/fr1+NznPodnnnlmVteHw2G43W6EQiG4XK55jLRwaJqOM1fDGIvLqLFbsL7JBVEUjB4WERHRopvt89uUxzEBADRNQyQSQU1NTb5f2nDH+kZwoKcf/cEoFFWHWRLQ4nHgie0t2NZaZ/TwiIiIDJH3AtZvf/vbiEaj+OxnP5vzmlQqhXA4POOt2B3rG8HTnafhHwyj0mqCx2lFpdUE/2AET3eexrG+EaOHSEREZIi8hpGDBw/i2WefxY9+9CN4PJ6c1+3fvx9ut3v6rbm5OY+jXHyapuNATz+iqTQaXDbYzBJEUYDNLKHBZUU0peJATz80bd4rZkREREUrb2Hk5Zdfxhe/+EX86Ec/QkdHxw2v3bdvH0Kh0PTbwMBAnka5NM5cDaM/GEW13QJBmFkfIggCquxm9AejOHO1+GeAiIiI5iovNSP/+3//bzz++ON4+eWX8alPfeqm11utVlit1jyMLD/G4jIUVYdFyp79rJKIkKZjLC7neWRERETGm3MYiUaj6Ovrm37/4sWLOHXqFGpqarBixQrs27cPV65cwQ9+8AMAmaWZxx57DH/3d3+HT3ziExgaGgIAVFRUwO12L9KnUdhq7BaYJQGyqsEmStf9eUrVYBYF1NgtBoyOiIjIWHNepjl58iQ2bdqETZs2AQD27t2LTZs2TW/THRwcxKVLl6av//u//3uk02ns2bMHjY2N029f+cpXFulTKHzrm1xo8TgwHlfw0Z3Uuq5jIq6gxePA+qbi3rZMREQ0HwvqM5IvpdBnZGo3TTSlospuhlUSkVI1TMQVOKwSnnt4A7f3EhFRSZnt85tn0+TJttY6PPfwBvganYin0ghGU4in0vA1OhlEiIiorOW96Vk529Zahy2ra9mBlYiI6BoMI3kmigI2LC+Pwl0iIqLZ4DINERERGYphhIiIiAzFMEJERESGYhghIiIiQzGMEBERkaEYRoiIiMhQDCNERERkKIYRIiIiMhTDCBERERmKYYSIiIgMxTBCREREhmIYISIiIkMxjBAREZGhGEaIiIjIUAwjREREZCiGESIiIjIUwwgREREZimGEiIiIDMUwQkRERIZiGCEiIiJDMYwQERGRoRhGiIiIyFAMI0RERGQok9EDKDSapuPM1TDG4jJq7Basb3JBFAWjh0VERFSyGEaucaxvBAd6+tEfjEJRdZglAS0eB57Y3oJtrXVGD4+IiKgkcZlm0rG+ETzdeRr+wTAqrSZ4nFZUWk3wD0bwdOdpHOsbMXqIREREJYlhBJmlmQM9/Yim0mhw2WAzSxBFATazhAaXFdGUigM9/dA03eihEhERlRyGEQBnrobRH4yi2m6BIMysDxEEAVV2M/qDUZy5GjZohNlpmo7Tl0PoOTeM05dDDEtERFSUWDMCYCwuQ1F1WKTs2cwqiQhpOsbicp5HlhvrW4iIqFRwZgRAjd0CsyRAVrWsf55SNZhFATV2S55Hlh3rW4iIqJQwjABY3+RCi8eB8bgCXZ+51KHrOibiClo8DqxvcuVtTLmWYFjfQkREpYbLNABEUcAT21vwdOdpDIVTqLKbYZVEpFQNE3EFDquEJ7a35K3fyI2WYJw286zrWzYsd+dlvERERAvBmZFJ21rr8NzDG+BrdCKeSiMYTSGeSsPX6MRzD2/IWx3GzZZg3ugbuWl9i1Jg9S1EREQ3wpmRa2xrrcOW1bWGdWD96BLM1MyHTZTQ4BIxFE7h1TNDMImArGqwidJ1/41Cq28hIiK6GYaRjxBFwbDljdlsMQ6Gk/C6K3B5PIEGlzjjuqn6Fl+jM6/1LURERAvBZZoCMpstxoqm44H1XjisEobCKSQUFZqmI6GoGAqn8l7fQkREtFCcGSkg124xvtESzCdb67FxedV0kWtI02EWBfganewzQkRERYdhpIBMbTH2D0ZuugQjioKh9S1ERESLhcs0BWRqi/Fsl2Cm6lu2r63HhuVuBhEiIipKcw4jR48exUMPPYSmpiYIgoBXXnnlpvccOXIEd9xxB6xWK1pbW/HSSy/NY6jloVC2GBMREeXLnJdpYrEYNm7ciMcffxyf/vSnb3r9xYsX8alPfQpf+tKX8MMf/hDd3d344he/iMbGRjzwwAPzGnSpM3qLMRERUT4J+kf7n8/lZkFAZ2cndu/enfOar371q/jFL36Bd999d/pjf/zHf4yJiQn86le/mtXrhMNhuN1uhEIhuFzcskpERFQMZvv8XvKakePHj6Ojo2PGxx544AEcP3485z2pVArhcHjGGxEREZWmJQ8jQ0ND8Hq9Mz7m9XoRDoeRSCSy3rN//3643e7pt+bm5qUeJhERERmkIHfT7Nu3D6FQaPptYGDA6CERERHRElnyPiMNDQ0IBAIzPhYIBOByuVBRUZH1HqvVCqvVutRDIyIiogKw5DMjW7duRXd394yPHTp0CFu3bl3qlyYiIqIiMOcwEo1GcerUKZw6dQpAZuvuqVOncOnSJQCZJZZHH310+vovfelLuHDhAv7Tf/pP6O3txfe+9z386Ec/wp//+Z8vzmdARERERW3OYeTkyZPYtGkTNm3aBADYu3cvNm3ahGeeeQYAMDg4OB1MAGDVqlX4xS9+gUOHDmHjxo34m7/5G/yP//E/2GOEiIiIACywz0i+sM8IERFR8SmYPiNEREREN1IUp/ZOTd6w+RkREVHxmHpu32wRpijCSCQSAQA2PyMiIipCkUgEbrc7558XRc2Ipmm4evUqnE4nBGH2h8WFw2E0NzdjYGCAtSYFht+bwsTvS2Hi96Vw8XtzY7quIxKJoKmpCaKYuzKkKGZGRFHE8uXL532/y+XiX5ICxe9NYeL3pTDx+1K4+L3J7UYzIlNYwEpERESGYhghIiIiQ5V0GLFarfja177Gc24KEL83hYnfl8LE70vh4vdmcRRFASsRERGVrpKeGSEiIqLCxzBCREREhmIYISIiIkMxjBAREZGhSjqMfPe738XKlSths9nwiU98AidOnDB6SGXv6NGjeOihh9DU1ARBEPDKK68YPSQCsH//ftx1111wOp3weDzYvXs3zp49a/Swyt6BAwdw2223TTfU2rp1K/7lX/7F6GHRRzz//PMQBAFPPfWU0UMpWiUbRv75n/8Ze/fuxde+9jW8/fbb2LhxIx544AEEg0Gjh1bWYrEYNm7ciO9+97tGD4Wu0dPTgz179uA3v/kNDh06BEVRcP/99yMWixk9tLK2fPlyPP/883jrrbdw8uRJ7Nq1C3/0R3+EM2fOGD00mvTmm2/i+9//Pm677Tajh1LUSnZr7yc+8Qncdddd+M53vgMgc75Nc3MzvvzlL+Mv//IvDR4dAYAgCOjs7MTu3buNHgp9xPDwMDweD3p6enDvvfcaPRy6Rk1NDb71rW/hT//0T40eStmLRqO444478L3vfQ9f//rXcfvtt+Nv//ZvjR5WUSrJmRFZlvHWW2+ho6Nj+mOiKKKjowPHjx83cGRExSEUCgHIPPioMKiqipdffhmxWAxbt241ejgEYM+ePfjUpz4141lD81MUB+XN1cjICFRVhdfrnfFxr9eL3t5eg0ZFVBw0TcNTTz2Fu+++G7feeqvRwyl7p0+fxtatW5FMJuFwONDZ2YmPfexjRg+r7L388st4++238eabbxo9lJJQkmGEiOZvz549ePfdd/HGG28YPRQCsG7dOpw6dQqhUAg/+clP8Nhjj6Gnp4eBxEADAwP4yle+gkOHDsFmsxk9nJJQkmGkrq4OkiQhEAjM+HggEEBDQ4NBoyIqfE8++SR+/vOf4+jRo1i+fLnRwyEAFosFra2tAICPf/zjePPNN/F3f/d3+P73v2/wyMrXW2+9hWAwiDvuuGP6Y6qq4ujRo/jOd76DVCoFSZIMHGHxKcmaEYvFgo9//OPo7u6e/pimaeju7uZaK1EWuq7jySefRGdnJw4fPoxVq1YZPSTKQdM0pFIpo4dR1trb23H69GmcOnVq+u3OO+/Ev/23/xanTp1iEJmHkpwZAYC9e/fisccew5133onNmzfjb//2bxGLxfAnf/InRg+trEWjUfT19U2/f/HiRZw6dQo1NTVYsWKFgSMrb3v27MHBgwfxs5/9DE6nE0NDQwAAt9uNiooKg0dXvvbt24cHH3wQK1asQCQSwcGDB3HkyBG8+uqrRg+trDmdzuvqqSorK1FbW8s6q3kq2TDyuc99DsPDw3jmmWcwNDSE22+/Hb/61a+uK2ql/Dp58iR27tw5/f7evXsBAI899hheeuklg0ZFBw4cAADs2LFjxsdffPFFfOELX8j/gAgAEAwG8eijj2JwcBButxu33XYbXn31Vdx3331GD41oUZVsnxEiIiIqDiVZM0JERETFg2GEiIiIDMUwQkRERIZiGCEiIiJDMYwQERGRoRhGiIiIyFAMI0RERGQohhEiIiIyFMMIERERGYphhIiIiAzFMEJERESGYhghIiIiQ/3/MgijhKju7+UAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -3973,7 +5032,7 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 76,
"id": "7bb8d264",
"metadata": {
"hidden": true
@@ -3987,7 +5046,7 @@
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": 77,
"id": "1ddf4e63",
"metadata": {
"hidden": true
@@ -3995,7 +5054,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 1330x410 with 2 Axes>"
]
@@ -4009,26 +5068,26 @@
"\n",
"diagnostics.plot_influence(ax=axes[0])\n",
"axes[0].axhline(0, linestyle=':', linewidth=1)\n",
- "diagnostics.plot_index(threshold=0.02, ax=axes[1]);\n",
+ "diagnostics.plot_index(threshold=0.02, ax=axes[1])\n",
"axes[1].axhline(0, linestyle=':', linewidth=1);"
]
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": 78,
"id": "7a441682",
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
- "high_leverage_point = np.argmax(diagnostics.hat_matrix_diag)\n",
- "cooks_distant_point = np.argmax(diagnostics.cooks_distance[0])"
+ "high_leverage_point = np.argmax(diagnostics.hat_matrix_diag) # 20\n",
+ "cooks_distant_point = np.argmax(diagnostics.cooks_distance[0]) # 5"
]
},
{
"cell_type": "code",
- "execution_count": 70,
+ "execution_count": 79,
"id": "0d8019bc",
"metadata": {
"hidden": true
@@ -4036,7 +5095,7 @@
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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"text/plain": [
"<Figure size 1330x410 with 2 Axes>"
]
@@ -4046,17 +5105,7 @@
}
],
"source": [
- "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
- "for ax, influential_point in zip(axes, set([high_leverage_point, cooks_distant_point])):\n",
- " sns.scatterplot(x=x, y=y, ax=ax)\n",
- " sns.regplot(x=x, y=y, ax=ax, scatter=False, label=f'{influential_point:d} included')\n",
- " selection = np.ones(len(x), dtype=bool)\n",
- " selection[influential_point] = False\n",
- " sns.regplot(x=x[selection], y=y[selection], scatter=False, ax=ax, label=f'{influential_point:d} excluded')\n",
- " xi, yi = x[influential_point], y[influential_point]\n",
- " ax.plot(xi, yi, 'r.', markersize=14)\n",
- " ax.text(xi, yi, f'{influential_point:d}')\n",
- " ax.legend()"
+ "statsmodels_material.illustration_outlier(x, y, high_leverage_point, cooks_distant_point)"
]
},
{
@@ -4068,9 +5117,9 @@
"source": [
"The leverage for a point tells how much the model would change if we move the response value of that point, while Cook's distance reflects how much the model changes if we omit the point.\n",
"\n",
- "Therefore, Cook's distance is an «effect size» for outliers. Influential points that fall above $1$ are undesirable and should preferably be removed or trimmed (see also [robust linear models](https://www.statsmodels.org/stable/generated/statsmodels.robust.robust_linear_model.RLM.html)). A Cook's distance between $0.5$ and $1$ signals a point (=an observation) to be examined.\n",
+ "Therefore, Cook's distance is an “effect size†for outliers. Influential points that fall above $1$ are undesirable and should preferably be removed or trimmed (see also [robust linear models](https://www.statsmodels.org/stable/generated/statsmodels.robust.robust_linear_model.RLM.html)). A Cook's distance between $0.5$ and $1$ signals a point (=an observation) to be examined.\n",
"\n",
- "Note: compared to other implementations of influence plots, `statsmodels`' influence plot lacks the Cook's distance isocurves."
+ "Note: compared to other implementations of influence plots, statsmodels' influence plot lacks the Cook's distance isocurves."
]
},
{
@@ -4078,7 +5127,6 @@
"id": "57e0be6f",
"metadata": {
"heading_collapsed": true,
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@@ -4107,16 +5155,15 @@
},
{
"cell_type": "code",
- "execution_count": 71,
+ "execution_count": 80,
"id": "b5b96410",
"metadata": {
- "hidden": true,
- "scrolled": true
+ "hidden": true
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -4143,7 +5190,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 81,
"id": "709f2da0",
"metadata": {
"hidden": true
@@ -4151,7 +5198,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 1500x800 with 6 Axes>"
]
@@ -4161,18 +5208,7 @@
}
],
"source": [
- "_, axes = plt.subplots(2, 3, figsize=(15, 8))\n",
- "\n",
- "for ax, tr in zip(axes.T, (lambda x: 1./x, np.log, lambda x: x*x)):\n",
- " x = np.linspace(1, 50, 30)\n",
- " y = tr(x)\n",
- " scale = y.max() - y.min()\n",
- " y += .05 * scale * stats.norm.rvs(size=x.size)\n",
- " x_grid = np.linspace(1, 50, 100)\n",
- " ax[0].plot(x, y, 'b+')\n",
- " ax[0].plot(x_grid, tr(x_grid), 'r-')\n",
- " ax[1].plot(tr(x), y, 'b+')\n",
- " ax[1].plot(tr(x_grid), tr(x_grid), 'r-')"
+ "statsmodels_material.illustration_monotonous_functions()"
]
},
{
@@ -4182,7 +5218,7 @@
"hidden": true
},
"source": [
- "Generic standardization functions exist, such as the [Box-Cox transform](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.boxcox.html) in `scipy`, but they often require the explanatory variable to take positive values and the interpretation of the relationship becomes less straight-forward."
+ "Generic standardization functions exist, such as the [Box-Cox transform](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.boxcox.html) in scipy, but they often require the explanatory variable to take positive values and the interpretation of the relationship becomes less straight-forward."
