diff --git a/README.md b/README.md
index e994180a09fc1e77cadce5ce842c4fd2aea52553..0b32451444b5c2598da34ddaa65976c366ecab89 100644
--- a/README.md
+++ b/README.md
@@ -4,10 +4,10 @@
 2. Numpy
 3. Pandas
 5. Seaborn/Matplotlib
-6. Scipy
+6. Scipy/Pingouin
 7. Statsmodels
 8. Scikit-learn
-9. Interactivity intro 
+9. Interactivity intro
 
 ## Materials requirements
 
@@ -42,7 +42,7 @@ To reactivate the environment
 
 	# to run jupyter notenook server
 	jupyter-lab
-	
+
 ### from conda/mamba (recommended for windows guys)
 
 
@@ -52,10 +52,10 @@ Then create your environement (to do only once)
 	mamba env create -n Scientific_Python -c conda-forge bioconda
 	conda activate Scientific_Python
 
-	# get the course materials 
+	# get the course materials
 	git clone https://gitlab.pasteur.fr/hub-courses/scientific_python.git Scientific_Python_Course
     cd Scientific_Python_Course
-	
+
 	# install prerequisites
     mamba install --file requirements.txt
 
@@ -66,7 +66,7 @@ To exit from the conda environment/mamba
 To reactivate the environment
 
 	conda activate Scientific_Python
-	
+
 	# to run jupyter notebook server
 	jupyter-lab
 
diff --git a/notebooks/pingouin_scipy_TP.ipynb b/notebooks/pingouin_scipy_TP.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f5168fe7f9df1f14e48668b649ee72d6cd9ca9ca
--- /dev/null
+++ b/notebooks/pingouin_scipy_TP.ipynb
@@ -0,0 +1,575 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a5a5210d",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Import `numpy`, `pandas`, `pingouin`, `seaborn`, and the `stats` module from `scipy`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ac6cc32",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "529c5f56",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93ad4aaf",
+   "metadata": {},
+   "source": [
+    "# Descriptive statistics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e4fd0d9",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Load the `mi.csv` data file located in the `../data` directory into a DataFrame (the first column in the file is an index column), and inspect its content printing:\n",
+    "* the first rows, with column names,\n",
+    "* a summary table of all the variables,\n",
+    "* a summary table of the categorical variables only."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08c1dd12",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eefc126c",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04163591",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Inspect the relationship between variables `Age` and `OwnsHouse`. What type of plots is most suitable?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6baac23",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5de6412d",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf917039",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Draw a box plot (or violin plot) of variable `Age` for two categorical variables, say `OwnsHouse` and `LivesWithKids`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae6ca008",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06e98558",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e94c17b",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Isolate the house-owners group from the others, and report their mean age(s) as $99\\%$ confidence interval(s)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "497142f3",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55d18f16",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41b73555-f925-444a-a3dc-033407054810",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "# Tests on single variables\n",
+    "\n",
+    "Let us consider the logarithm of variable `HeartRate`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b2909a2b-2a63-43ef-914d-4f3050d73589",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['logHeartRate'] = np.log(df['HeartRate'])\n",
+    "sns.histplot(df, x='logHeartRate', hue='Sex');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5d6ea38-8582-4eb8-839e-64a4b6cf33e1",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "Draw Q-Q plots for variables `HeartRate` and `logHeartRate`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28a8b3a6-1765-440b-88af-a9a491964b12",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a74cc81e-d3a1-4d48-a2da-31479af531d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb1c9031-3e44-4598-926b-39634faf996f",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Perform an omnibus normality test (`normaltest`) on the `logHeartRate` variable for the different levels of variable `Sex`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5ea7bf3-b792-4a0b-8e18-f94c4c97ab94",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e3b3b74-4cac-4a5a-b071-9b8e279d5d3d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d61f454a",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Perform a Welch *t*-test on `logHeartRate` between males and females."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b076e8e6",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd70b092-f188-430e-8587-964607e88221",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbe5f273-8b48-464a-8fcf-6271fdf728f5",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "Instead of taking the log of `HeartRate` and perform a parametric *t*-test, we could have performed a non-parametric Mann-Whitney *U* Test, for example.\n",
+    "\n",
+    "Check we also find a difference between males and females' means with this latter test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d2cb07c-50b6-43e5-aee3-ba065052fd66",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4721b937-35e1-4fd3-bf9e-fe5401874ef1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7d98432",
+   "metadata": {},
+   "source": [
+    "# Comparing two distributions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f5453a9",
+   "metadata": {},
+   "source": [
+    "Now let proceed to comparing age between people living with kids and those living without kids."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0aeaeee7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='LivesWithKids', y='Age', data=df)\n",
+    "sns.swarmplot(x='LivesWithKids', y='Age', hue='LivesWithKids', data=df, linewidth=1, size=3, legend=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02cbfb3c",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Looking at the distributions, it does not make sense to compare the group means. Let us perform a two-sample goodness-of-fit test instead.\n",
+    "\n",