]
},
{
@@ -4208,7 +5244,7 @@
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 82,
"id": "02debff3",
"metadata": {
"hidden": true
@@ -4216,7 +5252,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -4259,7 +5295,7 @@
},
{
"cell_type": "code",
- "execution_count": 74,
+ "execution_count": 83,
"id": "32746edb",
"metadata": {
"hidden": true
@@ -4267,7 +5303,7 @@
"outputs": [
{
"data": {
- "image/png": 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"text/plain": [
"<Figure size 1330x410 with 2 Axes>"
]
@@ -4305,7 +5341,7 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 84,
"id": "9a3edfab",
"metadata": {
"hidden": true
@@ -4381,7 +5417,7 @@
"4 2.486747 0.075106 0.005641"
]
},
- "execution_count": 75,
+ "execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
@@ -4393,14 +5429,14 @@
},
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": 85,
"id": "24c100a8",
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
- "poly2_model = ols('y ~ 1 + x + x2', augmented_df).fit()"
+ "poly2_model = smf.ols('y ~ 1 + x + x2', augmented_df).fit()"
]
},
{
@@ -4417,7 +5453,7 @@
},
{
"cell_type": "code",
- "execution_count": 77,
+ "execution_count": 86,
"id": "d6ac4ac8",
"metadata": {
"hidden": true
@@ -4430,7 +5466,7 @@
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 87,
"id": "078047e2",
"metadata": {
"hidden": true
@@ -4438,7 +5474,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -4448,15 +5484,7 @@
}
],
"source": [
- "x_grid = np.linspace(x.min(), x.max(), 100)\n",
- "X_grid = np.stack((np.ones_like(x_grid), x_grid, x_grid**2), axis=1)\n",
- "# y_grid = poly2_model.predict(X_grid) # bug?\n",
- "beta = poly2_model.params\n",
- "y_grid = np.dot(X_grid, beta)\n",
- "ax = sns.scatterplot(x='x', y='y', data=df, label='observations')\n",
- "ax.plot(x, y_th, 'r-', label='true')\n",
- "ax.plot(x_grid, y_grid, 'g-', label='predicted')\n",
- "ax.legend();"
+ "statsmodels_material.illustration_nonlinear_regression(df, y_th, poly2_model, 2)"
]
},
{
@@ -4468,7 +5496,7 @@
"source": [
"Polynomial models are flexible enough to closely approximate any function in the neighborhood of a point (think of Taylor series expansions) but, of course, may not be adequate enough as we are modelling a function across an entire domain.\n",
"\n",
- "It is also possible to introduce any other non-linear transformation of the explanatory variable as additional terms in the modelling equation or columns in the design matrix. See the following examples in `statsmodels` documentation: [1](https://www.statsmodels.org/stable/examples/notebooks/generated/predict.html) [2](https://www.statsmodels.org/stable/examples/notebooks/generated/ols.html)."
+ "It is also possible to introduce any other non-linear transformation of the explanatory variable as additional terms in the modelling equation or columns in the design matrix. See the following examples in statsmodels documentation: [1](https://www.statsmodels.org/stable/examples/notebooks/generated/predict.html) [2](https://www.statsmodels.org/stable/examples/notebooks/generated/ols.html)."
]
},
{
@@ -4494,7 +5522,7 @@
},
{
"cell_type": "code",
- "execution_count": 79,
+ "execution_count": 88,
"id": "fba76906",
"metadata": {
"hidden": true
@@ -4502,12 +5530,12 @@
"outputs": [],
"source": [
"augmented_again_df = augmented_df.assign(x3=x**3, x4=x**4, x5=x**5, x6=x**6) # yippee!\n",
- "poly6_model = ols('y ~ 1 + x + x2 + x3 + x4 + x5 + x6', augmented_again_df).fit()"
+ "poly6_model = smf.ols('y ~ 1 + x + x2 + x3 + x4 + x5 + x6', augmented_again_df).fit()"
]
},
{
"cell_type": "code",
- "execution_count": 80,
+ "execution_count": 89,
"id": "424e6080",
"metadata": {
"hidden": true
@@ -4515,7 +5543,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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XL5KQkJAtEEpJSaF+/fr6rJoQQggTuBObxIR1J/n74r0sxzOGihb0rZ+tVyXzNPM4h+2gScNJG4yzUhkAdyf7PF/T2DO2JKjJjZub2mNiqtd+Qu6P9fTY2dmhKEqWY6mpmT6s/7X1999/p0yZMlnKOTs7P3F9hBBCmM7N6EQuR8VnC2gy5DZUlDHNXEFLnP1mADzSOuvOuzs50DrYN0ticwZTzNiSoCY3Gk2BhoBMzcnJifT09HzLlSxZkjNnzmQ5duLECRwd1Q9sjRo1cHZ25urVqzLUJIQQViQjL6Zvk3J5lstpqChjmvmW8G2k2d1Co7jhnt4aUIMWHzdHs5qxJUGNhatQoQIHDx7k8uXLeHh4oNVqcyz39NNP8+mnn/LNN9/QrFkzvvvuO86cOaMbWvL09OStt97ijTfeQKvV0rJlS2JiYti7dy9eXl4MHGgeO7AKIYQonIy8mEHNK+RZLqehooxp5ruWTYRE8Eh/GjtcsgQt3m6woG99ouJSeJiUiqeLI74eptlZXIIaC/fWW28xcOBAatSoQWJiIitWrMixXMeOHZk8eTLvvPMOSUlJDBkyhFdeeYXTp0/rykybNo2SJUsyc+ZMLl26hI+PDw0aNODdd981VnOEEELoWUZezPFr0bSoXIK9OQxB5TVUlK6J4kbS3wAs7zWR+v51swUthp6xVVAa5fFECysWGxuLt7c3MTExeHl5ZTmXlJREREQEQUFBuLi4mKiGwhLIZ0UIYUnCI+No9/ku3Jzsmd+3Piv2RmQJbFoF+/JJzzq5roUzYdsEPtn3CW0qtOGvgX8Zq9pZ5HX9zkx6aoQQQggrlpEXszssijFrjjOkZRBDWgSRnKbFx9WRSn4elPLK+QtaXEocy44tA+CNp94wZrWLRFYUFkIIIaxYRl5M62BfElLSWbjzIkNXHeGHQ1cJ8nXPNaABWHViFdFJ0VQuXplnqzxrxFoXjfTUCCGEEFYuY/uFwiTzahUt8w7OA2Bs07HYacy/H0SCGiGEEMIGFDaZ9/cLvxN2PwwfFx8G1RtUsAdptWBnuuDH/MMuIYQQQhjd3ANzARjeYDgeTgXYNujjj+HFFyHVuFsjZCY9NUIIIYTI4sTtE/x1+S/sNfa83uT1/B8wYwa8/776/19/hR49DFvBXEhPjRBCCCGy+OLAFwD0qtGLQO/AvAtPnw7vvaf+/+OPTRbQgAQ1QgghhMjkdtxt1pxZAxRgGve0aTB5svr/GTPAxIu1SlAjhBBCCJ1FhxaRkp5Cs7LNaFq2ae4FP/wQPvhA/f/MmTBpknEqmAezCWp2795Nt27dCAgIQKPRsHHjxiznFUXhgw8+wN/fH1dXV9q3b09YWJhpKmujKlSowBdffKG7n9P7ZAxTp06lXr16Rn9dIYSwdrHJsSw8vBCA8c3G515w6lT1BjBrFkycaPC6FYTZBDXx8fHUrVuXRYsW5Xj+k08+Yf78+SxZsoSDBw/i7u5Ox44dSUpKMnJNRYZbt27RuXPn/AsigYgQQliCxYcXE50UTTXfavSonktuzNSpai8NwCefwIQJRqtffsxm9lPnzp1zvUAqisIXX3zB+++/z/PPPw/AN998Q6lSpdi4cSMvvfRSjo9LTk4mOTlZdz82Nlb/FbcwKSkpODnpZ9Ox0qVL6+V5hBBCmF5iaiKfH/gcgIktJmZfbE9R1IDmo4/U+59+Cm+9ZdxK5sNsemryEhERwe3bt2nfvr3umLe3N02bNmX//v25Pm7mzJl4e3vrboGB+WRwW6A2bdowevRoRo8ejbe3N76+vkyePJmMfUorVKjAtGnTeOWVV/Dy8mLEiBEA7Nmzh1atWuHq6kpgYCBjxowhPj5e97yRkZF069YNV1dXgoKCWL16dbbXfnz46fr16/Tt25fixYvj7u5Oo0aNOHjwICtXruTDDz/k5MmTaDQaNBoNK1euBCA6Opphw4ZRsmRJvLy8ePrppzl58mSW15k1axalSpXC09OToUOHSu+cEEIYwFfHviIyPpIKPhXoV7tf1pOKAlOmPApo5swxu4AGzKinJi+3b98GoFSpUlmOlypVSncuJ5MmTWL8+EdjgrGxsQUObBRFISE1oQi1fXJujm5oNJoCl1+1ahVDhw7l0KFDHDlyhBEjRlCuXDmGDx8OwJw5c/jggw+YMmUKAOHh4XTq1Inp06ezfPly7t69qwuMVqxYAcCgQYO4efMmf/31F46OjowZM4bIyMhc6xAXF0dISAhlypThl19+oXTp0hw7dgytVkufPn04c+YMmzdvZvv27YAalAL07t0bV1dX/vzzT7y9vVm6dCnt2rXjwoULFC9enLVr1zJ16lQWLVpEy5Yt+fbbb5k/fz4VK1Ys0s9WCCFEdinpKXy671MA3mn+Do72jo9OKoo6xPSpep7PPoPxeeTbmJBFBDVF5ezsjLOzc5Eem5CagMfMAqygaABxk+Jwd3IvcPnAwEDmzp2LRqOhatWqnD59mrlz5+qCmqeffpo333xTV37YsGH079+fcePGARAcHMz8+fMJCQlh8eLFXL16lT///JNDhw7RuHFjAL7++muqV6+eax2+//577t69y+HDhylevDgAlStX1p338PDAwcEhy5DVnj17OHToEJGRkbr3ac6cOWzcuJF169YxYsQIvvjiC4YOHcrQoUMBmD59Otu3b5feGiGE0KPvTn3HtdhrlPYozeD6gx+d0Gph3DhYsEC9P28ejBljkjoWhEUMP2VcCO/cuZPl+J07dySvA3jqqaey9Ow0a9aMsLAw0tPTAWjUqFGW8idPnmTlypV4eHjobh07dkSr1RIREcG5c+dwcHCgYcOGusdUq1YNHx+fXOtw4sQJ6tevrwtoCuLkyZPExcVRokSJLHWJiIggPDwcgHPnztG0adYphc2aNSvwawghhMhbujadWXtmAfBmszdxcfhv126tFv73PzWg0Whg6VKzDmjAQnpqgoKCKF26NDt27NDNoImNjeXgwYO8+uqrBnlNN0c34ibFGeS5C/La+uTunrXXJy4ujpEjRzImhw9nuXLluHDhQqFfw9XVtdCPiYuLw9/fn9DQ0Gzn8gqghBBC6M+6f9YRdj+M4q7F+V+j/6kH09JgyBD49lt1g8rly2HgQNNWtADMJqiJi4vj4sWLuvsRERGcOHGC4sWLU65cOcaNG8f06dMJDg4mKCiIyZMnExAQQPfu3Q1SH41GU6ghIFM6ePBglvsHDhwgODgYe3v7HMs3aNCAf/75J8vwUGbVqlUjLS2No0eP6oafzp8/T3R0dK51qFOnDl999RX379/PsbfGyclJ13OUuR63b9/GwcGBChUq5Pi81atX5+DBg7zyyitZ2ieEELYqJiGFqLgUYpNS8XJ1xNc999238yurKAoz9swAYGzTserGlamp8PLLsHYt2NvD6tXQp49R2vakzCaoOXLkCG3bttXdz0jwHThwICtXruSdd94hPj6eESNGEB0dTcuWLdm8eTMuLi6mqrLZuHr1KuPHj2fkyJEcO3aMBQsW8Nlnn+VafsKECTz11FOMHj2aYcOG4e7uzj///MO2bdtYuHAhVatWpVOnTowcOZLFixfj4ODAuHHj8uyN6du3LzNmzKB79+7MnDkTf39/jh8/TkBAAM2aNaNChQq6QLVs2bJ4enrSvn17mjVrRvfu3fnkk0+oUqUKN2/e5Pfff+eFF16gUaNGjB07lkGDBtGoUSNatGjB6tWrOXv2rCQKCyFs0s3oRCasP8XfYVG6Y62DfZnVsw4BPq6FLrvh3w2cunMKDycPRjcZDcnJagCzaRM4OsKPP8ILLxincXpgNjk1bdq0QVGUbLeMqb8ajYaPPvqI27dvk5SUxPbt26lSpYppK20mXnnlFRITE2nSpAmjRo1i7NixuqnbOalTpw67du3iwoULtGrVivr16/PBBx8QEBCgK7NixQoCAgIICQmhR48ejBgxAj8/v1yf08nJia1bt+Ln50eXLl2oXbs2s2bN0vUW9ezZk06dOtG2bVtKlizJmjVr0Gg0/PHHH7Ru3ZrBgwdTpUoVXnrpJa5cuaKb6danTx8mT57MO++8Q8OGDbly5YrBhhyFEMKcxSSkZAtSAHaHRTFx/SliElIKVTZdm877O9Wdtcc1HUdxXKF7dzWgcXaGjRstKqAB0CgZC5rYgNjYWLy9vYmJicHLyyvLuaSkJCIiIggKCrKo3p82bdpQr169LNsXCMOy1M+KEMKyhUfG0e7zXbme3zE+hEp+HgUuu+/WBl7Z+ArFXIpxadhpfF58BXbuBDc3+OUXaNdO720oqryu35mZzfCTEEIIIXIXm5Sa5/mHmc7nV/Z+fDxTQtW1y95pOAaf5/vA3r3g6Qm//w6tWj15hU1AghohhBDCAni5OOZ53jPT+fzKbr78AxHREZRy8+P1D36D/UfB2xu2bIGmeezMbeYkqLFwOU2HFkIIYX18PZxoHezL7sfyZEBNAPb1cCpQ2eaVPVh87BMA3jvgiPv+o1CiBGzdCg0aGK4BRmA2icJCCCGEyJ23mxOzetahdbBvluOtg32Z3bNOlqnaeZUNrrifW3E3KRfvyIhNN8DPD0JDLT6gAempycaG8qZFEclnRAhhKgE+rizoW5+ouBQeJqXi6eKIr0fO69TkVNbJMYn6SzsDMGV7Ks6lAmDHDqhWzdhNMQgJav6TMfU4JSWlSKvjCtuRkKBudOromPeYtRBCGIK3W+6L7eVX9qOf3+Ve8gOqRMErD8rB7p1QqZKhqmp0EtT8x8HBATc3N+7evYujoyN2djIyJ7JSFIWEhAQiIyPx8fHJdcVmIYQwRzcPbueTYwvBET4674/D33uhbFlTV0uvJKj5j0ajwd/fn4iICK5cuWLq6ggz5uPjIxupCiEsy4EDvP95V+JrKjx1340Xvz0O/y1yak0kqMnEycmJ4OBgUlJS8i8sbJKjo6P00AghLMuOHRwb/iwrX1GvbXNHbkBjhQENSFCTjZ2dnawSK4QQwjr8/DNK35d4o18qigb6VX+Rp2o8Y+paGYwkjgghhBDWaPly6N2bDZVS2V0BXBxcmNnxU1PXyqAkqBFCCCGszZw5MHQoyRotb/f0BOCtZm9RzruciStmWBLUCCGEENZCUeDdd+HttwGYPyGESw4P8ffwZ0LLCSaunOFJUCOEEEJYg/R0ePVVmDkTgMiZ7zPd8zgAM9rNwMPJw5S1MwoJaoQQQghLl5wML70ES5eCnR0sXcpbVa8QmxxLA/8GvFL3FVPX0CgkqBFCCCEs2cOH0LUrrFsHTk6wdi1/dQjm21PfokHD4q6LsdPYxuVepnQLIYQQlioyEjp3hmPHwMMDNm4kOaQlry6pC8CrjV6lSZkmJq6k8UhQI4QQQliiiAh45hm4eBF8feHPP6FRIz7dPZ3z985Tyr0UH7f72NS1NCoJaoQQQghLc/IkdOoEt29D+fKwdStUqcLF+xeZvns6AHM7zsXHxce09TQyCWqEEELYlJiEFKLiUohNSsXL1RFf94Lvem0WQkPh+echNhZq14bNmyEgAEVRGPXHKJLTk+lQsQMv1XrJ1DU1OglqhBBC2Iyb0YlMWH+Kv8OidMdaB/syq2cdAnxcTVizAlq7FgYMgJQUaN0aNm0CHx/11Nm1bA3firO9M4u6LEKj0Zi2riZgG+nQQgghbF5MQkq2gAZgd1gUE9efIibBzDcznjdPnbadkgI9e8KWLbqAJiohijGbxwDwbqt3CS4RbMKKmo4ENUIIIWxCVFxKtoAmw+6wKKLizDSo0WrhnXdg3Dh1xeDRo+HHHyHT5suj/hhFZHwkNUvWZEIL6185ODcy/CSEEMImxCal5nn+YT7nTSIlBYYMgdWr1fszZ8KECZBpaGnt2bWsPbsWe409q7qvwtnB2USVNT0JaoQQQtgELxfHPM975nPe6GJjoVcv2LYNHBzg66/hlawrA9+Ju8Nrv78GwHut3qNhQENT1NRsyPCTEEIIm+Dr4UTrYN8cz7UO9sXXw4xmQN28qSYCb9sG7u7w66/ZAhpFURj520juJd6jXul6vNf6PRNV1nxIUCOEEMImeLs5MatnnWyBTetgX2b3rGM+07rPnoVmzdS1aEqVgl271DVpHrP69Go2nd+Eo50jq7qvwsneTOpvQjL8JIQQwmYE+LiyoG99ouJSeJiUiqeLI74eZrROzc6d0KMHxMRA1arqKsFBQdmK3Yi9wet/vg7AlJAp1ClVx9g1NUsS1AghhLAp3m5mFMRk9s03MGwYpKZCy5awcSOUKJGtWLo2nf4/9yc6KZrGAY2Z0NJ2Zzs9ToIaIYQQQk+KtFqxosBHH8HUqer9l16CFSuI0doRFRmX7bmm757Oriu78HDyYHWP1TjYyaU8g/wkhBBCCD0oyGrF2YIeR/AeO0rtpQGYNAmmT+dmbDIT1h/P9lzPN43ho90fAbCk6xKbXWQvNxpFURRTV8JYYmNj8fb2JiYmBi8vL1NXRwghhJWISUhh9JrjOS7u1zrYlwV96xOfkp4l6PFKiuOHLZ9S49+jYG8PixfD8OG5Plc6MdxzH0ei9i6D6g1ixfMrjNI2c1DQ67f01AghhBBPKL/ViqMTUnl/0xldmTIxkaz4aSpV7l0l0dkN7Q8/4N69W67PpaBwz+kLErV3qegTzMLOCw3bIAslU7qFEEKIJ5TfasXxKWm6QKX2rTA2fPsmVe5d5ZZHCXr0m8Xt5m3zfK6HDhtJtD8MiiMzQ5bj7uSu3wZYCYsJatLT05k8eTJBQUG4urpSqVIlpk2bhg2NngkhhDBT+a1WHJ+SDkCHsAP8uGYifvEPOFeyAi8M+IxzfhWzbNHw+HMl2Z3mgcNKAIqnDqdhQD291t2aWMzw0+zZs1m8eDGrVq2iZs2aHDlyhMGDB+Pt7c2YMWNMXT0hhBA2LGO14t255NT4uDgw9PBG3tv5NXYo7K5Qn9e6TyLO2Q3IukVD5udK00Ry12kWaNJxSwuhS9AA81r52MxYTE/Nvn37eP755+natSsVKlSgV69ePPPMMxw6dCjXxyQnJxMbG5vlJoQQQuhbnqsVP1edcpPfYvLOr7BDYXW9TgzuPVUX0Dy+RUPGczWv7MFdp4/RamJw1Fbk+XIf8kmvuua5xo6ZsJiemubNm7Ns2TIuXLhAlSpVOHnyJHv27OHzzz/P9TEzZ87kww8/NGIthRBCFFWR1ngxIzmuVpyWgHe/nrB9O4pGw5peo3kv6BndLtu5bdHg7+1CuvcyUm6E4+Ncgk29f6Wuf2WL+nmYgsVM6dZqtbz77rt88skn2Nvbk56ezscff8ykSZNyfUxycjLJycm6+7GxsQQGBsqUbiGEMDMFWePF4ly8CN26wb//qptSfv89Me07FWiLhrn75zJ+63jsNfZsG7CNtkFtc3gB22F1U7rXrl3L6tWr+f7776lZsyYnTpxg3LhxBAQEMHDgwBwf4+zsjLOzs5FrKoQQojBiElKyBTSgToWeuP4UC/rWt7weil271D2c7t+HsmXVXbbr1cMb8m3L1vCtvLXtLQA+7/i5zQc0hWExQc3bb7/NxIkTeemllwCoXbs2V65cYebMmbkGNUIIIcxffmu8RMWlWFZQs2IFjByp7uHUuDFs2gT+/gV66MnbJ+m1thdaRcvAugN5vcnrBq6sdbGYoCYhIQE7u6x5zfb29mi1WhPVSAghhD7kt8bLw3zOF4VB8nfS0uCtt2DePPV+796wciW4uRXo4ddirtHl+y48THlImwptWPrsUjT/5d4Ypf5WwGKCmm7duvHxxx9Trlw5atasyfHjx/n8888ZMmSIqasmhLAgcjEwP/mt8eKZz/nCMkj+zv370KcPbN+u3v/gA5gyBewKNsk4Oimazqs7c/PhTWqWrMmGPhtwdsg5fcIq84/0xGIShR8+fMjkyZPZsGEDkZGRBAQE0LdvXz744AOcnAr2B0n2fhLCtsnFwDzFJKTw+prjua7xos+cmoLs0VTo1zp7Fp5/HsLD1V6Zb76Bnj0L/PDktGQ6re5E6OVQAjwD2D90P+W8yxmv/hagoNdviwlq9EGCGiFMz1Q9JbZ6MbAUN6MTmbj+VJbAJmO6s78eA87wyDjafb4r1/M7xodQyc+j4E/4yy/Qvz/ExUH58mr+TN26BX64VtEyYMMAvj/9PZ5OnuwevJt6pesZr/4WwupmPwnbJEMF1sWUPSVWl4xqZXJc4yWX6c5PQm/5O4oCM2bA5Mnq/0NC4KefoGTJAtdFURRe/e1Vvj/9PQ52Dqx/cX2eAQ2YJv/IkkhQI8yWDBVYF1NP25WLgfnzdjP8lxa95O/Ex8OQIbB2rXr/tdfgiy/AseC5P4qi8MaWN1h2bBkaNHz7wrd0qNQh38cZO//I0ljMNgnCtuR3AYxJSDFRzURRFaSnxJDkYiDg0b5KOXl8u4IcXb0KLVuqAY2DAyxdCosWFTqgeW/ne8w7qM6SWv78cl6q9ZJx6m/lJKgRZsnUF0Chf6buKZGLgYB89mjKYbuCLP7+Gxo1ghMn1GGmnTthxIgCvW5MQgrhkXEcv/qANzdPYeaemQD8X5f/Y1C9Qcapvw2Q4Sdhlkx9ART6Z+qekoyLQW7JqLZ+MbAlRcrf+fJLGDVKXVCvXj3YuFFNDC6AzEPpMQ4/Ee24CoAPWs3i1cavGqf+NkKCGmGWTH0BFPqX0VOS27RdY/SUyMVAZChw/k5qKrzxhjrEBOqCeitWqHs5FUDGUPrusLtEO3xHrOOP6uunvkxERBtiEoqWoG6M/CNLJMNPwizJUIH1MZduc283Jyr5eVCvXDEq+XnIhUHkLioKnnnmUUAzfTr8+GOBAxpQh9J3h0XywHGZLqDxSR2ET9pLMpRuANJTI8ySDBVYJ+kpERbj9Gl47jm4fBk8PGD1avV+IT1ISOKe4wLiHbYBUDzlVTzTu+rOy1C6fklQI8yWXACtk3SbC7P344/qlO2EBKhUSV1Qr2bNQj9NcloyU/aMVAMaxY4SqWPxSG+XpYwMpeuXBDXCrMkFUAhhNKmpMGECzJ2r3m/fXg1wihcv9FM9SHzACz++wK4ru7DDgeIpb+OubZGljAyl658ENUIIIQzO7FcHv3NH3ZBy139bEEycqObQ2NsX+qkiHkTQ5fsu/Bv1L55Onizr8j1/HCkpQ+lGIEGNEEIIgzL71cEPHIBeveDGDTV/ZtUq6NGjSE91+MZhnl3zLJHxkZT1Kssf/f6gdqnadA5OkaF0I5DZT0IIIQzGrFcHVxRYsgRat1YDmmrV4PDhIgc0G85tIGRlCJHxkdQrXY8DQw9Qu1RtQGbdGYsENUIIIQzGbFcHT0yEoUPh1VfVXJoePeDQITWwKaR0bTrv73yfHmt7kJiWSKfKndg9aDdlvMoYoOIiLzL8JIQQwmAMvTp4kXJ1Ll1Sh5uOHwc7O3W37XfeAY2m0K//IPEB/X7ux+aLmwEY13Qcnz7zKQ52cnk1BfmpCyGEMJgnXR08r6ClSLk6v/4Kr7wC0dHq/k1r1kC7djmXzcepO6d44ccXuPTgEq4OrnzZ7Uv61+lfpOcS+iFBjRBCCIN5ku0x8gpa3J3s88zVWdC3ftYem7Q0mDwZZs1S7z/1FPz0E5QtW+g2KYrCyhMrGf3naBJSE6jgU4ENfTZQr3S9Qj+X0C/JqRFCCAPJvDNz+N040ybFmkhRt8fIL8E48mFywXN1bt5Ue2MyApoxY9Sp20UIaGKSYuj3cz+G/DKEhNQEOlTswJHhRySgMRPSUyOEEHoWk5DCg4RUJm88zd8X7+mOm9U0ZiMqyurg+SUYRycWMFdn2zbo3x/u3gVPT/jqK3jxxSK148D1A/Rd35fL0Zex19gz/enpvN38beztCr+WjTAMCWqEEEKPbkYnsuvCXX47dZO9mQIayGNoxAYUdnXw/BKM3Z3yDiQ8He1gyhSYNk2dul23rjrcFBxc4DpkSNOmMXvPbKaETiFdSaeCTwXW9FzDU2WfKvRzGYLZL2xoRBLUCCGEnmQMmQxqXiFbQJMhY2jEVi86BZVfgrG7k0OuuTrd/DSU79sddoWqB0aMgC++ANfC95CdiTzDoI2DOHrrKAB9avZh6bNL8XbxLvRzGYLZL2xoZJJTI4QQepIxZJKcps2zXG7TmCUH55GMBOOctA72xcfNMcdcnZHpV/jikyE47AoFd3f47jtYurTQAU2aNo0Zf8+g4bKGHL11FB8XH77p/g1req4xm4DGrBc2NBHpqRFCCD3JGDJxdsj7+2JO05jlG3dWGQnGE9efynXPJG83HuXqJCQTuGQexT//GI1Wq+6qvW5dkRbTO37rOCN+G8GRm0cA6FalG0ufXYq/p7/e2qcPBVnY0NZ6BCWoEUIIPckYMjl+LZoWlUvkOASV0zTm/L5x22IODhQswdjbzQnv+BgY+TJs3aoeHDwYFi4EN7dCvV5sciyTd05m4eGFaBUtPi4+zO80n5frvIymCAvzGZqhFza0RBLUCCGEnmQMmSzfE8H8vvUBsgQ2uU1jlm/cucs3wXj3bujXT927ydUV/u//YNCgbMXySqZVFIW1Z9fyxpY3uBV3C1BzZ+Z2nGt2vTOZPenChtZIghohhNCTzEMmY9YcZ0jLIIa0CAKgbDFXSnu55HiBlm/cRZCeDtOnw0cfgVYLVauqw021amUrmtfQXlRyGOO3jGdHxA4AgosHs6jLIjpU6mC0phTV4wsbujnZM6RlEPUDfQDQKgoxCbYVEEtQI4SwGuYwtbUoa7LIN+5Cun5dXXtm9271/sCB6nCTh0e2orkN7f0VFkbIVzO5lPArWkWLs70z77Z6l3davIOLg4sxWvHEMgfRR648YH7f+qzYG8HCnRd1ZWwtL0uCGiGMyBwuutbKnBJtC7smy5NsJWBzfvlFzZm5f18NYhYvhpdfzrX440N7WpJ56LCJGIefUOITAehdozez2s+iYrGKBq++vmUE0dEJqby/8bTNr40kQY0QRmJOF11rY+mJtgWZ6WPzkpPVnbTnz1fvN2yobkaZz2J6GUN7CqnE2W8jxvEH0jX3AXDSBrPk2XkMbtzZoFU3NG83JzV4k7WRJKgRwhgs/aJr7qwh0bYow1Y24/x5eOklOHFCvT9+PMycCU75/2zcneyIs99BjMP3pNndAcBe64dP2gDc00NoXb6VAStuPJKXpZKgRggjsIaLrjmzlj/ohR22shRFHnZVFHWvprFjITERfH1h1Sro0iXfh6Zp01hzeg3Td3/MPafzANgrxfBO7YNHekc0OFrV0J7kZakkqBHCCKzlomuu5A+6+SrysOu9ezB8OGzYoN5v1w6++QYCAvJ8veS0ZFadXMWsPbOIiI4AwMelOBWd+3L3dlvscNHVwdyH9goTDEpelkqCGiGMQC66hiV/0M1TkYdd//oLBgxQ155xdIQZM9QhJ7vcV2qOTY7ly6NfMvfAXG48vAFASbeSvNnsTV5t/CpKuotFDe0VNhiUvCyVBDVCGIFcdA1L/qCbp0IPu6akqDtrz56tDj1VqaImAzdokOtrXIu5xryD8/jy2JfEJscCEOAZwDvN32F4w+G4OT5aVdhSPgdFDQYlL0uCGiGMQi66hid/0M1PoYZd//1XXXvm2DH1/rBh6s7a7u7ZHqcoCgeuH2DBoQWsPbuWdCUdgOq+1RnfbDwD6gzA2cFZX80wuifJwbPWvKyCsqig5saNG0yYMIE///yThIQEKleuzIoVK2jUqJGpqyZEvuSia3i2/gfd3BRo2FVRYMkSePNNNRm4eHH48kvo0SNb+cTURH48+yMLDi3g2K1juuNtK7TlzWZv0jm4M3aavDcTtQSSg1d0FhPUPHjwgBYtWtC2bVv+/PNPSpYsSVhYGMWKFTN11YQoMLnoCluS37BryYf3SB3QC8etWwBICHmatK+/xqtShSxlL9y7wJdHv2TFiRXcS1TXYnG2d6Zf7X6MbjKaBv65D0+Zg8LO/pIcvKKzmKBm9uzZBAYGsmLFCt2xoKAgE9ZICCFEXvIadp3neAn3Jl2xv3+PZHtHZocMYkWjbrQKvcOsEqUo7qHh53M/s+zoMnZd2aV7bHnv8rzW+DWG1B+Cr5uvKZpVKEWZ/SU5eEWnURRFMXUlCqJGjRp07NiR69evs2vXLsqUKcNrr73G8OHDc31McnIyycnJuvuxsbEEBgYSExODl5eXMaothBA2L6On4mFSKt4pCZT5YAJOa1YDcKZUJcY9+yYXfcuhoJCiCce7xB4i03fyIEld+ddOY0fX4K4MbzCcLsFdsLezN2VzCiwmIYXRa47nmB/TOtg3z0U3b0Yn5pqD52+DK5DHxsbi7e2d7/XbYoIaFxd1bYHx48fTu3dvDh8+zNixY1myZAkDBw7M8TFTp07lww8/zHZcghohhDCB0FB188mrV1Hs7FjYtDfzW7xEkn088fahxDlsJ9Xusq54oFcgwxoMY0j9IZT1KmuqWhdZeGQc7T7flev5HeNDqOSXfRPODJmDQVvPwbO6oMbJyYlGjRqxb98+3bExY8Zw+PBh9u/fn+NjpKdGCCHMQHw8vPcezJun3q9UiZOz5tD++D7i7f8i0e4YaLTqOcURt/RmfNrldUY2fcFiemVycvzqA174v325nt/4WnPqlZO80IIoaFBjMTk1/v7+1KhRI8ux6tWrs379+lwf4+zsjLOz5U7rE0IIi7d1K4wcCZcvk2YHO1/rzPchxVh3YQDxTnG6Yk7aKniktcctvTX2eNCxUohFBzQgCb+mYDFBTYsWLTh//nyWYxcuXKB8+fImqpEQQhhekfdNMrV792D8eLTffsP+srCmjyc/1bYnMu1POKsW8bAPwD6pNe7pbXBUHg0vWUsyrCT8Gp/FBDVvvPEGzZs3Z8aMGbz44oscOnSIZcuWsWzZMlNXTQghDKLI+yaZkqKg/PADRz5+jR/LRPPTWLjqA/AQ0qCEawl61+hN/zr9CfJswKSfT1vtgpSy6KbxWUxODcBvv/3GpEmTCAsLIygoiPHjx+c5++lxBR2TE0IIU3uSmTOmoCgKx07+yU9LxrDWJZyITKkiHk4evFDtBfrW6kv7iu1xtH807GILybC20EZDs7qcGoBnn32WZ5991tTVEEIIg3uSpfKNRVEUjt46yk9n1rLu4AouaaPAXz3nhiPdqnfnxdov0blyZ1wdc+5ZsoUFKW2hjebCooIaIYQwBVPktZjrUvlaRcuhG4dY98861p9bz+Xoy7pzrqnQ5V5x+nR/ny5tR+DulH3fJiEMSYIaIYTIg6nyWsxp5ky6Np191/ax/tx61p9bz/XY67pzbinQNQx6X3Smy6DpuH/4Bthb9qwlYbkkqBFCiFzEJKRkC2hAHf6ZuP6UQfNaTD1zJk2bRujlUNb/s54N/27gTvwd3TkPeze6RTjSc38MnS6Ce7tOsGEJWMlsVIudcSYkqBFCiNyYMq/FFDNnUtJT2HFpB+v+Wcem85t0m0cCeDt781ylLvQ+kkCHub/gkpoAJUrAinnQrx9oNHqvjylY5IwzoSNBjRBC5MLUeS