+    "Bin the two groups from 20 to 70 years (included) with 5-year-wide bins (hint: use Pandas' `cut` function) and proceed to performing a $\\chi^2$ test of homogeneity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4338fe92",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db11c1ed-8ba0-4c66-bcf3-48c88ded986e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "218a59ec",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Repeat the procedure with 10-year bins."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abdce59a",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e64b5ce",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "404b5a1d-4d24-497b-8a59-9e7b37d5f8fe",
+   "metadata": {},
+   "source": [
+    "# Multiway ANOVA\n",
+    "\n",
+    "## Q\n",
+    "\n",
+    "Explain variations in heart rate using age and sex as factors (beware: there is a trap!)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cd25a18-5f88-4b2b-9c42-a50eb9a28620",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1ae931b-5da2-4e9b-8019-8a1599e95970",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6dd75bf-aa69-4230-b65a-c0a347722ca6",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "We find a significant interaction while the effect of age fails to come up as significant by a short margin. Let us first draw an interaction plot."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb03922e-0914-429e-850a-b9247de0d642",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52c337c2-d5a0-4284-91d7-a11af70e5a9a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90404029-628c-49c9-a480-94609dcf75b2",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "For the purpose of performing multiple comparisons and some *p*-value correction, let us conduct separate $t$-tests for each age interval and organise the results into a dataframe.\n",
+    "\n",
+    "If comfortable enough with Python, define a \"Pingouin-like\" function `stratified_ttests` that takes a dataframe `data`, a dependent variable name `dv`, a between factor name `between` and a stratum factor `strata`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c4f62b3-a716-4bb7-a14e-8a758abea7fd",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "556d5072-a813-4551-9cb5-e5cbba528bed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c9c6f6-b875-4796-b3cc-3199b6759d86",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "Correct the *p*-values, for example using the Holm procedure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6feae507-f3d4-4946-9579-edb1eeac6682",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c91bde97-d6ea-4eb6-953b-391df9b3576e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": false,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/pingouin_scipy_TP_solutions.ipynb b/notebooks/pingouin_scipy_TP_solutions.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d819eb1747cd9bf2167fa294d48e68de60e8a4af
--- /dev/null
+++ b/notebooks/pingouin_scipy_TP_solutions.ipynb
@@ -0,0 +1,2982 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a5a5210d",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Import `numpy`, `pandas`, `pingouin`, `seaborn`, and the `stats` module from `scipy`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ac6cc32",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "529c5f56",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from scipy import stats\n",
+    "import seaborn as sns\n",
+    "import pingouin as pg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93ad4aaf",
+   "metadata": {},
+   "source": [
+    "# Descriptive statistics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e4fd0d9",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Load the `mi.csv` data file located in the `../data` directory into a DataFrame (the first column in the file is an index column), and inspect its content printing:\n",
+    "* the first rows, with column names,\n",
+    "* a summary table of all the variables,\n",
+    "* a summary table of the categorical variables only."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08c1dd12",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "eefc126c",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/mi.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "00130518",
+   "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>Age</th>\n",
+       "      <th>OwnsHouse</th>\n",
+       "      <th>PhysicalActivity</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>LivesWithPartner</th>\n",
+       "      <th>LivesWithKids</th>\n",
+       "      <th>BornInCity</th>\n",
+       "      <th>Inbreeding</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>CMVPositiveSerology</th>\n",
+       "      <th>...</th>\n",
+       "      <th>VaccineWhoopingCough</th>\n",
+       "      <th>VaccineYellowFever</th>\n",
+       "      <th>VaccineHepB</th>\n",
+       "      <th>VaccineFlu</th>\n",
+       "      <th>SUBJID</th>\n",
+       "      <th>DepressionScore</th>\n",
+       "      <th>HeartRate</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>HourOfSampling</th>\n",
+       "      <th>DayOfSampling</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22.33</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>94.9627</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>66</td>\n",
+       "      <td>36.8</td>\n",
+       "      <td>8.883</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>28.83</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>79.1024</td>\n",
+       "      <td>21.33</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>66</td>\n",
+       "      <td>37.4</td>\n",
+       "      <td>9.350</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.67</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>117.2540</td>\n",
+       "      <td>22.18</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>62</td>\n",
+       "      <td>36.9</td>\n",
+       "      <td>8.667</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>21.17</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>94.1796</td>\n",
+       "      <td>18.68</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>64</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>9.883</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>26.17</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>105.1250</td>\n",
+       "      <td>29.01</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>67</td>\n",
+       "      <td>36.7</td>\n",
+       "      <td>8.550</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 43 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Age OwnsHouse  PhysicalActivity     Sex LivesWithPartner LivesWithKids  \\\n",