0BPq4s6FvfoDNnktKS2HJxC+vOrePX878SkxyjO1fCtQQvVHuBnjV68vQlBaf/jYKICPVk//4wdy6ULKm3upiaKXvmhH5IUCOEELkwh7yWJ5k5k9swSkJqApsvbmbdP+v49cKvxKU8Wtm3tEdpelTrQc8aPWldvjUO0bHw1luwYoVaIDAQliyBLl300TyzYgkzzkTeJKgRQohcmDqv5Uk8PoyiJYmgMhfwLnGUnZc3E58arytb1qssPav3pFeNXjQPbI6dxg4UBdatg9dfhzt31OGlUaNgxgzw9DRVswzK1D1z4slJUCOEELmw1BVhM4ZRdoVdJ9HuCAn2e0m0P8S1+8lwXy1T3rs8vWv0pleNXjQu01gNZDLcuKEGMJs2qferV4evvoLmzY3fGCMyh5458WQkqBFCiDwYI69Fn7NtktKSWHN6Iz9fXkKiy0EUTZLunL22FO7pLVj50hi612yN5vHkXq0WvvwS3nkHYmPB0REmTYJ33wUb2BzYknvmhEqCGiGEyIchV4TVx2ybNG0aOy7tYM2ZNWz4dwOxybG6v+72Wj/c01vilt4SJyUYDRqCvOpkD2guXIDhw2H3bvV+kybw9ddQq5Y+mpkrc5o+bak9c+IRCWqEEMJEnmS2jVbRsu/aPr4//T0//fMTUQmPnqOUewAJMY1xT2uFk1IVDVkDmCzDKKmp8NlnMHUqJCeDmxt8/LGaS2PgRfTMcfq0MXrmhOFIUCOEMDpz+nZuSkWZbXM28iyrT6/m+9PfcyXmiu54CVdfOlbsztPlXuDpiq2Ysumf/IdRjh6FYcPgxAn1/jPPwNKlUKGCPpqXJ3OePi17NVkuCWqEEEZljt/OTaWgs21ux93m+9Pf8+2pbzlx+4TuvKeTJz2q9+CZoB78frgEe49Es/eIwmdOe1k+qDEKZPs5z+5ZB2/S4J331R4arRaKF1fXnBkwwGiL6Mn0aWEIEtQIIYzGnL+dm0Jes220JBF6bSOT/l7L1vCtaBUtAI52jnQO7kz/2v3pVqUbKan2jF5znL0XH/1ME1LSGbLyMJOfrcEHz9YgPjnt0TDKgT0wYgSEh6uF+/SB+fPBz8+gbX2cTJ8WhiBBjRDCaOTbeVaPz7ZRUEix+5c4++0kO+7hje2P1pJpVrYZA+oM4MWaL1LCrYTu+M0HcTn+TBNS0pn082l2jA+hXrli8OABjBmnJv8ClCkDixdDt24GbWNuZPq0MAQJaoQQRiPfzrPKmG0zZu0OdlxdR5z9DtLs/tsBW4EKPhUYUGcAA+oMILhEcI7PUaCf6fr1MHo03L6tHnz1VZg1C7y89NmcQpHp08IQJKgRQhhNUb6dW2tScZo2jT/C/uCrY1/xx+0/SHdMB8DVwY3uVXsyotEQWpdvnXVRvBzk9TP1j71L5VcHwh+/qgeqVlUX0WvZUm/tKCqZPi0MQYIaIYTRFPbbuTUmFV96cImvjn3FyhMruRV3S3e8RWALhtQfQu8avfF0Lvg2BDn9TJ3SUhl2eANjD6zFOSUJHBxgwgR4/31wcdFre56ETJ8W+qZRFEUxdSWMJTY2Fm9vb2JiYvAyYberELbsZnRirt/O/TMFKjEJKYxeczzHfJHWwb4WlVScmp7Krxd+ZenRpWwN36o7XtKtJAPrDmRog6FU861W5OfP/DNtFXGMqduXUun+DfVky5awaBHUqfOkzRDCZAp6/ZaeGiGEURX027k1JBVfj73OsqPL+PLYl9yOU/NZNGh4ptIzDG8wnG5Vu+Fk/+RtCPBxZeFT3titn47Hn78BoPUrhd2cT+Hll402TVsIU5OgRghhdAVZ3MxSk4oVRWFnxE4WHV7EL+d/IV1Rc2VKuZdiSP0hDG8wnKBiQfp7wbg4mDEDr88+g5QUdRXg11/HbupU8PbW3+sIYQEkqBFCmCVLm/L7MPkh35z8hgWHFnD+3nnd8TYV2vBao9foXq07jvZ6rLNWC6tXq7kyt/7LzWnfHr74AmrW1N/rmAFrTRYX+idBjRmSX2AhLGfK78X7F1l0aBHLTyxXN5JEXen3lbqv8Frj16hRsob+X/TQIRg7Fg4cUO9XrAiffw7PPWd1Q03WmCwuDEcShc2M/AIL8UhBk4qNTVEUQi+HMvfAXH678BsK6p/RKiWq8HqT1xlYd2ChZjAV2K1b8O67sHKlet/dXZ3R9MYb4Oys/9czMWtKFhdPRhKFLZAsIS8Ky9p79cxtym9Kego/nPmBuQfmZtmDqXPlzoxtOpYOlTrku65MkSQnw7x5MG2amkMD8MorMHMmBATo//XMhDUkiwvjKnRQM3DgQIYOHUrr1q0NUR+bJr/AojBspVfPHHZMjk6KZsmRJcw/OF+3toyrgyuD6g1ibNOxVPWtapgXVhTYtAnefhsuXlSPNWmi7tXUtKlhXtPEMgfqadq8BxLMNVlcmE6hg5qYmBjat29P+fLlGTx4MAMHDqRMmTKGqJvNsdTZHsL4pFfPOK7FXOOLA1+w7Ngy4lLUHhJ/D39eb/I6IxqOyLIHk94dPw7jx0NoqHq/dGmYPVudom1ngN4gM/B4oP71wEZ5lje3ZHFheoUOajZu3Mjdu3f59ttvWbVqFVOmTKF9+/YMHTqU559/HkdH+ZA9rqBDBJY220MYV+bPkauTvfTqGdA/d/9h9t7ZfH/6e9K0aQDU8qvFW83eom/tvnpZWyZXN27Ae+/BN9+oPTXOzvDmmzBxIngaIE/HTOQUqB+/Fk2LyiXYe/FetvLmlCwuzEeRcmpKlizJ+PHjGT9+PMeOHWPFihUMGDAADw8PXn75ZV577TWCg3PefM3WFGaIwFJmewjje/xz9H/9G+RZXnr18pfTl41/7x9n5p6ZbDq/SVeubYW2vN38bTpV7oTGkDOLHj6ETz6Bzz6DxET1WN++at5M+fKGe10zkdPw+/I9EczvWx8gS2Aj+0OJ3DxRovCtW7fYtm0b27Ztw97eni5dunD69Glq1KjBJ598whtvvKGvelqkwg4RyAZvIic5fY6cHfIefpBevbxlDhIVFJLsTqHx+pk7yUcBddXfHtV7MKHFBBqXaWzYyqSmwpdfwtSpcPeueqxFC3WKdpMmhn1tM5LT8HtCSjpj1hxnSMsg3utSnZQ0rcmTxYV5K3RQk5qayi+//MKKFSvYunUrderUYdy4cfTr1083zWrDhg0MGTLE5oOaoiT+mttsD2F6OX2OpFu+6DKCxN1hd0myO0qMww8k2/8LyaDBnn61Xub9kIlPtBdTgSgKbNigDiuFhanHgoPVvJnu3a1uvZn85Db8npCSzsKdF3mhXhlqBMgKySJvhQ5q/P390Wq19O3bl0OHDlGvXr1sZdq2bYuPj48eqmfZipr4aw6zPYT5yOlzJN3yRXf3YTJbwn8jxvkHUuzUGUUaxQmP9I54pb3Ah616U8nXw7CV2LtXndG0f796v2RJtadm+HCw0bxEGX4X+lDooGbu3Ln07t0blzy2r/fx8SEiIuKJKmYNJPHXephyPZicPkeZu+Und61BUmq69OrlQ1EUfjn/CxO2Teau82kANIoznmld8ErrgT3FAAPnI507py6et3Gjet/NTU0CfustMNMFQY1Fht+FPhQ6qBkwYIAh6mGV5JuHdTD1ejC5fY4SUtI5dS2a4S2D5A9+HjKCmQ93fcjx28cB0CiueKZ1xSvtBezJOqRhkC8b167BlCmwapW6Z5OdHQwdqvbOWPHieYUlw+/iSVnnYgdmIuObR+tg3yzH5ZuH5cgv2TsmIcXgdZDPUdEoisLmi5tp/GVjuv/YneO3j+Ph5MH4pu/QK2ADxdIGZQto9P5lIypK7YUJDoYVK9SApnt3OH0ali2TgCYH3m5OVPLzoF65YlTy85DPtygUi90mYdasWUyaNImxY8fyxRdfmLo6uZJvHpbNXFZ5ls9R4YReDuX9ne+z99peANwd3Xm9yeu82fxNfN1889xTSi8/09hYdbfsOXPUqdoAISEwaxY89dSTP78QIkcWGdQcPnyYpUuXUqdOHVNXpUCeJPHX2vf2MXfmtMqzJJDn79CNQ7y74112ROwAwMXBhVGNRzGhxQRKupfUlTNYkBgfDwsWwKefwv376rF69WDGDOjUyeZmNAlhbBYX1MTFxdG/f3++/PJLpk+fburqGJSpczmEJHtbin/u/sP7O99nw78bAHC0c2REwxG82+pdAjxzHuLRa5CYmAhLlqgL5WWsNVOtmpoz07u31W5rIIS5sbjftFGjRtG1a1fat2+fb9nk5GRiY2Oz3AwpJiGF8Mg4jl99QPjduCfKtzCHXA7xKEk3J5LsbXpXoq8weNNgai+uzYZ/N2CnsWNQvUGEvR7Gwi4Lcw1o9CY5GRYtgsqV1X2a7t6FSpXULQ7OnIE+fSSgEcKILKqn5ocffuDYsWMcPny4QOVnzpzJhx9+aOBaqfTdq2IuuRy27kmmmcrQoeHcT7zPjL9nsODQAlLS1QC/R/UeTGs7jRolaxi+AqmpsHIlTJ8OV6+qx8qVgw8+gFdesdm1ZoQwNYsJaq5du8bYsWPZtm1bnmvkZDZp0iTGjx+vux8bG0tgYKDe62aIHZPNKZfD1hUl/0KGDg0jMTWR+QfnM3PPTGKSYwB1b6ZZ7WfRpIwRthRIS4PVq+Gjj+DSJfVYQIC6AeXQoermk0IIk7GYoObo0aNERkbSoMGjjfzS09PZvXs3CxcuJDk5GXt7+yyPcXZ2xtkIf2QM0asiuRzmpTD5F4YIcm3N471cxd0c+CXsR97/632ux14HoE6pOsxuP5uOlToadqNJgPR0+OknNUfm/Hn1mJ8fTJoEI0eCqwSqQpgDiwlq2rVrx+nTp7McGzx4MNWqVWPChAnZAhpjMkSviizcZ7ksdejQXIbLHu/lSrQ7QarHNzxIvQBAOe9yTGs7jf61+2NvZ+Df+7Q0+PFHdZjp33/VYyVKwDvvwKhR4O5u2NcXQhSKxQQ1np6e1KpVK8sxd3d3SpQoke24sRmiV0WWDLdcljh0aC7DZZl7uVI0V4l2XE6i/RFIBUeNO++3epd3Wo3HxaFgQ9BFlpIC332nzma6qO4PRbFiajLwmDE2v6WBEObKYoIac2aoXhV9raVhLt/AbYWlDR2a03BZVFwKoWHhRDuuJs5+M2i0oNjjmd4F79SX6F/zOcMGNElJsHy5ulN2RgJwiRLwxhswejR4yy7RQpgziw5qQkNDTV0FwLC9Kk+6loa5fAO3JZY2dGguw2XJacksOjKXGy6zUTTxALimP0Wx1ME4KmUAA/ZyxcfD0qXqonm3b6vHSpdWd9IeMQI8DLxrtxBCLyw6qDEn5riMvTl9A7clljZ0aOrhsowNJ9/c+ibhD8JBA07aShRLHYqLNuuq4Xrv5YqJUdeZmTtX3acJIDAQJkyAIUMkAVgICyNBjR6Z2zL25vIN3BaZY5Cbm/yGy7xcHQ02hHkm8gzjNo/TbWtQyr00FZ2GceNmYzRkTQLWay/X3bswbx4sXKgGNqAumjdpEgwYAE7m9z4JIfInQY0VM/U3cFtniiC3KMFHXsNlHar74WRvx+g1x/U6hHkv4R5TQqew+MhitIoWZ3tn3mz2JpNaTSI2wd5wvVzh4fD552reTFKSeqx6dXWdmT59wEH+JAphyeQ32IpZWsKqeDJFzZ/Ka7hs6nM1mfjzab0NYaZr01l6dCnv73yfB0kPAOhZvSefdviUoGJBAHg4of9erqNH4ZNPYN060GrVY40bw8SJ0L272W5lIEn+QhSOBDVWzNISVkXRPWn+VG7DZfocwtx9ZTdj/hzDyTsnAajtV5t5nebRNqhttrJ66eVSFNi2TZ3JtHPno+OdO6vrzISEmPWu2ZLkL0ThmefXE6EXGd/AH9+Q0VwTVkXRFST4yI+3mxOV/DyoV64Ylfw88HZz0ssQ5vXY6/Rd35eQlSGcvHOSYi7FWNh5IcdGHssxoHliaWnw/fdQvz507KgGNA4Oaq7MyZPwxx/Qpo1ZBzSyoa0QRSM9NVbOkhJWRdEZKn/KwznvPxF5DWGmpKfw+f7PmbZ7GgmpCWjQMLLhSKY9PQ1ft5x3Pn8i0dHw5ZewYAFcu6Yec3eH4cPVdWbKldP/axqIJPkLUTQS1NgAc5uVJfTPEPlTN6MTOXLlAS0ql2DvxXvZzuc1hLk1fCuv//k6F+6pWxs0D2zOws4Lqe9fv9D1yFdYGMyfDytWqOvNgLov05gx8OqrULy4/l/TwCTJX4iikaBGCCug7/ypjOGPo1ceML+vGohkDmxyG8K8GnOVN7a8wc/nfgaglHspPunwCQPqDNDvppOKAqGh6voyv/2m3geoXRvGjYN+/cDFwFspGJAk+QtRNBLUCGEF9L3gX+bhjzFrjjOkZRBDWgSRnKbF2cGOyiU98M+UrPr4UJO9xp7Xm7zO1DZT8XbR49YCycmwZg188YWaH5Oha1d1iOnpp806V6agJMlfiKKRoOYJpWvT+fbUt3xz8hu2vLwFR3v5BiVMQ5/5U5mHPxJS0lm482KW8xtfa0551B2qd0bsZNQfo/g3St3FumW5lizqsog6pbKuBvxEIiNhyRL4v/+DO3fUY25uMGgQjB0LVaro77XMgKWtSi2EuZCg5gklpCYwYfsEIuMjWXp0KaObjDZ1lYQN01f+VEGGP249vMWbW99kzZk1APi5+/Fph0/1O9R05Iia+Pvjj2ovDUDZsurmksOHW2S+TEFJkr8QhadRlIzBaOsXGxuLt7c3MTExeHl56e15lxxZwqu/v0oJ1xKEjwnXb3e7ECYQk5DC62uO5zj80bKyD3WqHeLjPVOJTY7FTmPHq41eZfrT0/Fx8cnzOQu0kFxysrpI3sKFcODAo+NNmqhDTD17gqP0iAphSwp6/ZagRg/StGnUXlybf6P+ZUKLCcxqP0tvzy2EqdyMTsw2/FGj/B2uKfM5c1fNZ2kc0JjFXRfTMKBhvs+V70JyV6+qO2V/9ZU63ARq8NKnj9oz06SJVeTLCCEKT4KaHBgqqAH49fyvPPfDczjbO3Ph9QuU87acNTGEyE1G78rNmCiWnPyYNWe/RkHBx8WHme1mMrzBcOzt7PN8bLqiMO3Xs/ydw7TwkErF+b+SUbgv/xJ+//3RFgZlysD//qcOMZUqZcgmCiEsQEGv35JToyfPVnmWNhXaEHo5lPd2vse3L3xr6ioJ8cS8XB357eJPjN86nsh4tfdkQJ0BfNrhU0p55B5sZO6Z+Xpgo2wBTYn4aHqd2U6/pZtxj76tO54W0oaoV4Zxq80zeHm6qkNUhmmaEMIKSVCjJxqNhqmtZtLmcjO+O/UdvaqMoE1QU0nqExbrwr0LvPb7a+yI2AFANd9qLO66mDYV2uT5uMeX+E9O+6/3RVFodvUU/U9s5pkL+3HSpgGQ5uWNw5DB3Ok3iLdOJfH3hSi4cAiQvY6EEIUjQY2e3IxO5Ku/NLintSXe4S/6/DiK3uWWMbtXXfmDbCVsZcfkpLQkZu2Zxcw9M0lJT8HFwYXJrSfzVvO3cLLPv72PL/HvGX2PkQfX8dLJLQQ9uKU7Hl2rHrdeGoj7gP54+3rz1prjetsN3FzZymdICFORoEYPMn8z9dEMIN5+D8n2p9ly6U806zVW8wfZltnKjsk7Lu3g1d9fJex+GACdKndiUZdFVCxWscDPEZuUir02nTaXjtD79HaazzlMq/T/emXc3NnVqAOfV2zL2VKV4CG03naJ97pWt/q9jgrzGZLgR4iikaBGDzJ/M3VQ/PBKe55Yx3U8cPyaXWH1iYqrKX+QLFh+OyZbQ9AaGR/Jm1vf5LtT3wFQ2qM08zrNo3eN3oVbc+affwhatJQDq76hZHy07nBMnQace7YPy/wbsfN6YpaH7A6LYuCDRPJi6XsdFeYzZCsBtBCGIEGNHjy++Zx3Wm/iHXaQZneDGId1PEwKMVHNhD5Y847JWkXL8uPLeWfbOzxIeoAGDaMaj2L609MLvt5SdDT88IO6oeShQ/j8dzjKzZsNNdvyU+32XC9TiW+GNGHnkv1Fqqep9jrSV49JQT9DthBAC2FIEtToweOrr9rhTrHUYUQ5fUqMw1ruJ78DFDNN5cQTs9Ydk89GnmXkbyPZe20vAPVK12Pps0tpUqZJ/g/WamHnTli+HDZsgKQk9biDA3Ttyv0X+/H2wzL8FRGtHk9JJyYx95/T8WvRtAr2zfHCb6q9jvTZY1LQz5A1B9BCGIMENXqQ0+ZzbumtcUnfSpL9SWbuf5u2lTfrd5diYTTWtmNyYmoi03dP55N9n5CmTcPd0Z1pbafxetPXcbDL509CeDisWqXerl59dLxmTRgyBPr3h1KlKA588V8vR8YS/9o8lsRavieCP8a04oNNZ8xiryN995gU9DNkrQG0EMYiQY0e5LT5nAYNHQPe48+ol9kesZX159bTq0YvE9dUFIU17Zi85eIWXvvjNS49uATA81WfZ37n+dkWi8w87OKTmkCprb/h9uMa2LXrUSEfH+jbFwYPhkaNsq32+/g+VDEJKbn+HBuVL0YxN0ez2etI3z0mBf0MWVsALYSxSVCjJ7ltPvfZwQlM2z2NsX+OI9irBVqti8xmsDDWsGPy7bjbjN8yXrf5ZFmvsizovIDu1bpnK3szOpF3fzyK/batvHA2lOoXD+KSlqKe1GigQwc1kOneHVxcClyHgv4czeHnqe8ek4K23ZoCaCFMQbZJMLDE1ESqL6rJlZgIPNOep3jqcEBmM1iimMeGUyxhx2StouWrY18xYfsEopOisdPYMabJGD5q+xGezp6PFdYStyOUgx8vpMHB7RRLeqg7FVYikOMhz9Jp5pt4Van0RHWyhJ9jeGQc7T7flev5HeNDqOTnUejnLUjbc9pzKyP48Ze/F8JGyTYJZiIl1Z5KjmO4whs8tP8V97S2OCuVZTaDBXp8OMXcnb5zmv/9/j/2XdsHQAP/Bix7dlnWzScVBU6cUGcvrVmDx7VrtPvvVKR7MX6t3pqfa/63poxGQ0OfUjzp1wFL+DkaqsekIG3PrdfX3H9mQpgDCWoMLCouhfBrwbg5tiLB4W/uOc3FP/kLNDjKbAZhEAmpCUzbNY05++eQpk3Dw8mDj5/+mFGNRz3afPLsWTWQ+fFHCAvTPTbd05MN5ZuwsUYb9pWvg/axzSptJVHV1EOOlhD4CWGOJKgxsIyx+eKp/yPJ/jSpdleIdviOYmmDAdu5SAjj+DPsT0b9MYqI6AgAXqj2AvM7z6esV1k1ePnxRzWYOXv20YNcXKBrV+jXjyuNWvPW/x3M9fltKVFVekyEsDwS1BhYxmwGe7wpkTKau87TiXX4GVdtU1y0NWzqIiGKLr9F4G4+vMm4zeP46Z+fADUReGHnhTxPVZi/Etatg5MnHz2hoyN07gx9+kC3buCp5teUyGOGki0mqkqPiRCWRYIaA8s8Nu+mfQr3tHbEO+zgnuNcepX93uYuEqLw8loErpSXE4uPLOa9ne8RmxyLvcaesZX78+GFMnj0ei9rj4yDA7RrBy+9pM5c8vHJ9lqmHnbJieyDJIQoKJn9ZASZZzNoieem82jS7e4ysM4IVr6w1Gj1EJYnJiGF0TnsXg1Qs3wUt+wWcPz2UQCaaP1ZutmReocyLYrn6KhOwe7VC557DkqUKPDrmsOwi+yDJISAgl+/JagxkswXidNRe3nl124AbH15Kx0qdTBqXYTlyGlqsZYEYu1XEev4B4pGwStFw4xtCv87AvYK4OwMzzwDvXurQ0s59MhYgrwCutbBvjJzUAgbIlO6zUzmsfl65Z7l0O3RLDy8kEGbBnF85HH83P1MXENhjjIvAueRHEfZ+99zyH8zMU7qYnh9T8NnWxT88YBenaFnT+jSRZcjY8lkHyQhRGFJUGMiszvMZkfEDs5FnaPf+n5seXnLo+m2VkzyIwpBUShxNZzhB38mOGoPX9W/wJb/1r2rfA9mbHenY4NeeP3QW82VKcTqvpZA9kESQhSWBDUm4uboxvoX19P4y8bsiNjB1NCpTHt6mqmrZVCSH1EASUkQGgq//w6//47ftQhSW8Ir3SHZAZzSoENENew1r/DbGyE8078RWGlQKPsgCSEKy87UFSiomTNn0rhxYzw9PfHz86N79+6cP3/e1NV6ItVLVmdZt2UATP97On+G/WniGhlOfrsexySkmKhmZuDaNViyRM1/KV5cnWq9cCF/2kdQ6zX4sI0a0Pgk1cA3bSlnys7Bu83TzOpt3TklGTMHc2KL08uFEPmzmJ6aXbt2MWrUKBo3bkxaWhrvvvsuzzzzDP/88w/u7u6mrl6R9avdj71X9/J/R/6Plze8zLERxyjvU97U1dI7yY/IJC0NDhzQ9cZw+nSW05erleaN513Y6HoZgADPAGa0nUOLgGeJS06zmUXgzHF6uRDCvFns7Ke7d+/i5+fHrl27aN26dY5lkpOTSU5O1t2PjY0lMDDQJLOf8pKclkzLFS05cvMITco0Yfeg3Tg7OJu6Wk/k8dyZmIQUeizen2v5ja81p165YkasoZFFRMDWrbBlC+zYAbGxj87Z2UGzZiR36cic4Eg+vvA1iWmJ2GvsGffUOKaETMm++aQNMZfp5UII07H62U8xMTEAFC9ePNcyM2fO5MMPPzRWlYrM2cGZn3r/RIOlDTh04xD/+/1/LH9uORqNxtRVK5Kccme+H9Y0z8dYXX5ETAz89Rds26YGMxcvZj1fvDh06qRuT9CxI3/cP8jYzWO5+I9ark2FNizsvJCafjVNUHnzIqv6itzIxAPxOIvsqdFqtTz33HNER0ezZ8+eXMtZSk9Nhs0XN9P1+65oFS3T2k7j/dbvm7pKhZbb2iKjn67MiasP2HPxXrbHWMWaIwkJsG+fGsjs2AGHD4NW++i8gwM0a6auH9OxIzRoAPb2hN8PZ9yWcfx24TcASnuU5vNnPuelWi9ZbFArhDHIxAPbYtU9NaNGjeLMmTN5BjQAzs7OODtbzjBOp8qdWNRlEa/+/iqT/5pMxWIV6Ve7X56PMbdvKrnlzizfE8H8vvXRaDTZ/ghZZH5ESgocPKgGMTt3wv796rHMgoPV1Xw7dICnn4ZMv4jxKfHM3DWTT/d9Skp6Cg52DoxrOo7JIZPxcja/gNvQzO1zLMxbfhMPLP5LkigyiwtqRo8ezW+//cbu3bspW7asqaujd/9r9D/C74czZ/8cBm8aTFmvsrQun3POkDl+U8ltbZGElHTGrDnOplEtsNNoLC8/IjkZjh6FXbvUKdd79qi9M5mVKaOuF9O2rfpvYGC2p1EUhTVn1vDOtne48fAGAB0qdmBep3lUL1ndCA0xP+b4ORbmTSYeiNxYTFCjKAqvv/46GzZsIDQ0lKCgIFNXyWBmd5hNRHQE68+tp/sP3dk/dD9VfatmKWOu31TyWlskISUdO42GSn4eRqxREUVGqr0v+/bB3r1w5Iga2GRWsqTaA9O2rfpv5cqQx5DRkZtHGLt5LPuu7QOgvHd55nacS/dq3W12qMlcP8fCvMnCjCI3FhPUjBo1iu+//55Nmzbh6enJ7du3AfD29sbV1bq+zdlp7Pj2hW+58fAGB64foPPqzuwevJuyXo96psz1m0rmXckfZ7Zri2i18M8/agCTEcQ8ntgL4OsLrVtDSIgaxNSsmWcQk+Hmw5u8t/M9Vp5YCYC7ozuTWk5ifLPxuDpa12e3sMz1cyzMmyzMKHJjMUHN4sWLAWjTpk2W4ytWrGDQoEHGr5CBuTq6sumlTTT/ujnhD8J5etXThA4KJcAzADDfbyoWsbbIw4dw6NCjAObAAXW20uNq1oTmzR/dgoMLFMRkiE+J57P9nzF772wSUtWhqgF1BjCz3UzKeJXRV2ssmrl+joV5s8gvT8IoLCaoscBJWk/Mz92PnQN3ErIyhLD7YbrAprRHabP+phLg48qCvvXNY20RRYErV7L2wpw6lXVmEoC7OzRt+iiAeeopKFa0dXO0ipZvT37Luzvf5ebDmwA0K9uMuR3n0rRs3lPbbY05f46F+bKIL0/CJCwmqLFV5bzL8dfAvwhZGcL5e+d1gY2vh49Zf1Mx2doiSUlw8mTWIObWrezlypd/FMC0aAG1a6vTrp/QtvBtTNg+geO3jwNQwacCs9vPpneN3jabN5MX+cYtisqsvjwJs2GR69QUVUHnuZuj8PvhtFnVhuux16nlV4udr+wkNdUj128q/rYwayQ5Wd1i4OhRNZH3yBE4c0bdhiAzBwd1XZgWLdQgplkzdaaSHh2/dZwJ2yew7dI2ALycvXiv1XuMaToGFwfr2j1b325GJ9r251gIka+CXr8lqLEgYffCCFkZwq24WwQXD2bzy5sp4VzWNr6pJCaqAczx43DsmBrInDoFqTnkXJQsqQ4fZQQxjRqBgZLJLz24xAd/fcDq06sBcLRz5LXGr/F+6/fxdct5M0ZzYi7rw8hWCEKIvEhQkwNLD2oAzkedp+N3HbkScwU/dz9+7/c7jQIambpa+nXnjjqElHE7cQL+/RfS07OXLV5cDVoybg0bquvDGHio50bsDabvns5Xx78iTav2DPWt1ZfpT0+nYrGKBn1tfZH1YYQQlkKCmhxYQ1ADcOvhLbp834UTt0/g7ujO2t5r6RLcxdTVKrykJHUq9enTj26nTsF/0/WzKVkS6tdXbw0bqkFMhQoGD2Ayi0qIYtaeWSw6vIiktCQAnqn0DDOenkHDgIZGq8eTym07C7CSbSuEEFbFqrdJsHX+nv7sHrSbXj/1Ymv4Vp5b8xxLnl3CsAbDTF21nCUnw4ULagBz9qz675kzEBaWfRYSqEFK5cpQrx7Urave6tVT82BMlGx7N/4un+3/jIWHFhKfGg9Ay3It+fjpj3Nd8dmcyfowQghrJEGNhfJ09uS3vr8x/NfhrDq5iuG/DufwjcPM6zzPdImpUVFw/rwawJw/r97OnVMXsstp6AigRAl15lHt2lCrFtSpo/7f3d24dc/F3fi7zNk3h0WHF+mCmQb+DZjedjqdKnfKNqPJXHJU8iPrwwghrJEENRbM0d6RFc+voGKxikwNncqyY8s4dPMQ63qvo1LxSoZ50eRkCA9/FLRkvt2/n/vjvL3Vxexq1Hj0b+3aULq0yXpf8nI99jqf7fuMZceW6RbOa+jfkCkhU3i2yrM5Ts+2pBwVWR9GCGGNJKfGSmwN30r/n/sTlRCFl7MXK55fQY/qPYr2ZLGxauCS0+3atZyHjDIEBkLVqllvNWtCQIBZBi+P+zfqXz7Z+wnfnfqOVK3aW9EooBFTQqbQNbhrrmvNWFqOSkxCCq+vOZ7r+jDmVl8hhG2TROEcWHNQA+qMnD7r+rD32l4ARjYcyScdPsHL+bG2JiSoq+xeuQKXLz+6RUTApUvqMFJePD0fBSxVqjz6f3Cw2QwbFYaiKOy5uoe5B+ay8d+NKKi/Em0qtGFii4k8U+mZfBfOC4+Mo93nu3I9v2N8iNlt5CnrwwghLIUkCtugMl5l+KvfVt77fTyfnlnK0qNL+e3oGhbHtqLbJUe4elW95Re0gLp5Y6VKWW+VK6v/liplEb0u+UlJT2Ht2bXMPTCXY7eO6Y4/V/U5JrWcxFNlnyrwc1lijoqsyCqEsDYS1DwhoyWGpqaq67fcupXz7cYNuHEDx8hIPlEUOleAEd3gYolYnvP6nRcdYP55KBX/3/N5eqpbBQQFqdOiK1RQ/59x8/bWfxvMxPXY63x17CuWHV3GrTh1CwUXBxcG1BnAuKfGUaNkjUI/p6XmqJhsOwshhDAACWqewBMlhqanq4m1UVFZb3fvQmSkertz59Ht3j11c8aCcHSkraYsp04HMLXOA+YUO8faWgpb67rzbrXhjG7zDq4lzDNB11C0ipat4VtZcmQJv174Fa2i5gX5e/gzqvEoRjYa+UQrAMseRkIIYXqSU1NEmRNDS8dG4ZP0EM/keLyS42nsY8fAal64xj9UA5cHD9TbvXuPbvfvFzxIyeDgoA79+Ptnv5Up8+jm6wt2drqHHb15lGG/DuPE7RMAlPUqy9SQqQysNxAHO+uOa8PuhfHtqW/59tS3XI6+rDseUj6EkQ1H0rNGT5zs9RNwSI6KEEIYhiQK50CfQU3mxNC/lwwlMOZO0Z7Ix0ddKdfXV72VKKEGLn5+6q1UKfVWunS2YKUw0rXpfHPyG6aETuFa7DUAqvtW5/3W79OrRi+9XdjNQVRCFOv/Wc+qk6vYf32/7riPiw8D6w5kZMORVC9Z3SCvLXsYFYylrOcjhDAPEtTkQJ9BzfGrD3jh//YBsPGb8ZSJiSTWxZ2Hzm7EOntQu1Z5ipUppe5NVKyYeiteXA1aMgKY4sXB0bi5FklpSfzf4f/j478/5n6iuq6Mv4c/rzV+jRENR+Dn7mfU+uhLZHwkG85tYN25dfwV8RfpirrYn53Gjo6VOvJK3Vd4vurzuDpKj4mpWdJ6PkII8yBBTQ4M1VOTE3OcwptZTFIM8w/O5/+O/B+349S9lpztnelTqw8v136ZtkFtzXpoSlEUTt05xeaLm/nj4h/subpHlycDUK90PV6u/TL9avfD39PfhDUVmVnaej5CCPMgQU0O9J1TYw2Ll6Wkp/DT2Z+Yd3Aeh28e1h0v4VqCHtV78GLNF2lToY3JAxxFUYiIjmDv1b3svLyTLRe36GYuZWgc0JheNXrRs3pPw62oLJ6IpX8ZEEKYhgQ1OdD34nvWlhh64PoBVp5Yyfpz64lKeNQmDycPWgS2IKR8CK3Lt6ZxmcYGz8GJSoji9J3TnLh9gn3X97H36t5sQYyboxtPBz1N58qd6RLchQo+FQxaJ/HkMg/b5mTja82pV66YEWskhLAEEtTkwBArCltjYmiaNo1dl3ex9uxafv735ywBDqjDVFV9q1KjZA2q+1anum91gooFUdKtJCXdS+Lm6JbvaySlJRGdFM21mGtcibnClegrXIm5Qtj9ME7dOcXNhzezPcbRzpFGAY1oWa4lHSt1pGW5ljg7OOut3ZbKkpJupadGCFEUEtTkwNq3STAEraLl9J3T7L6ym11XdrH7ym7uJtzN8zHuju6UcCuBo50j9nb22GnssNPYkZKeQkxSDDHJMaSkp+T72hWLVaS2X22almlKi3ItaBzQWBJ9M4lJSOFBQiqTN57m74v3dMfNOenWWoZthRDGJUFNDiSoeXKKonDpwSXORZ3jn7v/cC7qHOfunuN67HXuJtwtULCSmb+HP+V9ylPeW71VLFaROqXqUMuvFp7OngZqheW7GZ3Irgt3+e3UTfZmCmgyZA4QzK0nx9qGbYUQhidBTQ4kqDEsRVF4mPKQu/F3uZd4jzRtGunadLSKFq2ixcHOAW8Xb7ydvfF28cbTyRN7O3tTV9viZMwgGtS8AkNXHcm13M43Q3BxtDfL6dPWOGwrhDAc2dBSGJ1Go8HL2QsvZy8qIbOPDCUqLoW/w6Lo26RcnuXStUq2gAZgd1gUE9efMulQj+w5JYQwhKItTyuEMJmMHcGdHfL+9U3XKjmuBwNqYBMVV7ihQiGEMHcS1AhhYTJ2BD9+LZoWlUvkWKZ1sC8JKWl5Ps/D/4IjIYSwFhLUCGFhMnYEX74ngsEtgrIFNhlJt96ueQ/veLoYd4sOIYQwNMmpETrmNktG5MzbzYlZPeswcf0pxqw5zpCWQQxpEQRA2WKulPZy0c16ah3sm+v0aV8PeW+FENZFZj8JQDYZtEQFmUEk06eFENZApnTnQIKanMkmg9ZNpk8LISydTOkWBZYxRTgnGbNk5CJouWT6tBDCVkiisNBNEc6NzJIRQghhCaSnRuimCOfGkmbJSLKzEELYLglqhG6KsKXPkpFkZyGEsG0y/CR0U4RbB/tmOa5b78QCejpiElLy3BIgJkFWzxVCCGtncT01ixYt4tNPP+X27dvUrVuXBQsW0KRJE1NXyyw8ydBLgI8rC/rWt9hZMpLsLIQQwqKCmh9//JHx48ezZMkSmjZtyhdffEHHjh05f/48fn5+pq6eSelj6MWSZ8lIsrMQQgiLGn76/PPPGT58OIMHD6ZGjRosWbIENzc3li9fnmP55ORkYmNjs9yskQy9WFeysxBCiKKxmKAmJSWFo0eP0r59e90xOzs72rdvz/79+3N8zMyZM/H29tbdAgMDjVVdoyrI0Is+xCSkEB4Zx/GrDwi/G2dWwVJGsnNOLCnZWQghRNFZTFATFRVFeno6pUqVynK8VKlS3L59O8fHTJo0iZiYGN3t2rVrxqiq0Rlj6OVmdCKj1xyn3ee7eOH/9tHus128vuY4N6MTn/i59cEakp2FEEI8GYvKqSksZ2dnnJ2dTV0NgzP00Et+w1vmso2CpSc7CyGEeDIWE9T4+vpib2/PnTt3shy/c+cOpUuXNlGtzIOh15mxpJlFlpzsLIQQ4slYzPCTk5MTDRs2ZMeOHbpjWq2WHTt20KxZMxPWzPQMPfRiyplF5pzHI4QQwrxYTE8NwPjx4xk4cCCNGjWiSZMmfPHFF8THxzN48GBTV83kDDn0YoyZRTmtsROfki4rBAshhCgwiwpq+vTpw927d/nggw+4ffs29erVY/PmzdmSh22VoYZeDD28ldMaOzN71OaPU7f4+6J55/EIIYQwHxYz/JRh9OjRXLlyheTkZA4ePEjTpk1NXSWrZ8jhrdySkP08nbMFNBn0OU1dZCXDfUIIS2ZRPTXCdAw1vJVbEnJymjbPx8kKwfonG4IKISydxfXUCNPxdnOikp8H9coVo5Kfh16Gf3JLQnZ2yPujKSsE65esSi2EsAYS1AiTyi0J+fi1aFpULpHjOVkhWP+MtSq1EEIYkgQ1wqRy295g+Z4IXn86WFYINhLZEFQIYQ0kp0aYVEYS8sT1p7LMrmpUvhgVirvJCsF6ktOU+cw/R9kQVAhhDSSoESaXXxKyBDFPpiAJwIaeti+EEMYgw0/CLBgiCVkUPAFYNgQVQlgD6akRwooVZt8u2RBUCGHpJKgxI/nlPQhRWIVNAJYNQYUQlkyCGjNhzIXPJHiyHZIALISwJRLUmIH88h70uc+RrBprWyQBWAhhSyRR2AwYa+EzWTXW9kgCsBDClkhPjRkw1sJnhUkaFdZDEoCFELZCghozYKy8B1k11nZJArAQwhbI8JMZyG2rANBv3oMkjQohhLBmEtSYAWPlPRgreBJCCCFMQaMoimLqShhLbGws3t7exMTE4OXlZerqZJMx1dqQeQ83oxOz7bOUETz5y+wnIYQQZqig12/JqTEjxsh7kKRRIYQQ1kqCGhskSaNCCCGskeTUCCGEEMIqSFAjhBBCCKsgQY0QQgghrIIENUIIIYSwChLUCCGEEMIqSFAjhBBCCKsgQY0QQgghrIIENUIIIYSwChLUCCGEEMIqSFAjhBBCCKsg2yQI3UaasUmpeLk64usu2ygIIYSwPBLU2Lib0YlMWH+Kvx/btXtWzzoEyK7dQgghLIgMP9mwmISUbAENwO6wKCauP0VMQoqJaiaEEEIUnvTU2LCouJRsAU2G3WFRRMWlmN0wlAyVCSGEyI0ENTYsNik1z/MP8zlvbDJUJoQQIi8y/GTDvFwc8zzvmc95Y5KhMiGEEPmxiKDm8uXLDB06lKCgIFxdXalUqRJTpkwhJUUuZE/C18OJ1sG+OZ5rHeyLr4f5DOsUZKhMCCGEbbOIoObff/9Fq9WydOlSzp49y9y5c1myZAnvvvuuqatm0bzdnJjVs062wKZ1sC+ze9Yxq1wVSxsqE0IIYXwWkVPTqVMnOnXqpLtfsWJFzp8/z+LFi5kzZ44Ja2b5AnxcWdC3PlFxKTxMSsXTxRFfD/NLvrWkoTIhhBCmYRFBTU5iYmIoXrx4nmWSk5NJTk7W3Y+NjTV0tSySt5v5BTGPyxgq253DEJS5DZUJIYQwDYsYfnrcxYsXWbBgASNHjsyz3MyZM/H29tbdAgMDjVRDoW+WNFQmhBDCNDSKoiimevGJEycye/bsPMucO3eOatWq6e7fuHGDkJAQ2rRpw1dffZXnY3PqqQkMDCQmJgYvL68nq7wwiYx1asx5qEwIIYR+xcbG4u3tne/126RBzd27d7l3716eZSpWrIiTk3rRunnzJm3atOGpp55i5cqV2NkVrqOpoD8U8eRkkTwhhBD6UtDrt0lzakqWLEnJkiULVPbGjRu0bduWhg0bsmLFikIHNMJ4ZJE8IYQQpmARkcGNGzdo06YN5cqVY86cOdy9e5fbt29z+/ZtU1dNPEYWyRNCCGEqFjH7adu2bVy8eJGLFy9StmzZLOdMOHomcmCJ+0kJIYSwDhbRUzNo0CAURcnxJsyLLJInhBDCVCwiqBGWQxbJE0IIYSoS1Ai9sqT9pIQQQlgXCWqEXskieUIIIUzFIhKFhWWxlP2khBBCWBcJaoRBWMJ+UkIIIayLDD8JIYQQwipIT40QBiDbRAghhPFJUCOEnsk2EUIIYRoy/CSEHsk2EUIIYToS1AihRwXZJkIIIYRhSFAjhB7JNhFCCGE6EtQIoUeyTYQQQpiOBDVC6JFsEyGEEKYjQY0QeiTbRAghhOnIlG4h9Ey2iRBCCNOQoEYIA5BtIoQQwvhk+EkIIYQQVkGCGiGEEEJYBQlqhBBCCGEVJKgRQgghhFWQoEYIIYQQVkGCGiGEEEJYBQlqhBBCCGEVJKgRQgghhFWQoEYIIYQQVkGCGiGEEEJYBZvaJkFRFABiY2NNXBMhhBBCFFTGdTvjOp4bmwpqHj58CEBgYKCJayKEEEKIwnr48CHe3t65ntco+YU9VkSr1XLz5k08PT3RaDSFemxsbCyBgYFcu3YNLy8vA9XQ9GyhnbbQRrCNdtpCG8E22mkLbQTbaKch2qgoCg8fPiQgIAA7u9wzZ2yqp8bOzo6yZcs+0XN4eXlZ7QcxM1topy20EWyjnbbQRrCNdtpCG8E22qnvNubVQ5NBEoWFEEIIYRUkqBFCCCGEVZCgpoCcnZ2ZMmUKzs7Opq6KQdlCO22hjWAb7bSFNoJttNMW2gi20U5TttGmEoWFEEIIYb2kp0YIIYQQVkGCGiGEEEJYBQlqhBBCCGEVJKgRQgghhFWw6aBm0aJFVKhQARcXF5o2bcqhQ4fyLP/TTz9RrVo1XFxcqF27Nn/88UeW84qi8MEHH+Dv74+rqyvt27cnLCzMkE3IV2Ha+OWXX9KqVSuKFStGsWLFaN++fbbygwYNQqPRZLl16tTJ0M3IV2HauXLlymxtcHFxyVLG0t/LNm3aZGujRqOha9euujLm+F7u3r2bbt26ERAQgEajYePGjfk+JjQ0lAYNGuDs7EzlypVZuXJltjKF/V03pMK28eeff6ZDhw6ULFkSLy8vmjVrxpYtW7KUmTp1arb3slq1agZsRf4K287Q0NAcP7O3b9/OUs6S38ucfuc0Gg01a9bUlTG393LmzJk0btwYT09P/Pz86N69O+fPn8/3caa6XtpsUPPjjz8yfvx4pkyZwrFjx6hbty4dO3YkMjIyx/L79u2jb9++DB06lOPHj9O9e3e6d+/OmTNndGU++eQT5s+fz5IlSzh48CDu7u507NiRpKQkYzUri8K2MTQ0lL59+/LXX3+xf/9+AgMDeeaZZ7hx40aWcp06deLWrVu625o1a4zRnFwVtp2grnSZuQ1XrlzJct7S38uff/45S/vOnDmDvb09vXv3zlLO3N7L+Ph46taty6JFiwpUPiIigq5du9K2bVtOnDjBuHHjGDZsWJaLflE+H4ZU2Dbu3r2bDh068Mcff3D06FHatm1Lt27dOH78eJZyNWvWzPJe7tmzxxDVL7DCtjPD+fPns7TDz89Pd87S38t58+Zladu1a9coXrx4tt9Lc3ovd+3axahRozhw4ADbtm0jNTWVZ555hvj4+FwfY9LrpWKjmjRpoowaNUp3Pz09XQkICFBmzpyZY/kXX3xR6dq1a5ZjTZs2VUaOHKkoiqJotVqldOnSyqeffqo7Hx0drTg7Oytr1qwxQAvyV9g2Pi4tLU3x9PRUVq1apTs2cOBA5fnnn9d3VZ9IYdu5YsUKxdvbO9fns8b3cu7cuYqnp6cSFxenO2aO72VmgLJhw4Y8y7zzzjtKzZo1sxzr06eP0rFjR939J/3ZGVJB2piTGjVqKB9++KHu/pQpU5S6devqr2J6VpB2/vXXXwqgPHjwINcy1vZebtiwQdFoNMrly5d1x8z9vYyMjFQAZdeuXbmWMeX10iZ7alJSUjh69Cjt27fXHbOzs6N9+/bs378/x8fs378/S3mAjh076spHRERw+/btLGW8vb1p2rRprs9pSEVp4+MSEhJITU2lePHiWY6Hhobi5+dH1apVefXVV7l3755e614YRW1nXFwc5cuXJzAwkOeff56zZ8/qzlnje/n111/z0ksv4e7unuW4Ob2XRZHf76U+fnbmRqvV8vDhw2y/l2FhYQQEBFCxYkX69+/P1atXTVTDJ1OvXj38/f3p0KEDe/fu1R23xvfy66+/pn379pQvXz7LcXN+L2NiYgCyff4yM+X10iaDmqioKNLT0ylVqlSW46VKlco2fpvh9u3beZbP+Lcwz2lIRWnj4yZMmEBAQECWD16nTp345ptv2LFjB7Nnz2bXrl107tyZ9PR0vda/oIrSzqpVq7J8+XI2bdrEd999h1arpXnz5ly/fh2wvvfy0KFDnDlzhmHDhmU5bm7vZVHk9nsZGxtLYmKiXn4PzM2cOXOIi4vjxRdf1B1r2rQpK1euZPPmzSxevJiIiAhatWrFw4cPTVjTwvH392fJkiWsX7+e9evXExgYSJs2bTh27Bign79p5uTmzZv8+eef2X4vzfm91Gq1jBs3jhYtWlCrVq1cy5nyemlTu3SLgps1axY//PADoaGhWZJoX3rpJd3/a9euTZ06dahUqRKhoaG0a9fOFFUttGbNmtGsWTPd/ebNm1O9enWWLl3KtGnTTFgzw/j666+pXbs2TZo0yXLcGt5LW/P999/z4YcfsmnTpiy5Jp07d9b9v06dOjRt2pTy5cuzdu1ahg4daoqqFlrVqlWpWrWq7n7z5s0JDw9n7ty5fPvttyasmWGsWrUKHx8funfvnuW4Ob+Xo0aN4syZMybP18qLTfbU+Pr6Ym9vz507d7Icv3PnDqVLl87xMaVLl86zfMa/hXlOQypKGzPMmTOHWbNmsXXrVurUqZNn2YoVK+Lr68vFixefuM5F8STtzODo6Ej9+vV1bbCm9zI+Pp4ffvihQH8MTf1eFkVuv5deXl64urrq5fNhLn744QeGDRvG2rVrs3XtP87Hx4cqVapY1HuZkyZNmujaYE3vpaIoLF++nAEDBuDk5JRnWXN5L0ePHs1vv/3GX3/9RdmyZfMsa8rrpU0GNU5OTjRs2JAdO3bojmm1Wnbs2JHlG3xmzZo1y1IeYNu2bbryQUFBlC5dOkuZ2NhYDh48mOtzGlJR2ghqRvq0adPYvHkzjRo1yvd1rl+/zr179/D399dLvQurqO3MLD09ndOnT+vaYC3vJajTKpOTk3n55ZfzfR1Tv5dFkd/vpT4+H+ZgzZo1DB48mDVr1mSZlp+buLg4wsPDLeq9zMmJEyd0bbCW9xLUGUUXL14s0JcNU7+XiqIwevRoNmzYwM6dOwkKCsr3MSa9Xj5RmrEF++GHHxRnZ2dl5cqVyj///KOMGDFC8fHxUW7fvq0oiqIMGDBAmThxoq783r17FQcHB2XOnDnKuXPnlClTpiiOjo7K6dOndWVmzZql+Pj4KJs2bVJOnTqlPP/880pQUJCSmJho9PYpSuHbOGvWLMXJyUlZt26dcuvWLd3t4cOHiqIoysOHD5W33npL2b9/vxIREaFs375dadCggRIcHKwkJSWZpI2KUvh2fvjhh8qWLVuU8PBw5ejRo8pLL72kuLi4KGfPntWVsfT3MkPLli2VPn36ZDturu/lw4cPlePHjyvHjx9XAOXzzz9Xjh8/rly5ckVRFEWZOHGiMmDAAF35S5cuKW5ubsrbb7+tnDt3Tlm0aJFib2+vbN68WVcmv5+dsRW2jatXr1YcHByURYsWZfm9jI6O1pV58803ldDQUCUiIkLZu3ev0r59e8XX11eJjIw0evsyFLadc+fOVTZu3KiEhYUpp0+fVsaOHavY2dkp27dv15Wx9Pcyw8svv6w0bdo0x+c0t/fy1VdfVby9vZXQ0NAsn7+EhARdGXO6XtpsUKMoirJgwQKlXLlyipOTk9KkSRPlwIEDunMhISHKwIEDs5Rfu3atUqVKFcXJyUmpWbOm8vvvv2c5r9VqlcmTJyulSpVSnJ2dlXbt2innz583RlNyVZg2li9fXgGy3aZMmaIoiqIkJCQozzzzjFKyZEnF0dFRKV++vDJ8+HCT/UHJrDDtHDdunK5sqVKllC5duijHjh3L8nyW/l4qiqL8+++/CqBs3bo123OZ63uZMa338VtG2wYOHKiEhIRke0y9evUUJycnpWLFisqKFSuyPW9ePztjK2wbQ0JC8iyvKOo0dn9/f8XJyUkpU6aM0qdPH+XixYvGbdhjCtvO2bNnK5UqVVJcXFyU4sWLK23atFF27tyZ7Xkt+b1UFHXqsqurq7Js2bIcn9Pc3suc2gdk+T0zp+ul5r9KCyGEEEJYNJvMqRFCCCGE9ZGgRgghhBBWQYIaIYQQQlgFCWqEEEIIYRUkqBFCCCGEVZCgRgghhBBWQYIaIYQQQlgFCWqEEEIIYRUkqBFCCCGEVZCgRgghhBBWQYIaIYQQQlgFCWqEEBbr7t27lC5dmhkzZuiO7du3DycnJ3bs2GHCmgkhTEE2tBRCWLQ//viD7t27s2/fPqpWrUq9evV4/vnn+fzzz01dNSGEkUlQI4SweKNGjWL79u00atSI06dPc/jwYZydnU1dLSGEkUlQI4SweImJidSqVYtr165x9OhRateubeoqCSFMQHJqhBAWLzw8nJs3b6LVarl8+bKpqyOEMBHpqRFCWLSUlBSaNGlCvXr1qFq1Kl988QWnT5/Gz8/P1FUTQhiZBDVCCIv29ttvs27dOk6ePImHhwchISF4e3vz22+/mbpqQggjk+EnIYTFCg0N5YsvvuDbb7/Fy8sLOzs7vv32W/7++28WL15s6uoJIYxMemqEEEIIYRWkp0YIIYQQVkGCGiGEEEJYBQlqhBBCCGEVJKgRQgghhFWQoEYIIYQQVkGCGiGEEEJYBQlqhBBCCGEVJKgRQgghhFWQoEYIIYQQVkGCGiGEEEJYBQlqhBBCCGEV/h/A6r8XhDorTAAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -4525,13 +5553,7 @@
}
],
"source": [
- "beta6 = poly6_model.params\n",
- "X6_grid = np.stack([x_grid**p for p in range(7)], axis=1)\n",
- "y6_grid = np.dot(X6_grid, beta6)\n",
- "ax = sns.scatterplot(x='x', y='y', data=df, label='observations')\n",
- "ax.plot(x, y_th, 'r-', label='true')\n",
- "ax.plot(x_grid, y6_grid, 'g-', label='predicted')\n",
- "ax.legend();"
+ "statsmodels_material.illustration_nonlinear_regression(df, y_th, poly6_model, 6)"
]
},
{
@@ -4546,7 +5568,7 @@
},
{
"cell_type": "code",
- "execution_count": 81,
+ "execution_count": 90,
"id": "74c19b10",
"metadata": {
"hidden": true
@@ -4616,7 +5638,7 @@
"6 0.663161 0.641430 -206.537608 427.075215 445.311406"
]
},
- "execution_count": 81,
+ "execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
@@ -4630,42 +5652,6 @@
"scores"
]
},
- {
- "cell_type": "code",
- "execution_count": 82,
- "id": "572d19c8",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": [
- "# we need a few helpers to automate the parameter search\n",
- "\n",
- "def poly(x, order):\n",
- " return np.stack([x**k for k in range(order+1)], axis=1)\n",
- "\n",
- "def fit(x, y, order):\n",
- " return sm.OLS(y, poly(x, order)).fit()\n",
- "\n",
- "models = {k: fit(x, y, k) for k in range(1, 7)}\n",
- "\n",
- "def predict(model, x, order):\n",
- " X = poly(x, order)\n",
- " beta = model.params\n",
- " y_pred = np.dot(X, beta)\n",
- " return y_pred\n",
- "\n",
- "def sum_of_squares(y_predicted, y_expected=None):\n",
- " y_ = np.mean(y_predicted) if y_expected is None else y_expected\n",
- " y_ = y_predicted - y_\n",
- " return np.dot(y_, y_)\n",
- "\n",
- "def R2(y_predicted, y_expected):\n",
- " residual_ss = sum_of_squares(y_predicted, y_expected)\n",
- " total_ss = sum_of_squares(y_expected)\n",
- " return 1 - residual_ss / total_ss"
- ]
- },
{
"cell_type": "markdown",
"id": "648b703e",
@@ -4678,7 +5664,7 @@
},
{
"cell_type": "code",
- "execution_count": 83,
+ "execution_count": 91,
"id": "42c8cdf3",
"metadata": {
"hidden": true
@@ -4690,7 +5676,7 @@
},
{
"cell_type": "code",
- "execution_count": 84,
+ "execution_count": 92,
"id": "3131de8c",
"metadata": {
"hidden": true
@@ -4698,7 +5684,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -4708,21 +5694,7 @@
}
],
"source": [
- "R2_train_data = {}\n",
- "R2_test_data = {}\n",
- "for order in models:\n",
- " trained_model = models[order]\n",
- " y_pred = predict(trained_model, x_test, order)\n",
- " R2_train_data[order] = trained_model.rsquared\n",
- " R2_test_data[order] = R2(y_pred, y_test)\n",
- " \n",
- "ax = plt.gca()\n",
- "ax.plot(list(R2_train_data.keys()), list(R2_train_data.values()), 'b-', label='train')\n",
- "ax.plot(list(R2_test_data.keys()), list(R2_test_data.values()), 'g-', label='test')\n",