+       "1  22.33       Yes               3.0  Female               No            No   \n",
+       "2  28.83       Yes               0.0  Female              Yes            No   \n",
+       "3  23.67       Yes               0.0  Female              Yes            No   \n",
+       "4  21.17        No               0.5  Female               No            No   \n",
+       "5  26.17       Yes               1.5  Female               No            No   \n",
+       "\n",
+       "  BornInCity  Inbreeding    BMI CMVPositiveSerology  ...  \\\n",
+       "1        Yes     94.9627  20.13                  No  ...   \n",
+       "2        Yes     79.1024  21.33                 Yes  ...   \n",
+       "3        Yes    117.2540  22.18                  No  ...   \n",
+       "4         No     94.1796  18.68                  No  ...   \n",
+       "5        Yes    105.1250  29.01                  No  ...   \n",
+       "\n",
+       "   VaccineWhoopingCough  VaccineYellowFever  VaccineHepB  VaccineFlu  SUBJID  \\\n",
+       "1                   Yes                  No          Yes          No       2   \n",
+       "2                   Yes                  No          Yes          No       3   \n",
+       "3                    No                  No          Yes          No       4   \n",
+       "4                    No                  No          Yes          No       5   \n",
+       "5                   Yes                  No          Yes          No       8   \n",
+       "\n",
+       "   DepressionScore  HeartRate Temperature HourOfSampling DayOfSampling  \n",
+       "1              0.0         66        36.8          8.883            40  \n",
+       "2              0.0         66        37.4          9.350            40  \n",
+       "3              0.0         62        36.9          8.667            40  \n",
+       "4              1.0         64        36.0          9.883            40  \n",
+       "5              0.0         67        36.7          8.550            81  \n",
+       "\n",
+       "[5 rows x 43 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3097c601-7651-4e9a-afcf-e2c3d9b11fd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>46.485711</td>\n",
+       "      <td>13.854402</td>\n",
+       "      <td>20.170000</td>\n",
+       "      <td>35.830000</td>\n",
+       "      <td>47.710000</td>\n",
+       "      <td>58.352500</td>\n",
+       "      <td>69.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PhysicalActivity</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>2.751804</td>\n",
+       "      <td>3.565008</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Inbreeding</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>91.904255</td>\n",
+       "      <td>12.936172</td>\n",
+       "      <td>43.727000</td>\n",
+       "      <td>84.077225</td>\n",
+       "      <td>91.862800</td>\n",
+       "      <td>100.008000</td>\n",
+       "      <td>150.107000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BMI</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>24.208958</td>\n",
+       "      <td>3.181184</td>\n",
+       "      <td>18.500000</td>\n",
+       "      <td>21.770000</td>\n",
+       "      <td>23.850000</td>\n",
+       "      <td>26.210000</td>\n",
+       "      <td>32.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FluIgG</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>0.203601</td>\n",
+       "      <td>0.232411</td>\n",
+       "      <td>-0.430491</td>\n",
+       "      <td>0.065082</td>\n",
+       "      <td>0.227855</td>\n",
+       "      <td>0.363819</td>\n",
+       "      <td>0.769841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MetabolicScore</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>0.932598</td>\n",
+       "      <td>0.893942</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LowAppetite</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>0.512255</td>\n",
+       "      <td>1.674008</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TroubleConcentrating</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>0.355392</td>\n",
+       "      <td>1.408535</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TroubleSleeping</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>1.119771</td>\n",
+       "      <td>0.931400</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HoursOfSleep</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>7.499246</td>\n",
+       "      <td>1.017186</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>7.500000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Listless</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>1.290441</td>\n",
+       "      <td>2.055716</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUBJID</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>576.877451</td>\n",
+       "      <td>518.489935</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>300.750000</td>\n",
+       "      <td>556.500000</td>\n",
+       "      <td>779.250000</td>\n",
+       "      <td>5701.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DepressionScore</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>0.544526</td>\n",
+       "      <td>1.333918</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HeartRate</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>59.209559</td>\n",
+       "      <td>9.206104</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>58.000000</td>\n",
+       "      <td>65.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Temperature</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>36.431985</td>\n",
+       "      <td>0.318461</td>\n",
+       "      <td>35.700000</td>\n",
+       "      <td>36.200000</td>\n",
+       "      <td>36.400000</td>\n",
+       "      <td>36.600000</td>\n",
+       "      <td>37.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HourOfSampling</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>9.214806</td>\n",
+       "      <td>0.378376</td>\n",
+       "      <td>8.433000</td>\n",
+       "      <td>8.917000</td>\n",
+       "      <td>9.233000</td>\n",
+       "      <td>9.550000</td>\n",
+       "      <td>11.217000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DayOfSampling</th>\n",
+       "      <td>816.0</td>\n",
+       "      <td>185.485294</td>\n",
+       "      <td>84.971737</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>136.000000</td>\n",
+       "      <td>187.000000</td>\n",
+       "      <td>263.000000</td>\n",
+       "      <td>335.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      count        mean         std        min         25%  \\\n",
+       "Age                   816.0   46.485711   13.854402  20.170000   35.830000   \n",
+       "PhysicalActivity      816.0    2.751804    3.565008   0.000000    0.500000   \n",
+       "Inbreeding            816.0   91.904255   12.936172  43.727000   84.077225   \n",
+       "BMI                   816.0   24.208958    3.181184  18.500000   21.770000   \n",
+       "FluIgG                816.0    0.203601    0.232411  -0.430491    0.065082   \n",
+       "MetabolicScore        816.0    0.932598    0.893942   0.000000    0.000000   \n",
+       "LowAppetite           816.0    0.512255    1.674008   0.000000    0.000000   \n",
+       "TroubleConcentrating  816.0    0.355392    1.408535   0.000000    0.000000   \n",
+       "TroubleSleeping       816.0    1.119771    0.931400   0.000000    0.000000   \n",
+       "HoursOfSleep          816.0    7.499246    1.017186   3.000000    7.000000   \n",
+       "Listless              816.0    1.290441    2.055716   0.000000    0.000000   \n",
+       "SUBJID                816.0  576.877451  518.489935   2.000000  300.750000   \n",
+       "DepressionScore       816.0    0.544526    1.333918   0.000000    0.000000   \n",
+       "HeartRate             816.0   59.209559    9.206104  37.000000   54.000000   \n",
+       "Temperature           816.0   36.431985    0.318461  35.700000   36.200000   \n",
+       "HourOfSampling        816.0    9.214806    0.378376   8.433000    8.917000   \n",
+       "DayOfSampling         816.0  185.485294   84.971737  17.000000  136.000000   \n",
+       "\n",
+       "                             50%         75%          max  \n",
+       "Age                    47.710000   58.352500    69.750000  \n",
+       "PhysicalActivity        2.000000    4.000000    49.000000  \n",
+       "Inbreeding             91.862800  100.008000   150.107000  \n",
+       "BMI                    23.850000   26.210000    32.000000  \n",
+       "FluIgG                  0.227855    0.363819     0.769841  \n",
+       "MetabolicScore          1.000000    1.000000     4.000000  \n",
+       "LowAppetite             0.000000    0.000000    14.000000  \n",
+       "TroubleConcentrating    0.000000    0.000000    14.000000  \n",
+       "TroubleSleeping         1.000000    2.000000     3.000000  \n",
+       "HoursOfSleep            7.500000    8.000000    12.000000  \n",
+       "Listless                0.000000    3.000000    14.000000  \n",
+       "SUBJID                556.500000  779.250000  5701.000000  \n",
+       "DepressionScore         0.000000    1.000000    14.000000  \n",
+       "HeartRate              58.000000   65.000000   100.000000  \n",
+       "Temperature            36.400000   36.600000    37.700000  \n",
+       "HourOfSampling          9.233000    9.550000    11.217000  \n",
+       "DayOfSampling         187.000000  263.000000   335.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8d45dd25",
+   "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>count</th>\n",
+       "      <th>unique</th>\n",
+       "      <th>top</th>\n",
+       "      <th>freq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>OwnsHouse</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sex</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LivesWithPartner</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>501</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LivesWithKids</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BornInCity</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CMVPositiveSerology</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UsesCannabis</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RecentPersonalCrisis</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Smoking</th>\n",
+       "      <td>816</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Never</td>\n",
+       "      <td>432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Employed</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Education</th>\n",
+       "      <td>816</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Vocational</td>\n",
+       "      <td>279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DustExposure</th>\n",
+       "      <td>816</td>\n",
+       "      <td>3</td>\n",
+       "      <td>No</td>\n",
+       "      <td>607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Income</th>\n",
+       "      <td>816</td>\n",
+       "      <td>4</td>\n",
+       "      <td>(1000-2000]</td>\n",
+       "      <td>262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HadMeasles</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HadRubella</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HadChickenPox</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HadMumps</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HadTonsillectomy</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HadAppendicectomy</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VaccineHepA</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VaccineMMR</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VaccineTyphoid</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VaccineWhoopingCough</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VaccineYellowFever</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VaccineHepB</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VaccineFlu</th>\n",
+       "      <td>816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>655</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     count unique          top freq\n",
+       "OwnsHouse              816      2           No  528\n",
+       "Sex                    816      2       Female  417\n",
+       "LivesWithPartner       816      2          Yes  501\n",
+       "LivesWithKids          816      2           No  458\n",
+       "BornInCity             816      2          Yes  434\n",
+       "CMVPositiveSerology    816      2           No  527\n",
+       "UsesCannabis           816      2           No  769\n",
+       "RecentPersonalCrisis   816      2           No  580\n",
+       "Smoking                816      3        Never  432\n",
+       "Employed               816      2          Yes  421\n",
+       "Education              816      5   Vocational  279\n",
+       "DustExposure           816      3           No  607\n",
+       "Income                 816      4  (1000-2000]  262\n",
+       "HadMeasles             816      2           No  504\n",
+       "HadRubella             816      2           No  740\n",