- "ax.set_xlabel('polynomial order')\n",
- "ax.set_ylabel('$R^2$')\n",
- "ax.axvline(3, color='r', linestyle=':', linewidth=1, label='true')\n",
- "ax.legend();"
+ "statsmodels_material.illustration_R2_poly(x, y, x_test, y_test)"
]
},
{
@@ -4785,7 +5757,7 @@
},
{
"cell_type": "code",
- "execution_count": 85,
+ "execution_count": 93,
"id": "43ac886e",
"metadata": {
"hidden": true
@@ -4855,7 +5827,7 @@
"6 0.663161 0.641430 -206.537608 427.075215 445.311406"
]
},
- "execution_count": 85,
+ "execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
@@ -4892,7 +5864,7 @@
},
{
"cell_type": "code",
- "execution_count": 86,
+ "execution_count": 94,
"id": "60322cd4",
"metadata": {
"hidden": true
@@ -4900,7 +5872,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -4910,38 +5882,7 @@
}
],
"source": [
- "AIC = {}\n",
- "BIC = {}\n",
- "for order in models:\n",
- " trained_model = models[order]\n",
- " AIC[order] = trained_model.aic\n",
- " BIC[order] = trained_model.bic\n",
- "\n",
- "ax = plt.gca()\n",
- "\n",
- "orders, criteria = list(AIC.keys()), list(AIC.values())\n",
- "ax.plot(orders, criteria, 'b-', label='AIC')\n",
- "k = np.argmin(criteria)\n",
- "ax.plot(orders[k], criteria[k], 'ro', markerfacecolor='none')\n",
- "ax.annotate('AIC best candidate',\n",
- " xy=(orders[k], criteria[k]),\n",
- " xytext=(orders[k]-.2, criteria[k]+6),\n",
- " arrowprops=dict(arrowstyle=\"->\"),\n",
- ")\n",
- "\n",
- "orders, criteria = list(BIC.keys()), list(BIC.values())\n",
- "ax.plot(orders, criteria, 'g-', label='BIC')\n",
- "k = np.argmin(criteria)\n",
- "ax.plot(orders[k], criteria[k], 'ro', markerfacecolor='none')\n",
- "ax.annotate('BIC best candidate',\n",
- " xy=(orders[k], criteria[k]),\n",
- " xytext=(orders[k]-.6, criteria[k]+7),\n",
- " arrowprops=dict(arrowstyle=\"->\"),\n",
- ")\n",
- "\n",
- "ax.set_xlabel('polynomial order')\n",
- "ax.axvline(3, color='r', linestyle=':', linewidth=1, label='true')\n",
- "ax.legend();"
+ "statsmodels_material.illustration_AIC_BIC_poly(x, y)"
]
},
{
@@ -5038,7 +5979,7 @@
},
{
"cell_type": "code",
- "execution_count": 87,
+ "execution_count": 95,
"id": "c906df73",
"metadata": {
"hidden": true
@@ -5077,7 +6018,7 @@
},
{
"cell_type": "code",
- "execution_count": 88,
+ "execution_count": 96,
"id": "42165fc7",
"metadata": {
"hidden": true
@@ -5112,7 +6053,7 @@
},
{
"cell_type": "code",
- "execution_count": 89,
+ "execution_count": 97,
"id": "fcf2d971",
"metadata": {
"hidden": true
@@ -5135,15 +6076,15 @@
},
{
"cell_type": "code",
- "execution_count": 90,
+ "execution_count": 98,
"id": "9c495f24",
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
- "family = sm.families.Gamma(sm.families.links.log())\n",
- "family = sm.families.InverseGaussian(sm.families.links.log())\n",
+ "family = sm.families.Gamma(sm.families.links.Log())\n",
+ "family = sm.families.InverseGaussian(sm.families.links.Log())\n",
"# the log link function must be specified, as the log link function is not default"
]
},
@@ -5170,7 +6111,7 @@
},
{
"cell_type": "code",
- "execution_count": 91,
+ "execution_count": 99,
"id": "01e6b10d-fc3a-4fe8-9f94-b3ff3a1278ea",
"metadata": {
"hidden": true
@@ -5314,7 +6255,7 @@
"4 0 373450 8.0500 NaN S "
]
},
- "execution_count": 91,
+ "execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
@@ -5326,7 +6267,7 @@
},
{
"cell_type": "code",
- "execution_count": 92,
+ "execution_count": 100,
"id": "e959c8a9",
"metadata": {
"hidden": true
@@ -5353,10 +6294,10 @@
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -323.64</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Date:</th> <td>Mon, 26 Sep 2022</td> <th> Deviance: </th> <td> 647.28</td>\n",
+ " <th>Date:</th> <td>Mon, 21 Aug 2023</td> <th> Deviance: </th> <td> 647.28</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Time:</th> <td>01:44:44</td> <th> Pearson chi2: </th> <td> 767.</td> \n",
+ " <th>Time:</th> <td>16:37:53</td> <th> Pearson chi2: </th> <td> 767.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>5</td> <th> Pseudo R-squ. (CS):</th> <td>0.3587</td> \n",
@@ -5386,6 +6327,34 @@
"</tr>\n",
"</table>"
],
+ "text/latex": [
+ "\\begin{center}\n",
+ "\\begin{tabular}{lclc}\n",
+ "\\toprule\n",
+ "\\textbf{Dep. Variable:} & Survived & \\textbf{ No. Observations: } & 714 \\\\\n",
+ "\\textbf{Model:} & GLM & \\textbf{ Df Residuals: } & 709 \\\\\n",
+ "\\textbf{Model Family:} & Binomial & \\textbf{ Df Model: } & 4 \\\\\n",
+ "\\textbf{Link Function:} & Logit & \\textbf{ Scale: } & 1.0000 \\\\\n",
+ "\\textbf{Method:} & IRLS & \\textbf{ Log-Likelihood: } & -323.64 \\\\\n",
+ "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Deviance: } & 647.28 \\\\\n",
+ "\\textbf{Time:} & 16:37:53 & \\textbf{ Pearson chi2: } & 767. \\\\\n",
+ "\\textbf{No. Iterations:} & 5 & \\textbf{ Pseudo R-squ. (CS):} & 0.3587 \\\\\n",
+ "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\\begin{tabular}{lcccccc}\n",
+ " & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
+ "\\midrule\n",
+ "\\textbf{Intercept} & 3.7770 & 0.401 & 9.416 & 0.000 & 2.991 & 4.563 \\\\\n",
+ "\\textbf{C(Pclass)[T.2]} & -1.3098 & 0.278 & -4.710 & 0.000 & -1.855 & -0.765 \\\\\n",
+ "\\textbf{C(Pclass)[T.3]} & -2.5806 & 0.281 & -9.169 & 0.000 & -3.132 & -2.029 \\\\\n",
+ "\\textbf{C(Sex)[T.male]} & -2.5228 & 0.207 & -12.164 & 0.000 & -2.929 & -2.116 \\\\\n",
+ "\\textbf{Age} & -0.0370 & 0.008 & -4.831 & 0.000 & -0.052 & -0.022 \\\\\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "%\\caption{Generalized Linear Model Regression Results}\n",
+ "\\end{center}"
+ ],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
@@ -5396,8 +6365,8 @@
"Model Family: Binomial Df Model: 4\n",
"Link Function: Logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -323.64\n",
- "Date: Mon, 26 Sep 2022 Deviance: 647.28\n",
- "Time: 01:44:44 Pearson chi2: 767.\n",
+ "Date: Mon, 21 Aug 2023 Deviance: 647.28\n",
+ "Time: 16:37:53 Pearson chi2: 767.\n",
"No. Iterations: 5 Pseudo R-squ. (CS): 0.3587\n",
"Covariance Type: nonrobust \n",
"==================================================================================\n",
@@ -5412,7 +6381,7 @@
"\"\"\""
]
},
- "execution_count": 92,
+ "execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
@@ -5435,7 +6404,7 @@
},
{
"cell_type": "code",
- "execution_count": 93,
+ "execution_count": 101,
"id": "797dabea",
"metadata": {
"hidden": true
@@ -5447,7 +6416,7 @@
"657.2831255018241"
]
},
- "execution_count": 93,
+ "execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
@@ -5463,66 +6432,7 @@
"hidden": true
},
"source": [
- "However, the traditional analysis of variance is no longer available:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 94,
- "id": "42a99e49",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": [
- "%%script false --no-raise-error\n",
- "\n",
- "sm.stats.anova_lm(model)\n",
- "# raises:\n",
- "\n",
- "# > AttributeError: 'GLMResults' object has no attribute 'ssr'"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3fc5e6d3",
- "metadata": {
- "hidden": true
- },
- "source": [
- "As a consequence, the contribution of the factors or descriptors is often discussed in the light of the coefficients and *p*-values shown in the second table. Beware the [correlations between quantitative descriptors](https://scikit-learn.org/stable/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html), though.\n",
- "\n",
- "Other approaches are possible, that compare between models (with and without a chosen factor/descriptor) drawing multiple subsets from the dataset. However, these approaches need to be tailored to the data and problem, and we won't cover them here."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4d16690d-a3b2-4903-b746-04ce95030288",
- "metadata": {
- "heading_collapsed": true,
- "tags": []
- },
- "source": [
- "## Hierarchical models"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e9847067-50f5-4271-a3ef-440ca555ee82",
- "metadata": {
- "hidden": true
- },
- "source": [
- "The notion of hierarchy in an analysis of variance arises when not all factors or dependent variables have equal importance in the analysis.\n",
- "\n",
- "* repeated measurements on the same sample;\n",
- "* nested designs (student < class < school);\n",
- "* crossed designs;\n",
- "* etc.\n",
- "\n",
- "We cannot cover all cases and variations. Especially, the field of mixed-effects models is too vast to even give an introduction.\n",
- "\n",
- "A key point is to be careful whether your observations are independent and were sampled randomly ([good read](https://online.stat.psu.edu/onlinecourses/sites/stat503/files/lesson14/recognize_split_plot_experiment.pdf))."