+       "HadChickenPox          816      2          Yes  522\n",
+       "HadMumps               816      2           No  585\n",
+       "HadTonsillectomy       816      2           No  750\n",
+       "HadAppendicectomy      816      2           No  628\n",
+       "VaccineHepA            816      2           No  780\n",
+       "VaccineMMR             816      2           No  647\n",
+       "VaccineTyphoid         816      2           No  775\n",
+       "VaccineWhoopingCough   816      2           No  616\n",
+       "VaccineYellowFever     816      2           No  748\n",
+       "VaccineHepB            816      2           No  413\n",
+       "VaccineFlu             816      2           No  655"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe(exclude='number').T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04163591",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Inspect the relationship between variables `Age` and `OwnsHouse`. What type of plots is most suitable?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6baac23",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5de6412d",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# categorical vs continuous => boxplot, violinplot\n",
+    "sns.boxplot(x='OwnsHouse', y='Age', data=df);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ee08a262",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='OwnsHouse', y='Age', data=df);\n",
+    "sns.swarmplot(x='OwnsHouse', y='Age', hue='OwnsHouse', data=df, linewidth=1, size=3, legend=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "966a41f4-38ce-4df3-89f4-2cd98b70c438",
+   "metadata": {},
+   "source": [
+    "The \"Yes\" and \"No\" levels might have been interchanged."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf917039",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Draw a box plot (or violin plot) of variable `Age` for two categorical variables, say `OwnsHouse` and `LivesWithKids`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae6ca008",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "06e98558",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Sx6lhTHVtzFzr0T6g193652ODJUmvvjFPjz6e4t52203N9NabM4t9/umvvKjuf7nFo23Lhky1aVRHqWOelsvlUsqYCRo/6bViHxsAgCtBkCmhiIiIy77N+0q1jE/QpvWegWVj5lpFRNbUpnOCjCMvT998vUWtE9pKkqpUqapKQUGlXk/Gyi/00H29df/fHlHKmPGyWCyqHBys4JCQUj8XAACXQpApoXOHltq2bauXX37ZY/uxoz/rhmtraPOGdZKkfIdDLz0/Wre2bKwW9WJ1712dPXpTDh7Yr8EP9FN8o+vUol6s7urYVqv/vVyS1Cq+rb7evFGFhYWSpNOnTmnnf7dp0CPDPALO1q82K9/hUKv4M0Hm3KGcAb3u1sED+/XCuGfUMKa6GsZU96h3Tca/1aXDTWpRP1YP3ddLR3IuPHvyJ2lL9NjDDyj5qWc1ePjj7vY/Di39+utppQwfohb1Y9W+eUPNe+P8i7QXvvUvJbVrpabXR6ldswYa/vADl/iKA8azOAulogJzPQrzpfxfzzwK842vpwQPi7PQ6G89vJhXzSNjVn379lVqaqpGjhzpbkv/OE3VwyPUvNWZOW+ef+af2rtnl16e9oaqh0do5bLP9PD9vZX2RYZia9fR86OeVEFBgeZ/8H8KCAjU3j27FVipkiSp9U0J+vX0aW3/5mvd2LyltmzMVK3adXRb0p16+fkxcuTlyWa3a2PmGtWMjlHN6Jjzanz1jXnq0fkW3dP3ft3T568e23777TfNmzVDqZNnyGq16MnHBuul8WP04lTPYagF8+foxeee1fMvTdGd3e655Nfk5fFjtWnDOk17821Vu+YavfrCeO3Y/q3qNWgkSdr+zValjn5Kqa/OUNPmLXXi+C/asnF98b/4QDkK2rrQ6BIA/AE9MqWgZ8+eOnz4sLZs2eJu++yjD3X7Xd1ksVh08KcDSlu8UJNf/5eat45XTK3aeuDhIWrWorWWvn/mF+Ohgz+paYtWqlu/gaJja+mWxE5q0fomSVJs7ToKj6jhHkbamLlOLdrEq3pYuGrUrKmtX22WJG3KXKtW8QkXrLFKlaqy+vioUqVKqh4Wruph4e5thQUFenbCS2p0w41q0PgG9R0wSOvXrvZ4/w/f79H4Z/6pZ8e/+Kch5vTpU/rwvXf1j6fHqE3bdqpbv4EmTJ6mosIi9z6HDh5QQGCgbunYSZFR0Ypr1ER/HfjQ5X7JAQCQRI9MqahevboSEhL08ccfq2m7TjqQ9aO2btmk0alnhpv2fLdDRUVFuv2W1h7vK8jPV5WqVSVJ/R74m557+gmtXf0fxbdtr9tuv1P14hq6920Zn6CN69fqwaHDtWn9Wg18eIgkqUXrm7Qpc61uaNZc3279Svf0ua/Y9QcEBCqmVu3fP09YuI797LmmUUREpCoHB2vurOm6+ZaOqh5+8WUi9v/4PxXk56tx0+butipVqqpWnd/vjrrp5lsUWTNandu2UNv2HdT2lg7q+JfbFRDALMvwLna7Xenp6UaXUWJ5eXnuNbuWLl0qu9280y2e+1mAswgypeTOO+/U+PHjlTKhQJ9+tER16zdQ3foNJEm/nj4tHx8fLf50paw+np1ggYFnho/u6XOfEtp30OqVy7Xuy1WaPWOKnhg1Vv0eeFCS1Co+QaljntbxX47pu/9uU4s2Z3prWra5Se+/O18tWserID9frRNuLnbtvn6e/wwsFotcLs/ZWQODKunNBUv0YL979MC93TR30dJLhpk/UykoSIs/W6lNmWu1dvUqTZv0gqZPflHvfbyci4bhVSwWS4VZ+NJut1eYzwKcxdBSKenYsaPy8/O1ZtW/9Wnah7qjaw/3trhGTVRUVKRjPx9RbK1rPR7nDvHUiKyp3vcN0JQ35mnAg4/og4XvuLe1im+r3379VfNnv66YWtcq9JozF+s2bx2vbd98rS9XrVRs7WsVHlHjojX6+fnJ6Sz5goshVarozQVLVCmosgb07qrD2Re+IDg6tpZ8/fy07evfh9pOHD+uH3/4wWM/X19fxd/cXo8/PVoffrFKBw/s14Z1X5a4PgDA1YcemT9x4sQJbd261aPtQitlBwYGqmPHjnpt0kT98P1u3X53d/e2WtfW0Z3d7lFK8lD9Y9RYxTVsrGPHjmrDmtWqG9dA7Tt2UuqYp3XzrR1Vq3Yd5Z44ro2Za3Xtdde7jxEdW0s1akbp3Xlv6s6uv1+jUiOypsLCIrR4wVu6/a7uupSaUTHavCFTSXd1k7+/v6pWK/6K38EhIXrz3Q/00H29NKD33Zr33kcK+8Nq5JUqBalH7356ecJYhVStptBrrtGUFyfIYv19TatVK77Qgaz/qXnreIWEVNHq/6yQ0+lU7WuvK3ZNAICrF0HmT6xatUpNmzb1aBs0aNAF9+3SpYseeughtWgdr8iaUR7bnn95qmZNfUUvPT9aOdmHVLVqNd3QrIXaJ3aSJDmdRXp+1JPKyT6koKDKatu+g54c/ZzHMVrFJ+ijD95Ty/8/rHRWizbxSlu8SK1uanvJzzJ05JMak/K4/nJzS+U7HPpv1pHL+hr8UeXgYM1+Z7Eevr+3+ve6S/Pe/+i8fUY+PVq/nj6toQP/qsCgShrw4GCdOpnrcYwVn3+q6ZNfUr7DoZja1+ql12bpunr1S1QTAODqZHH98WKICiY3N1chISE6ceKEgoODPbbl5eVp3759ql279hVdAOd0OrVnzx5JUlFgNUmspl3anIUFOnzogCat2Kvsk/nn71BUoMpfve3RNLv9UdOufm1mjiLpwYwzvX3p6elck2Gw3377zT0Ludm/H+d+Fn6+jVGeP9+X+vt9Lq6RAQAApkWQAQAApkWQAQAApkWQAQAAplXiIJOfn69du3a5FzIEAAAob8UOMr/++qsGDRqkwMBANWzYUFlZWZKkYcOGaeLEiaVeIAAAwMUUO8ikpKTom2++0apVqzxuWU5MTNR7771XqsUBAABcSrEnxEtLS9N7772nNm3ayGL5fb6Uhg0bau/evaVaHAAAwKUUO8gcOXJEYWFh57WfPn3aI9jgzGy9Lmf5zTdosVpktTJDFADg6lHsINOiRQt9+umnGjZsmCS5w8ubb76p+Pj40q3OxJzOIv3z0b/r1Ilfyu2cQSFVNXHqTMIMAOCqUewgM2HCBCUlJWnHjh0qLCzUlClTtGPHDq1bt04ZGRllUaMpuZwunTrxi042u1+ylMNd7i6n9NVbZ3qALvN0TyUP1UcfvKfhT47Sg0Mec7evXPaZHn2wf4nXYgIAoLwU+y9s27ZttXXrVhUWFqpx48b64osvFBYWpszMTDVv3rwsajQ3i1WylsOjhGHJZrPrX6+/phPHj5fu5wYAoByUaPXrOnXqaPbs2aVdCwzQpm077f9xn2ZPn6LHnx59wX2++OxjTZv0grJ+3KfqYeHqN+BvGvDQ4HKuFACA8xX7v/G5ubkXfJw8eVL5+RdYlRhezcfHR4898bQWzHtT2YcOnrf9v99+o5GD/6aku7op7YvVGjziH3rt5YlaunihAdUCAOCp2D0yVapUueTdSVFRURowYIBGjx4tq5UVEMwg8S93qH7DRpr+ygt67qUpHtvmv/m62iS00yOPjZQk1bq2jvbu3q25M6erW88+RpQL4Crlcv1+F6ijyMBCrmLnft3P/X4YqdhBZt68eXr66ac1YMAAtWrVSpK0ceNGzZ8/X6NGjdKRI0f08ssvy2az6amnnir1glE2klOe0cB7u2vAQ0M82n/4frc63Jbk0dasZSu9/a9ZKioqko8Pd0gBKB8Oh8P9fOiaUAMrgXTm+xEYGGh0GcUPMvPnz9ekSZPUq1cvd1uXLl3UuHFjzZo1SytXrlRMTIzGjx9PkDGRFq1vUkL7W/XqC8+ra897jS4HAIDLUuwgs27dOs2cOfO89qZNmyozM1PSmTubzq7BBPMY8c9n1OMvt6rWtde52669rq6+3rzRY7+vNm1Urdp16I0BUK5sNpv7+bS2R2XjV1C5cxT93ht27vfDSMUOMtHR0ZozZ855C0TOmTNH0dHRkqSjR4+qatWqpVOh2bmckrOcznOF6tZvoDu73qN35/5+R9qABx9R7y6d9PqUSUrq0lVbt2zSwvlzNGr8C1d8PgAojnOvz7T5iCBjMG+Zzb/YQebll19Wz549lZ6erpYtW0qSNm/erJ07d2rJkiWSpE2bNql3796lW6nJWKwWBYVUlb56q9zOGRRSVRbrlf3DGjrySaV/kuZ+3aDxDZo0401Nm/SCZk6dpOph4Ro68kku9AUAeIViB5m77rpLu3bt0syZM7V7925JUlJSktLS0nTq1ClJ0iOPPFK6VZqQ1eqjiVNnevVaSxNemXZeW83oGG39/iePtk63d1Gn27tccX0AAJS2Ek2IV6tWLffQUm5urhYuXKjevXtr8+bNKirinrizrFafEszUAwAALleJ/8yuXr1a/fv3V2RkpCZNmqRbb71V69evL83aAAAALqlYPTLZ2dmaN2+e5syZo9zcXPXq1UsOh0NpaWlq0KBBWdUIAABwQZfdI9OlSxfVq1dP3377rV599VUdPHhQr732WlnWBgAAcEmX3SOTnp6uRx99VI888oiuv/76sqwJAADgslx2j8yaNWt08uRJNW/eXK1bt9a0adP0888/l2VtAAAAl3TZQaZNmzaaPXu2Dh06pIcffliLFi1SZGSknE6nli9frpMnT5ZlnQAAAOcp9l1LlSpV0sCBA7VmzRpt27ZNI0eO1MSJExUWFqa77rqrLGoEAAC4oCua5aRevXp68cUXdeDAAS1cuLC0aqownM4iFRUWltvD6WQOHwDA1aVEE+L9kY+Pj7p27aquXbuWxuEqBKezSM88+jf9cqL8htyqhlTWc1PfLNbsvgAAmFmpBBmcz+V06ZcTJzW7/VH5lMO6WkUu6cGMM+e9nH42l8ulv/W9R1Yfq2a/s9hj28K3/qUpL45X2vIvFVEjsowqBgDgyjGBfhnzsUi+1rJ/FDcsWSwWPT9pqrZt/UrvvzPf3X4g60dNmjBWT41NJcQAALweQeYqViOypv45erxeGj9aB7J+lMvl0jNPDFdCu1sV16ixHr6/t1rUj1W7Zg30z8cG65djR93vXfbp/6nrbe3U7Ppo3dSkrgb16aFffz1t4KcBAFyNCDJXua4971WbhHYa9Y/HtGDem/p+13canfqyBt7bXXENG+v9T1Zo1luLdPTnI0oe/DdJ0pGcbD0x7GF1691XH/97rea9n6bEpDvkcpXfSt8AAEhcIwNJYyZO0t2JN2vLhky9Omuu3n93vuo3bKThT45y7/Pcy1PUsfUN+t8Pe/Xr6VMqLCzUbX+5Q5FR0ZKkuvVZawsAUP4IMlDoNdXVs9/9+veydHXsfLs+WfqBNmauVYv6seftu//Hfbqp3a1qk9BOXTu1U0K7W3VTu1vV6fYuCqlSpfyLBwBc1QgykCT5+vjKx/fMbdu//npatyR2UnLKs+ftVz0sXD4+PnpzwQf6evNGrVu9SgvmvampL03Qwo8+V1TM+eEHAICywjUyOE+DRk20d/cu1YyKUWytaz0egYGVJJ2566lZy9YaOvJJfZD+b/n5+WnFss8MrhwAcLUhyJSxIpdU6Cz7R1EpXmfb5/5BOnH8uP4x9CFt++ZrZf1vn9Zk/FtPjxymoqIiffv1Fr0xbbK2f7NVB386oBXpn+jYsaOqcx2rogMAyhdDS2XEYrWoakhlPZhRfuesGlJZFuuVz74XFhGhdz78RK+kPqeH+vVUfn6+IqOilNC+g6xWqyoFVdbmDZl6e84bOnXqpCJrRukfo8bq5lsTS+FTAABw+QgyZcRq9dFzU988M9NuObFYLSVenmBI8hMakvyE+3Vs7Tqa8sa8C+5b5/q6euPt90t0HgAAShNBpgxZrT4M3gEAUIYIMqiwHCwGbohzv+5MkgigrBFkUGENXRNqdAlXPYfDocDAQKPLAFCBec3Ax8SJE2WxWDR8+HB3W15enoYMGaLQ0FAFBQWpR48eysnJMa5IAADgVbyiR2bTpk2aNWuWmjRp4tE+YsQIffrpp1q8eLFCQkI0dOhQde/eXWvXri3V8zudzlI9HsqAyyWXq3i3mU9re1S2kl37jCvgKPq9N8xmsxlcDYCKzvAgc+rUKfXr10+zZ8/W888/724/ceKE5syZowULFqhDhw6SpLlz5youLk7r169XmzZtrvjc/v7+slqtOnjwoKpXry5/f39ZLMW/fdnpdLrDkLOwQNKV3wKNs1xyFRXp15PHdeK3fB37teCy32nzEUHGYCX5eQKA4jA8yAwZMkR33HGHEhMTPYLMli1bVFBQoMTE3+cmqV+/vmJiYpSZmXnRIONwOORwONyvc3NzL3puq9Wq2rVr69ChQzp48GCJP4PL5XIPebn8T5FjSpHLJRU5XfouO1cfbTusonK8nR0A4P0MDTKLFi3SV199pU2bNp23LTs7W/7+/qryh4UIw8PDlZ2dfdFjpqamauzYsZddg7+/v2JiYlRYWKiiopLd5pKXl6cxY8ZIkk43uEuyGp4PKwynpF/zi3TaUSQiDADgjwz7i7t//3499thjWr58uex2e6kdNyUlRcnJye7Xubm5io6OvuR7LBaL/Pz85OfnV6JzulwuHT58WJJ0Msoh+XDNDQAA5cGwu5a2bNmiw4cPq1mzZvL19ZWvr68yMjI0depU+fr6Kjw8XPn5+Tp+/LjH+3JychQREXHR49psNgUHB3s8AABAxWRYj0zHjh21bds2j7YHHnhA9evX15NPPqno6Gj5+flp5cqV6tGjhyRp165dysrKUnx8vBElAwAAL2NYkKlcubIaNWrk0VapUiWFhoa62wcNGqTk5GRVq1ZNwcHBGjZsmOLj40vljiUAAGB+Xn1V6uTJk2W1WtWjRw85HA517txZM2bMMLosAADgJbwqyKxatcrjtd1u1/Tp0zV9+nRjCgIAeCVHkUUy4b2MLpeU///vB/G3SmabaunM1927eFWQAQDgcgxdU83oEuAlvGatJQAAgOKiRwYAYAp2u13p6elGl3FF8vLy1K1bN0nS0qVLS3UetfLmLbUTZAAApmCxWBQQEGB0GaXGbrdXqM9jFIaWAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAafkaXQBQVhxFFkkuo8soNpdLyneeee5vlSwWY+sprjNfdwAoHwQZVFhD11QzugQAQBljaAkAAJgWPTKocJYuXSq73W50GSWWl5enbt26STL/ZzFz7QDMgSCDCsdutysgIMDoMkpFRfosAFAWGFoCAACmRZABAACmRZABAACmxTUypcDlOmeukqIC4wq5mp3zdff4fgAAKjSCTClwOBzu55W/WWRgJZDOfD8CAwONLgMAUA4YWgIAAKZFj0wpsNls7ucnb7hX8vEzsJqrVFGBuzfs3O8HAKBiI8iUAsu5i+H4+BFkDGYx2+JEAIASY2gJAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYlqFB5vXXX1eTJk0UHBys4OBgxcfHKz093b09Ly9PQ4YMUWhoqIKCgtSjRw/l5OQYWDEAAPAmhgaZqKgoTZw4UVu2bNHmzZvVoUMH3X333frvf/8rSRoxYoQ+/vhjLV68WBkZGTp48KC6d+9uZMkAAMCL+Bp58i5duni8Hj9+vF5//XWtX79eUVFRmjNnjhYsWKAOHTpIkubOnau4uDitX79ebdq0ueAxHQ6HHA6H+3Vubm7ZfQAAAGAor7lGpqioSIsWLdLp06cVHx+vLVu2qKCgQImJie596tevr5iYGGVmZl70OKmpqQoJCXE/oqOjy6N8AABgAMODzLZt2xQUFCSbzaa///3vWrp0qRo0aKDs7Gz5+/urSpUqHvuHh4crOzv7osdLSUnRiRMn3I/9+/eX8ScAAABGMXRoSZLq1aunrVu36sSJE/rggw/Uv39/ZWRklPh4NptNNputFCsEAADeyvAg4+/vr+uuu06S1Lx5c23atElTpkxR7969lZ+fr+PHj3v0yuTk5CgiIsKgagEAgDcxfGjpj5xOpxwOh5o3by4/Pz+tXLnSvW3Xrl3KyspSfHy8gRUCAABvYWiPTEpKipKSkhQTE6OTJ09qwYIFWrVqlZYtW6aQkBANGjRIycnJqlatmoKDgzVs2DDFx8df9I4lAABwdTE0yBw+fFj333+/Dh06pJCQEDVp0kTLli3TbbfdJkmaPHmyrFarevToIYfDoc6dO2vGjBlGlgwAALyIoUFmzpw5l9xut9s1ffp0TZ8+vZwqAgAAZuJ118gAAABcLoIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLV+jCwAAwBu4XC7l5eWV6TnOPX5Zn8tut8tisZTpObwBQQYAAJ0JFklJSeV2vm7dupXp8dPT0xUQEFCm5/AGDC0BAADTokcGAACdGYpJT08v03O4XC45HA5Jks1mK9OhH7vdXmbH9iYEGQAAJFkslnIZigkMDCzzc1xNGFoCAACmRZABAACmRZABAACmxTUyAGCgsp67pDznLZGunrlL4D0IMgBgoPKcu6Ss5y2Rrp65S+A9GFoCAACmRY8MABiorOcuKc95S6SrZ+4SeA+CTCmzOAvlMrqI4nK5JGfhmedWX8mE49uWs/UDJlMec5cwbwkqMoJMKQvautDoEgAAuGpwjQwAVHDr1q1T7969tW7dOqNLAUodPTKloDzW5yhLeXl57rsZli5davoxbrPXD5SmvLw8vfLKK/r555/1yiuvqFmzZvyMoEIhyJSC8lqfozzY7fYK81kASO+++66OHj0qSTp69KgWLFiggQMHGlwVUHoYWgKACurAgQNasGCBXK4ztyC4XC4tWLBABw4cMLgyoPQQZACgAnK5XJoyZcpF28+GG8DsCDIAUAFlZWVp06ZNKioq8mgvKirSpk2blJWVZVBlQOkiyABABRQTE6OWLVvKx8fHo93Hx0etWrVSTEyMQZUBpYsgAwAVkMVi0WOPPXbRdhZ2REVBkAGACioqKkp9+/Z1hxaLxaK+ffuqZs2aBlcGlB6CDABUYP369XPPG2O329W3b1+DKwJKF0EGACqwvLw85eXlnfccqCgIMgBQgT3zzDMe88g8++yzBlcElC6CDABUUJs3b9a2bds82r799ltt3rzZoIqA0scSBUAxuFyuMu+aP/f4ZX0uu93O3SsVlNPp1Lhx4y64bdy4cUpLS5PVyv9lYX4EGaAY8vLylJSUVG7nO7uYZ1lJT09nba0KasOGDcrNzb3gttzcXG3YsEHx8fHlXBVQ+ojjAFABtW7dWkFBQRfcFhQUpNatW5dzRUDZoEcGKAa73a709PQyPYfL5ZLD4ZAk2Wy2Mh36OXtbLioei8WiyMhI7d69+7xtkZGRDCmiwiDIAMVgsVjKZSgmMDCwzM+Bii0rK+uCIUaSdu/eraysLMXGxpZzVUDpY2gJACqg6OhoBQcHX3BbcHCwoqOjy7kioGwQZACgAtq/f/8lL/bdv39/OVcElA2CDABUQGdXv/7jtTAWi4XVr1GhEGQAoAJi9WtcLQgyAFBBRUVFqVGjRh5tjRo1YvVrVCiGBpnU1FS1bNlSlStXVlhYmLp27apdu3Z57JOXl6chQ4YoNDRUQUFB6tGjh3JycgyqGADM48CBA9qxY4dH244dO3TgwAGDKgJKn6G3X2dkZGjIkCFq2bKlCgsL9dRTT6lTp07asWOHKlWqJEkaMWKEPv30Uy1evFghISEaOnSounfvrrVr1xpZerkq62nxy3NKfIlp8YHy4HK5NGXKlIu2v/jii/wcokKwuM4ui+oFjhw5orCwMGVkZKhdu3Y6ceKEqlevrgULFuiee+6RJH333XeKi4tTZmam2rRp86fHzM3NVUhIiE6cOHHRWxG93W+//Vau0+KXNabFB8rejz/+qP79+190+/z585lHBl7tcv9+e9U1MidOnJAkVatWTZK0ZcsWFRQUKDEx0b1P/fr1FRMTo8zMzAsew+FwKDc31+MBAFebs3ct+fj4eLT7+Phw1xIqFK+Z2dfpdGr48OFKSEhwX5yWnZ0tf39/ValSxWPf8PBwZWdnX/A4qampGjt2bFmXW67Kelr88pwSX2JafKA8nL076Y+9Mty1hIrGa4LMkCFDtH37dq1Zs+aKjpOSkqLk5GT369zcXNPPYFke0+IzJT5Q8URFRalv375655135HK5ZLFY1LdvX+5aQoXiFUNLQ4cO1SeffKL//Oc/ioqKcrdHREQoPz9fx48f99g/JydHERERFzyWzWZTcHCwxwMArlb9+vVTaGioJOmaa65R3759Da4IKF2GBhmXy6WhQ4dq6dKl+ve//63atWt7bG/evLn8/Py0cuVKd9uuXbuUlZWl+Pj48i4XAEzHbrcrOTlZ4eHhGjFiBEO7qHAMvWtp8ODBWrBggT766CPVq1fP3R4SEuIeSnnkkUf02Wefad68eQoODtawYcMkSevWrbusc1SEu5YAALjaXO7fb0ODzMUuNps7d64GDBgg6cy8JiNHjtTChQvlcDjUuXNnzZgx46JDS39EkAEAwHxMEWTKA0EGAADzMeU8MgAAAMVBkAEAAKZFkAEAAKZFkAEAAKZFkAEAAKZFkAEAAKZFkAEAAKZFkAEAAKblNatfl5Wz8/3l5uYaXAkAALhcZ/9u/9m8vRU+yJw8eVKSFB0dbXAlAACguE6ePKmQkJCLbq/wSxQ4nU4dPHhQlStXvujaTqg4cnNzFR0drf3797MkBVDB8PN9dXG5XDp58qQiIyNltV78SpgK3yNjtVoVFRVldBkoZ8HBwfyiAyoofr6vHpfqiTmLi30BAIBpEWQAAIBpEWRQodhsNo0ePVo2m83oUgCUMn6+cSEV/mJfAABQcdEjAwAATIsgAwAATIsgAwAATIsgAwAATIsgA1NwuVxKTExU586dz9s2Y8YMValSRQcOHDCgMgClacCAAbJYLJo4caJHe1paGrOz44IIMjAFi8WiuXPnasOGDZo1a5a7fd++fXriiSf02muvMYMzUEHY7Xa98MIL+uWXX4wuBSZAkIFpREdHa8qUKXr88ce1b98+uVwuDRo0SJ06dVLTpk2VlJSkoKAghYeH67777tPPP//sfu8HH3ygxo0bKyAgQKGhoUpMTNTp06cN/DQALiYxMVERERFKTU296D5LlixRw4YNZbPZVKtWLU2aNKkcK4Q3IcjAVPr376+OHTtq4MCBmjZtmrZv365Zs2apQ4cOatq0qTZv3qzPP/9cOTk56tWrlyTp0KFD6tOnjwYOHKidO3dq1apV6t69+58uDQ/AGD4+PpowYYJee+21Cw4Zb9myRb169dK9996rbdu2acyYMXrmmWc0b9688i8WhmNCPJjO4cOH1bBhQx07dkxLlizR9u3b9eWXX2rZsmXufQ4cOKDo6Gjt2rVLp06dUvPmzfW///1PsbGxBlYO4M8MGDBAx48fV1pamuLj49WgQQPNmTNHaWlp6tatm1wul/r166cjR47oiy++cL/viSee0Keffqr//ve/BlYPI9AjA9MJCwvTww8/rLi4OHXt2lXffPON/vOf/ygoKMj9qF+/viRp7969uuGGG9SxY0c1btxYPXv21OzZsxl7B0zghRde0Pz587Vz506P9p07dyohIcGjLSEhQXv27FFRUVF5lggvQJCBKfn6+srX11eSdOrUKXXp0kVbt271eOzZs0ft2rWTj4+Pli9frvT0dDVo0ECvvfaa6tWrp3379hn8KQBcSrt27dS5c2elpKQYXQq8mK/RBQBXqlmzZlqyZIlq1arlDjd/ZLFYlJCQoISEBD377LOKjY3V0qVLlZycXM7VAiiOiRMn6sYbb1S9evXcbXFxcVq7dq3HfmvXrlXdunXl4+NT3iXCYPTIwPSGDBmiY8eOqU+fPtq0aZP27t2rZcuW6YEHHlBRUZE2bNigCRMmaPPmzcrKytKHH36oI0eOKC4uzujSAfyJxo0bq1+/fpo6daq7beTIkVq5cqWee+457d69W/Pnz9e0adP0+OOPG1gpjEKQgelFRkZq7dq1KioqUqdOndS4cWMNHz5cVapUkdVqVXBwsFavXq3bb79ddevW1ahRozRp0iQlJSUZXTqAyzBu3Dg5nU7362bNmun999/XokWL1KhRIz377LMaN26cBgwYYFyRMAx3LQEAANOiRwYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQbAZdm/f78GDhyoyMhI+fv7KzY2Vo899piOHj1qSD1jxozRjTfeeF77//73P1ksFm3durXcawJQ/ggyAP7UDz/8oBYtWmjPnj1auHChvv/+e82cOVMrV65UfHy8jh07ZnSJAK5SBBkAf2rIkCHy9/fXF198ofbt2ysmJkZJSUlasWKFfvrpJz399NOaNm2aGjVq5H5PWlqaLBaLZs6c6W5LTEzUqFGjJP3eo/L222+rVq1aCgkJ0b333quTJ0+69//ggw/UuHFjBQQEKDQ0VImJiTp9+nSx68/IyFCrVq1ks9lUo0YN/fOf/1RhYaF7e61atfTqq696vOfGG2/UmDFjJEkul0tjxoxRTEyMbDabIiMj9eijj7r3dTgcevzxx1WzZk1VqlRJrVu31qpVq4pdJ4DiI8gAuKRjx45p2bJlGjx4sAICAjy2RUREqF+/fnrvvffUvn177dixQ0eOHJF0Jjxcc8017j/oBQUFyszM1C233OJ+/969e5WWlqZPPvlEn3zyiTIyMjRx4kRJ0qFDh9SnTx8NHDhQO3fu1KpVq9S9e3cVd53bn376Sbfffrtatmypb775Rq+//rrmzJmj559//rKPsWTJEk2ePFmzZs3Snj17lJaWpsaNG7u3Dx06VJmZmVq0aJG+/fZb9ezZU3/5y1+0Z8+eYtUKoPh8jS4AgHfbs2ePXC6X4uLiLrg9Li5Ov/zyi8LCwlStWjVlZGTonnvu0apVqzRy5EhNmTJFkrRx40YVFBTopptucr/X6XRq3rx5qly5siTpvvvu08qVKzV+/HgdOnRIhYWF6t69u2JjYyXJIzxI0rZt2xQUFOTR9segM2PGDEVHR2vatGmyWCyqX7++Dh48qCeffFLPPvusrNY///9cVlaWIiIilJiYKD8/P8XExKhVq1bubXPnzlVWVpYiIyMlSY8//rg+//xzzZ07VxMmTPjT4wMoOXpkAFyWP+sJsVgsateunVatWqXjx49rx44dGjx4sBwOh7777jtlZGSoZcuWCgwMdL+nVq1a7hAjSTVq1NDhw4clSTfccIM6duyoxo0bq2fPnpo9e7Z++eUXj3PWq1dPW7du9Xh89tlnHvvs3LlT8fHxslgs7raEhASdOnVKBw4cuKzP3rNnT/3222+69tpr9eCDD2rp0qXuoalt27apqKhIdevWVVBQkPuRkZGhvXv3XtbxAZQcPTIALum6666TxWLRzp071a1bt/O279y5U1WrVlX16tV1yy236I033tCXX36ppk2bKjg42B1uMjIy1L59e4/3+vn5eby2WCxyOp2SJB8fHy1fvlzr1q3TF198oddee01PP/20NmzYoNq1a0uS/P39dd1113kcw9e3+L/WrFbreUGtoKDA/Tw6Olq7du3SihUrtHz5cg0ePFgvvfSSMjIydOrUKfn4+GjLli3y8fHxOMYfe4sAlD56ZABcUmhoqG677TbNmDFDv/32m8e27Oxsvfvuu+rdu7csFov7OpnFixe7r4W55ZZbtGLFCq1du9bj+pjLYbFYlJCQoLFjx+rrr7+Wv7+/li5dWqxjxMXFKTMz0yOorF27VpUrV1ZUVJQkqXr16jp06JB7e25urvbt2+dxnICAAHXp0kVTp07VqlWrlJmZqW3btqlp06YqKirS4cOHdd1113k8IiIiilUrgOIjyAD4U9OmTZPD4VDnzp21evVq7d+/X59//rluu+021axZU+PHj5ckNWnSRFWrVtWCBQs8gkxaWpocDocSEhIu+5wbNmzQhAkTtHnzZmVlZenDDz/UkSNHLnqtzsUMHjxY+/fv17Bhw/Tdd9/po48+0ujRo5WcnOy+PqZDhw56++239eWXX2rbtm3q37+/R+/KvHnzNGfOHG3fvl0//PCD3nnnHQUEBCg2NlZ169ZVv379dP/99+vDDz/Uvn37tHHjRqWmpurTTz8tVq0Aio8gA+BPXX/99dq8ebOuvfZa9erVS3Xq1NFDDz2kW2+9VZmZmapWrZqkMz0oN998sywWi9q2bSvpTLgJDg5WixYtVKlSpcs+Z3BwsFavXq3bb79ddevW1ahRozRp0iQlJSUVq/aaNWvqs88+08aNG3XDDTfo73//uwYNGuS+DVySUlJS1L59e915552644471LVrV9WpU8e9vUqVKpo9e7YSEhLUpEkTrVixQh9//LFCQ0MlSXPnztX999+vkSNHql69euratas2bdqkmJiYYtUKoPgsruLeywgAAOAl6JEBAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACm9f8A8oRLZN5VTBYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.boxplot(x='OwnsHouse', y='Age', data=df, hue=\"LivesWithKids\")\n",
+    "#sns.move_legend(ax, bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e94c17b",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Isolate the house-owners group from the others, and report their mean age(s) as $99\\%$ confidence interval(s)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "497142f3",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "55d18f16",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "owns_house = df.groupby('OwnsHouse').groups\n",
+    "house_owners_age = df.loc[owns_house['Yes'], 'Age']\n",
+    "others_age = df.loc[owns_house['No'], 'Age']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4f129b2a",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "mean = np.mean(house_owners_age)\n",
+    "sem = stats.sem(house_owners_age)\n",
+    "distribution_of_the_mean = stats.norm(mean, sem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9613dc45-cdac-4932-b646-d36965d2d4e6",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(37.56221005578753, 42.072095499768025)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "distribution_of_the_mean.interval(.99)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a721a374",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "### Bonus\n",
+    "\n",
+    "Alternative calculation, for both groups:\n",
+    "\n",
+    "First we need to evaluate the inverse survival function of the standard normal distribution at $0.005$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1fca5a60",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "alpha = 0.01\n",
+    "z = stats.norm().isf(alpha / 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9f044f06",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "House owners: 39.82 ± 2.25 years old on average\n",
+      "Others: 50.12 ± 1.32 years old on average\n"
+     ]
+    }
+   ],
+   "source": [
+    "for group_name, group_age in (\n",
+    "    ('House owners', house_owners_age),\n",
+    "    ('Others', others_age),\n",
+    "):\n",
+    "    m = np.mean(group_age)\n",
+    "    z_times_sem = z * stats.sem(group_age)\n",
+    "    print(f'{group_name}: {m:.2f} ± {z_times_sem:.2f} years old on average')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41b73555-f925-444a-a3dc-033407054810",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "# Tests on single variables\n",
+    "\n",
+    "Let us consider the logarithm of variable `HeartRate`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b2909a2b-2a63-43ef-914d-4f3050d73589",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['logHeartRate'] = np.log(df['HeartRate'])\n",