+ "Main effects and pairwise differences can be tested using Wald test as already shown."
]
},
{
@@ -5532,7 +6442,7 @@
"hidden": true
},
"source": [
- "### Repeated measures ANOVA and sphericity"
+ "## Repeated-measures ANOVA and sphericity"
]
},
{
@@ -5559,7 +6469,7 @@
},
{
"cell_type": "code",
- "execution_count": 95,
+ "execution_count": 102,
"id": "bea2319c",
"metadata": {
"hidden": true
@@ -5665,7 +6575,7 @@
"31 2 Post Product 6"
]
},
- "execution_count": 95,
+ "execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
@@ -5699,24 +6609,32 @@
"hidden": true
},
"source": [
- "`statsmodels` features [AnovaRM](https://www.statsmodels.org/stable/generated/statsmodels.stats.anova.AnovaRM.html) but corrections for departure from sphericity are not implemented and we should first perform a Mauchly's test for sphericity, for example with [pingouin.sphericity](https://pingouin-stats.org/generated/pingouin.sphericity.html):"
+ "statsmodels features [AnovaRM](https://www.statsmodels.org/stable/generated/statsmodels.stats.anova.AnovaRM.html) but corrections for departure from sphericity are not implemented and we should first perform a Mauchly's test for sphericity, for example with [pingouin.sphericity](https://pingouin-stats.org/generated/pingouin.sphericity.html):"
]
},
{
"cell_type": "code",
- "execution_count": 96,
+ "execution_count": 103,
"id": "a7df0d24",
"metadata": {
"hidden": true
},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_2421/2299974535.py:1: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.\n",
+ " pg.sphericity(data, dv='Performance', subject='Subject', within=['Time', 'Metric'])\n"
+ ]
+ },
{
"data": {
"text/plain": [
"SpherResults(spher=True, W=0.6247989838343564, chi2=3.762602454747652, dof=2, pval=0.15239168046050933)"
]
},
- "execution_count": 96,
+ "execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
@@ -5727,7 +6645,7 @@
},
{
"cell_type": "code",
- "execution_count": 97,
+ "execution_count": 104,
"id": "6a8362eb",
"metadata": {
"hidden": true
@@ -5793,7 +6711,7 @@
"Time:Metric 12.63227 2.0 18.0 0.000373"
]
},
- "execution_count": 97,
+ "execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
@@ -5811,12 +6729,12 @@
"hidden": true
},
"source": [
- "In contrast, [rm_anova](https://pingouin-stats.org/generated/pingouin.rm_anova.html) from `pingouin` does implement Greenhouse-Geiser correction."
+ "In contrast, [rm_anova](https://pingouin-stats.org/generated/pingouin.rm_anova.html) from pingouin does implement Greenhouse-Geiser correction."
]
},
{
"cell_type": "code",
- "execution_count": 98,
+ "execution_count": 105,
"id": "a66cb913",
"metadata": {
"hidden": true
@@ -5911,7 +6829,7 @@
"2 0.001708 0.084420 0.727166 "
]
},
- "execution_count": 98,
+ "execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
@@ -5927,27 +6845,15 @@
"hidden": true
},
"source": [
- "Note that neither `AnovaRM` nor `rm_anova` give access to the model's coefficients.\n",
- "\n",
"Mixed effects models are increasingly popular and preferred over the standard repeated measures ANOVA, especially because sphericity simply cannot be expected from the data in most cases."
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4de57a31",
- "metadata": {
- "hidden": true
- },
- "outputs": [],
- "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "scientific_python",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "scientific_python"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -5959,7 +6865,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.4"
+ "version": "3.10.12"
},
"toc": {
"base_numbering": 1,
diff --git a/notebooks/statsmodels_material.py b/notebooks/statsmodels_material.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ba6e9fd8515ad9c798c18ee9a050a87eb6021f2
--- /dev/null
+++ b/notebooks/statsmodels_material.py
@@ -0,0 +1,257 @@
+import numpy as np
+import matplotlib
+from matplotlib import pyplot as plt
+import seaborn as sns
+import statsmodels.api as sm
+from scipy import stats
+
+def illustration_residuals(dataframe, fitted_model, response='Y', factor='Group'):
+ _, axes = plt.subplots(1, 2)
+ sns.boxplot(x=factor, y=response, data=dataframe, ax=axes[0])
+ ax = axes[1]
+ sns.boxplot(x=dataframe[factor], y=fitted_model.resid, ax=ax)
+ ax.set_ylabel('residuals')
+ ax.yaxis.set_label_position('right')
+ ax.yaxis.tick_right()
+ ax.axhline(0, linestyle='--', color='red', linewidth=1);
+
+design_matrix_to_str = lambda g: repr(g.data.obj)
+
+def side_by_side(*args, sep=' | '):
+ """
+ Horizontally concatenate multiline string representations of objects.
+
+ Example:
+
+ >>> import numpy as np
+ >>> rng = np.random.default_rng(seed=1)
+ >>> a = rng.integers(10, size=(5,3))
+ >>> b = rng.random(size=(6,1))
+ >>> c = rng.choice(list('abcd'), size=(4,2))
+ >>> print(side_by_side(str(a), str(b), str(c)))
+ [[4 5 7] | [[0.54959369] | [['b' 'a']
+ [9 0 1] | [0.02755911] | ['b' 'a']
+ [8 9 2] | [0.75351311] | ['b' 'd']
+ [3 8 4] | [0.53814331] | ['a' 'b']]
+ [2 8 2]] | [0.32973172] |
+ | [0.7884287 ]] |
+ >>>
+ """
+ strs = [ design_matrix_to_str(arg) for arg in args ]
+ row_series = [ s.split('\n') for s in strs ]
+ nrows = max([ len(rows) for rows in row_series ])
+ padded_row_series = []
+ for rows in row_series:
+ max_len = max([len(row) for row in rows])
+ padded_rows = []
+ r = 0
+ for r, row in enumerate(rows):
+ padded_rows.append(row + ' '*(max_len-len(row)))
+ empty_row = ' '*max_len
+ r += 1
+ while r < nrows:
+ padded_rows.append(empty_row)
+ r += 1
+ padded_row_series.append(padded_rows)
+ concatenated_rows = [ sep.join(rows) for rows in zip(*padded_row_series) ]
+ return '\n'.join(concatenated_rows)
+
+def illustration_2way_data(data, response='height', factorA='sun', factorB='water'):
+ sns.boxplot(data=data, x=factorA, y=response, hue=factorB)
+ ax = sns.swarmplot(data=data, x=factorA, y=response, hue=factorB, dodge=True, palette='dark:k');
+ ax.legend([], frameon=False);
+ # redraw the legend
+ import matplotlib
+ colored_patches = [ child for child in ax.get_children() if isinstance(child, matplotlib.patches.Rectangle) ][:-1]
+ ax.legend(colored_patches, [ patch.get_label() for patch in colored_patches ], title='water');
+
+def interaction_plot(data, response='height', factorA='water', factorB='sun'):
+ ax = sns.swarmplot(data=data, x=factorA, y=response, hue=factorB);
+
+ # get the colors used by swarmplot, to tell interaction_plot which colors to use;
+ # and get the objects drawn by swarmplot to redraw the legend after calling interaction_plot
+ sun_levels = np.unique(data[factorB])
+ colors = {}
+ colored_points = []
+ for child in ax.get_children():
+ if child.get_label() in sun_levels:
+ colors[child.get_label()] = child.get_facecolor()
+ colored_points.append(child)
+
+ # interaction plot
+ from statsmodels.graphics.factorplots import interaction_plot
+ colors = [ colors[sun] for sun in np.sort(sun_levels) ]
+ interaction_plot(x=data[factorA], trace=data[factorB], response=data[response],
+ ax=ax, colors=colors, markers='d'*len(sun_levels), markerfacecolor='w');
+
+ # redraw the legend to remove the duplicates from interaction_plot
+ ax.legend(colored_points, [ points.get_label() for points in colored_points ], title=factorB);
+
+def illustration_multiple_comparisons(power=0.8, type1_error_rate=0.05):
+ true_grid = np.zeros((20, 60), dtype=bool)
+ true_grid[:10,-10:] = True
+
+ rejection_grid = np.array([[ np.random.rand() <= type1_error_rate for _ in range(60) ] for _ in range(20)])
+ rejection_grid[:10,-10:] = [[ np.random.rand() <= power for _ in range(10)] for _ in range(10)]
+
+ _, axes = plt.subplots(1, 2, figsize=(13.3,4.1))
+ for ax, title, grid in zip(axes[::-1], ('true', 'observed (actual test results)'), (true_grid, rejection_grid)):
+ ax.imshow(grid, cmap='seismic')
+ ax.set_title(title)
+ ax.axis("off");
+
+def confidence_intervals(all_comparisons):
+ y = 0
+ post_hoc_tests = all_comparisons[['coef', 'Conf. Int. Low', 'Conf. Int. Upp.', 'reject-hs']]
+ for y, contrast in enumerate(post_hoc_tests.index):
+ mean, lower_bound, upper_bound, reject = post_hoc_tests.loc[contrast]
+ plt.errorbar(mean, -y, lolims=True, xerr=[[mean-lower_bound], [upper_bound-mean]], yerr=0, linestyle='', c='red' if reject else 'black')
+ plt.text(mean, -y, contrast, ha='center', va='top')
+ plt.axvline(0, color='darkorange')
+ plt.yticks([]);
+
+def illustration_regression(patients, model, example_patient=173, response='Response', predictor='CHUK', scatter_label='Patient', line_label='Model prediction'):
+ ax = sns.scatterplot(data=patients, x=predictor, y=response, label=scatter_label)
+ sm.graphics.abline_plot(model_results=model, ax=ax, label=line_label)
+ plt.legend()
+
+ x = patients.loc[example_patient, predictor]
+ expected_value = patients.loc[example_patient, response]
+ predicted_value = model.fittedvalues[example_patient]
+ ax.plot([x, x], [predicted_value, expected_value], 'r-', zorder=0)
+ ax.plot(patients[predictor], model.fittedvalues, 'g+');
+
+def illustration_regression_residuals(patients, model, example_patient=173, response='Response', predictor='CHUK', scatter_label='Patient', line_label='Model prediction'):
+ ax = sns.scatterplot(x=predictor, y='residuals', label=scatter_label,
+ data={predictor: patients[predictor], 'residuals': model.resid})
+ ax.axhline(0, linestyle=':', lw=1, label=line_label)
+ plt.legend()
+
+ x = patients.loc[example_patient, predictor]
+ example_patient_residual = model.resid[example_patient]
+ ax.plot([x, x], [0, example_patient_residual], 'r-', zorder=0);
+
+def illustration_outlier(x, y, high_leverage_point, cooks_distant_point):
+ _, axes = plt.subplots(1, 2, figsize=(13.3,4.1))
+ for ax, influential_point in zip(axes, set([high_leverage_point, cooks_distant_point])):
+ sns.scatterplot(x=x, y=y, ax=ax)
+ sns.regplot(x=x, y=y, ax=ax, scatter=False, label=f'{influential_point:d} included')
+ selection = np.ones(len(x), dtype=bool)
+ selection[influential_point] = False
+ sns.regplot(x=x[selection], y=y[selection], scatter=False, ax=ax, label=f'{influential_point:d} excluded')
+ xi, yi = x[influential_point], y[influential_point]
+ ax.plot(xi, yi, 'r.', markersize=14)
+ ax.text(xi, yi, f'{influential_point:d}')
+ ax.legend()
+
+def illustration_monotonous_functions():
+ _, axes = plt.subplots(2, 3, figsize=(15, 8))
+
+ for ax, tr in zip(axes.T, (lambda x: 1./x, np.log, lambda x: x*x)):
+ x = np.linspace(1, 50, 30)
+ y = tr(x)
+ scale = y.max() - y.min()
+ y += .05 * scale * stats.norm.rvs(size=x.size)
+ x_grid = np.linspace(1, 50, 100)
+ ax[0].plot(x, y, 'b+')
+ ax[0].plot(x_grid, tr(x_grid), 'r-')
+ ax[1].plot(tr(x), y, 'b+')
+ ax[1].plot(tr(x_grid), tr(x_grid), 'r-')
+
+def illustration_nonlinear_regression(df, y_th, model, order):
+ x = df['x']
+ x_grid = np.linspace(x.min(), x.max(), 100)
+ if order == 2:
+ X_grid = np.stack((np.ones_like(x_grid), x_grid, x_grid**2), axis=1)
+ else:
+ X_grid = np.stack([x_grid**p for p in range(order+1)], axis=1)
+ # y_grid = model.predict(X_grid) # bug?
+ beta = model.params
+ y_grid = np.dot(X_grid, beta)
+ ax = sns.scatterplot(x='x', y='y', data=df, label='observations')
+ ax.plot(x, y_th, 'r-', label='true')
+ ax.plot(x_grid, y_grid, 'g-', label='predicted')
+ ax.legend();
+
+# support functions
+
+def poly(x, order):
+ return np.stack([x**k for k in range(order+1)], axis=1)
+
+def fit(x, y, order):
+ return sm.OLS(y, poly(x, order)).fit()
+
+def fit_models(x, y, order=6):
+ return {k: fit(x, y, k) for k in range(1, order+1)}
+
+def predict(model, x, order):
+ X = poly(x, order)
+ beta = model.params
+ y_pred = np.dot(X, beta)
+ return y_pred
+
+def sum_of_squares(y_predicted, y_expected=None):
+ y_ = np.mean(y_predicted) if y_expected is None else y_expected
+ y_ = y_predicted - y_
+ return np.dot(y_, y_)
+
+def R2(y_predicted, y_expected):
+ residual_ss = sum_of_squares(y_predicted, y_expected)
+ total_ss = sum_of_squares(y_expected)
+ return 1 - residual_ss / total_ss
+
+def illustration_R2_poly(x, y, x_test, y_test, order=6):
+ models = fit_models(x, y, order)
+
+ R2_train_data = {}
+ R2_test_data = {}
+ for order in models:
+ trained_model = models[order]
+ y_pred = predict(trained_model, x_test, order)
+ R2_train_data[order] = trained_model.rsquared
+ R2_test_data[order] = R2(y_pred, y_test)
+
+ ax = plt.gca()
+ ax.plot(list(R2_train_data.keys()), list(R2_train_data.values()), 'b-', label='train')
+ ax.plot(list(R2_test_data.keys()), list(R2_test_data.values()), 'g-', label='test')
+ ax.set_xlabel('polynomial order')
+ ax.set_ylabel('$R^2$')
+ ax.axvline(3, color='r', linestyle=':', linewidth=1, label='true')
+ ax.legend();
+
+def illustration_AIC_BIC_poly(x, y, order=6):
+ models = fit_models(x, y, order)
+
+ AIC = {}
+ BIC = {}
+ for order in models:
+ trained_model = models[order]
+ AIC[order] = trained_model.aic
+ BIC[order] = trained_model.bic
+
+ ax = plt.gca()
+
+ orders, criteria = list(AIC.keys()), list(AIC.values())
+ ax.plot(orders, criteria, 'b-', label='AIC')
+ k = np.argmin(criteria)
+ ax.plot(orders[k], criteria[k], 'ro', markerfacecolor='none')
+ ax.annotate('AIC best candidate',
+ xy=(orders[k], criteria[k]),
+ xytext=(orders[k]-.2, criteria[k]+6),
+ arrowprops=dict(arrowstyle="->"),
+ )
+
+ orders, criteria = list(BIC.keys()), list(BIC.values())
+ ax.plot(orders, criteria, 'g-', label='BIC')
+ k = np.argmin(criteria)
+ ax.plot(orders[k], criteria[k], 'ro', markerfacecolor='none')
+ ax.annotate('BIC best candidate',
+ xy=(orders[k], criteria[k]),
+ xytext=(orders[k]-.6, criteria[k]+7),
+ arrowprops=dict(arrowstyle="->"),
+ )
+
+ ax.set_xlabel('polynomial order')
+ ax.axvline(3, color='r', linestyle=':', linewidth=1, label='true')
+ ax.legend();
+