+    "sns.histplot(df, x='logHeartRate', hue='Sex');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5d6ea38-8582-4eb8-839e-64a4b6cf33e1",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "Draw Q-Q plots for variables `HeartRate` and `logHeartRate`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28a8b3a6-1765-440b-88af-a9a491964b12",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a74cc81e-d3a1-4d48-a2da-31479af531d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pg.qqplot(df['HeartRate']);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fe198641-2797-4f86-87b0-017fa744f997",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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v2NyMCbaoKHXg3Jkz6iwjNjYwfHhO3kq+Ix1KhBAimwID4d+7RbhGeXryD2eoaXDM08F2zcvIYNu1C2rVgt9+U8epRUTk5C3kWxJuQgiRTeHhcInK1OYk2+hgsL82J/SC7YRLbQ5NzWKwxcera6u1bat+0BdfQFBQvpm139Qk3IQQwlhJSTBwIJViTwDpd/mvzQn22rTQC7bm0YG4V8xije3119UBc9Wrq0vRfPKJ2hwpskR+U0IIYQxFUbvg+/nRICmZMmV+JyxM/5D0gq1FdCBFvFxo3jyLnzNuHJQvD7NmgYNDjt5CQSA1NyGEMELK5Kng50domRd5W7uYxo319z/dFHnSuRbNowN5qHFh7lywTn/uZHjwAAYOVGfxB2jZEr7/XoItm6TmJoQQWXR0+M/U//Ez/qUyvmHrub9cP3hSg61IchSgBluzh/so4uWC31zo3j2DCwcHQ+/e6vg1Nzf49lvT3kgBIOEmhBBZsG/SVl78cSh3cKcTm7lPcb396QXbH2/uY90balNkujU2RVGfq40fr/785Zfw0Uemv5kCwGKaJadPn06jRo1wdnamRIkSdOvWjQsXLpi7WEKIAkCrhXE/VeEEdXiVDVzBW29/LU6mW2P7a2MmwRYVpXYaGTNG7QG5Zw98/HEm7ZbCGBYTbnv27GHEiBHs37+f7du3k5SUxEsvvURsbKy5iyaEyOcCA2H/7Qo04hCH0H/IVouT7LVpYRBsD3Hhxg313HQlJMD+/WpX/2PHoGlTE99FwWIxzZJbtmzRe+/n50eJEiU4cuQILVq0MFOphBD5WkQE9OjBo7azgQYoT9UHng62U841dcGWKjz8qWuGhUGZMmptbd8+dSotqa3lOIupuT0tKkr9j6lo0aIZHpOQkEB0dLTeSwghsiQ+Hrp2hYAATny+zmD3002Rp5xr0vRhkMGK2x4eaa43ZAjUrAlXr6rbKlSQYDMRiwy3lJQURo8eTdOmTalZ03C6m1TTp0/H1dVV9/Ly8srFUgohLFZKCvTrB/v28Qd9mJg4VW93arC5JUcCGQebpyfquLawMLVr/88/Q+XKEmi5QKMoimLuQhhr2LBhbN68mX379uHp6ZnhcQkJCSQkJOjeR0dH4+XlRVRUFC4uLhmeJ4Qo4MaMgTlz2GfXhjYJm0nCVrerJqfYa9NCL9iaPdxHNK4Gl1m1CrqXDIIePeD2bRg0CH74AezscutO8pXo6GhcXV2z9B1uMc/cUo0cOZINGzawd+/eTIMNwM7ODjv5j0gIYYwff4Q5c4ipUJNXrvpnK9iKFYPFi6G7zTpo/brazX/BAhg2TBYTzSUWE26KovDee++xevVqAgICqFChgrmLJITIj159FdavZ8crPxE98kloGQZbDb1g69pVnby/VSv1ZW0N3HkR6tSBb74B6fiWqywm3EaMGMGyZctYu3Ytzs7O3Lp1CwBXV1ccZHoaIcTzSkwEW1vw8oJNmzj52ZNd6QdbkF6NbfRoNdS4fRtCLkKzZmqPyIMHpbZmBhbToeTHH38kKiqKVq1a4eHhoXutWLHC3EUTQli6/fvVjh4HDwLqoO3Fi9VdNTj9zGDTdRw5eRIaNVJrfzdvqjsl2MzCYmpuFtjvRQhhCc6dg1degehouH8fgIAAtYNjDU4TaNM802ADmDcPrDetV+eHjIuDGTPSjAEQ5mAxNTchhMhxN25Ahw7qYO1ff4VOnfD3V7Pu6WA7nU6wOTnBqpUK3a/MVh+6Aaxerc4VKTU2s7KYmpsQQuSoiAg12K5fh9mzoW9f/P3VXvvpBVvTdGpsa9ZA2xNz1LXXvLxg/Xq1A4kwOwk3IUTBNGIEnD2rBtOHH6LVwnvvPX7GVqgFbkmRQMbB5un5uANJnX5w6BDMmQOlSuX6bYj0SbgJIQqm2bPhhRdInDCZ779VB1y73VSDrWjSAyC1KdJwHFtVzvFxmzCsrdtB8eLw11/muAORCQk3IUTBoShqTxFPTyhThvFxU/nGUZ1tK7XG9nSwRVFE7xKt2M1qXqPwPwp8c1kNN5HnSIcSIUTB8cknULs2HDnC+PEwa5YabNU5oxdsZ5yqpxtsffiDrXTAgUdcGrtIgi0Pk3ATQhQM8+bB9OlQsiSJpcvzzTfq5uqcIbBQc71gaxoT9FSwKXzCF/xBX2Jw4k33Hbww5Y1cvwWRdRJuQoj8788/1SlEypSBrVv5YUUxXY3t2cEGo5nLF0ziChXwJZg+C5vLxP55nISbECJ/27AB+vcHNzfYsgXKluXixdSmyGcHG4AfA/idt2htF8JXq6rSvXsu34MwmnQoEULkX1otTJwI9vawaRPUrIm/PwQtVoOt2ONgO+tUzSDYShPGC1xgN22IxI1+/M6OjdC2rZnuRRhFwk0IkX9ZW8O2bXDxIrz4Iv7+8GmPswQ+FWy+McF6wVaLk2zkFYoSQTXOcZ2yT8a1CYsg4SaEyH8uXICEBLVnpIcHeHig1cJ3Q88SWKhZpsHWnm2s5HWciGEM33IdL+Dx/JHynM1iSLgJIfKX0FBo3x5iYkg8/S/fLSvO6tXgcPUsqyKbZdoU2Zel/MJgkrHhdVaymu5PFh6V52wWRcJNCJF/3LmjBtv166xv9Q1dPYujKFCNs6wrZBhskbjpTn2Dv1hKf+5RjM6sp0QXH3aMSrPwqLAo0ltSCJE/REVBx47w77/sfPETugSM0QVb2mds55yqGgQbwGY6sY7ONCWI/fjwwQdq5xEJNssk4SaEsHyPHkHnznDsGNp3h9N+/+dA2mCLANRg840J1gVbIRJphLpAaRRF6Mo6/uWFJ4uPCosl4SaEsHwRERAeDm++yfwq81HQUJVzmQabEw/ZyCvsoSX1OKp3Oek8YvnkmZsQwvKVKQPBwVCkCBdGWVGVc+wr1CzDYCvBbTbyCg05gj+vcZbqABQqBMuXS+eR/EBqbkIIy6Qo8OmncOyY+t7dnW49CxGw8Olge0Ev2CpymSCa0pAjLORdevIPCdgDMGOGBFt+IeEmhLBMkyfDl1/CmDGgKHTrBhfWphdsIbpgq8ZZgvGlEpeZzDSG8SMpqO2P1tYwcqS5bkbkNGmWFEJYnm+/hS++gCpVYMUKHsVrOL/2PEGZBBvADTwJpSyT+JyfeEfvkmPGgK1trt6FMCEJNyGEZfn1V/jwQx65ezGo+Hb21C1BsbvnCSrUVC/YmqZpiixFOLfw4CEu+BCCNs1Xn0YDY8fCzJlmuRthItIsKYSwHGvWoAwZwh3cqXt3O8uDy+ISfp4AjWGwPaAoAMNZwGW8acEeALTY4OQEvr7qYqXx8RJs+ZHU3IQQFmPznQa4pTRiKAv5lxd4gfN6z9jOO6YNNoVP+YLPmUwYpYl4HHYAn3+uLu8m8i+NoiiKuQuRW6Kjo3F1dSUqKgoXFxdzF0cIkVWJiWitbfHygvBwBdDogq140n1ADTbfWDXYNKTwDR/yAXO5hDft2c41KgBqx5G4OHm+ZomM+Q5/7mZJrVbL8ePHefDgwfNeSgghDAUFQZUqHPvlKOHh8KxgsyaZXxjMB8zlJLVoTqAu2EA6jhQURofb6NGj+eWXXwA12Fq2bEn9+vXx8vIiICAgp8snhCjIjh6Fl1+GmzeJuXwbgCpcyDDYAOyJpwZnCMaHluzhFh66y40bJ8/XCgqjw23lypXUqVMHgPXr13P16lXOnz/PBx98wCeffJLjBRRCFFBnzsBLL0FMDCxbxsL/OlGFCwQVappBsKlPWGJxohObac92vWEAs2ZJsBUkRofbvXv3KFWqFACbNm2iZ8+eVKlShUGDBnHq1KkcL6AQogC6dElduub+ffj1V8YffJ1jKzIOtmLcYy8taMNOACIoRhyOustZW8OoUWa5E2EmRodbyZIlOXv2LFqtli1bttC+fXsA4uLisJaZRoUQOWHIEHUi5AULSOzdn7WzLrDP9klT5AXHKrpgK8MN9tKC5uyjM+vTvZw8Zyt4jB4KMHDgQHr16oWHhwcajYZ27doBcODAAapWrZrjBRRCFEC//w6bN8OQIfz1sRps7on3AP1gq8RFttOe8vzHDD5iItP1LiMDtAsuo8Nt6tSp1KxZk+vXr9OzZ0/s7OwAsLa2ZsKECTleQCFEARERAbdu8ahCdd6b6om//xBKDb3AHhvDYIugGLU5wVY6UIrbfMQMZvIRAMWLq7Nyvfaa2hQpNbaC6bnGucXHx2Nvb5+T5TEpGecmRB4VHQ1t2xJ98iq1Eo8QSjkq8y9Btk3TDTaATXSiA1t5l0X8zBDdpXbvhlatzHETwtRMOs5Nq9Xy+eefU6ZMGZycnLhy5QoAkyZN0g0REEKILIuLg1dfhcOH+SuxB6GUfWawAfThT7qwTi/YZAVtkcrocPvyyy/x8/Nj5syZ2Kap79esWZOff/45RwsnhMjnEhLU9sPAQP7kTYbzA5W5qBds/zpW1gXb6/xDe7YB8ICibORVvcvJCtoildHhtnTpUhYvXkyfPn30ekfWqVOH8+fP52jhhBD5WFIS2tf/B9u2sVbTjQH44c1lg2DziQ0hgmIMYAnLeYNfGYQd8QaXmzZNFhoVTxgdbmFhYVSqVMlge0pKCklJSTlSKCFE/jfw1bv8t+EkW3mJXspyKnBVr1dk2mAbzgKWMIgIivIqG3QrZ6dVuXJu34HIy4zuLVm9enUCAwMpV66c3vaVK1dSr169HCuYECL/6tYN1m4rzVb2EUkRyvEf+2ybUSLxLqDfFDmer/maCYRRmnbs4DzV0r2mh0e6m0UBZXS4TZ48mf79+xMWFkZKSgr+/v5cuHCBpUuXsmHDBlOUUQiRXygKSZ9O4/LaHkAtwilNJS6mG2z3Kc7b/MTXTOAq5WnLTq5SMd3LSkcS8TSjmyW7du3K+vXr2bFjB46OjkyePJlz586xfv163WwlQghhQFFg4kQKfTWNebwPQKXHnUdSg+2iYyVdsAGs5HX+pict2JthsIF0JBGGLGo9t7179zJr1iyOHDlCeHg4q1evplu3blk+X8a5CWEeWi1c7juVKn9N4yzVaEUArkQZBJtPbAgPcKM6ZzlNrWde19kZ/PykI0lBkavrueWm2NhY6tSpw4IFC8xdFCFEFvn7wxTb6VT5axoXqURbdmYYbFG4spR+HKAJjTiodx2NRg2zcuWgd2/Ytg0ePJBgE+nL0jM3Nzc3NBpNli4YERHxXAXKTKdOnejUqZPJri+EyFn+/rClx2IW8zFXKU8bduFEDPvSCbaHOPM3vXiNNeylOefRn6s2NhYcHMxxF8ISZSnc5s6da+JimEZCQgIJCQm699HR0WYsjRAFi1YLI0aAHS8RQEsGsgR74tln25SSqcFWWA22OAqzji50YBtbeYnXWM0jCuuu1bWrBJswTpbCrX///qYuh0lMnz6dadOmmbsYQhRIwdtiuHXLCShPawLw5pJhsMWFkIgtW+hICwJZQ1f+xwoSsdNdp2tXWLPGPPcgLFeWnrmlrfFER0dn+spLJk6cSFRUlO51/fp1cxdJiAIh/uc/qNqlMjVRFzD25hJBtr4GwXaf4liRgjMPWUZvevIPidhhZaUu6RYXJ8EmsifLz9zCw8MpUaIERYoUSff5m6IoaDQatFptjhcyu+zs7HRL8gghcsfHlf7m88v9saIIGpR0g803LpgIigIQRRFas5uHOJOC2p+/SxdYvNhstyDygSyF265duyhaVP0Pcffu3SYtkBDCco2rvIbpl9/kIc60ZztxFE432OxI4DAN+YA57KUlURTRu87vv5uh8CJfyVK4tWzZUvdzhQoV8PLyMqi9KYpi8ma/mJgYLl26pHt/9epVjh8/TtGiRSlbtqxJP1sIkblHqzbx5aVePMKBDmwlGheCnnrG5hsXjDMP2UlbKnCN5gSyl5Z612nUCJyczHEHIj8xehC3tbW1rokyrfv371OiRAmTNksGBATQunVrg+39+/fHz8/vmefLIG4hTCQ5mTC3GhSJuUFHthCOx+NguwPApcLe+MSFUIz77KQtZbjJBKbzNRP0LtOoERw8mN4HCGHcd7jRc0umPlt7WkxMjMlX5W7VqhUWNKGKEPleVBR06ACHD9vgqd1KOf7jJqUJtvU1CLZS3GIH7SjJHd7jO77nPUCdNqtjR1i+XGpsIudkOdzGjBkDgEajYdKkSRQu/GQMilar5cCBA9StWzfHCyiEyJsqVYLil/cThStaqvEf5bFGm26w3cOdP+mDO3d5m5/4hbd11xk6FL7/3lx3IfKrLIfbsWPHALXmdurUKb1VuG1tbalTpw5jx47N+RIKIfKcSpWgyOXDbKUDD3GmMhcpzU2DYPONC+Ye7gC8yTKaE8gaXtO71qxZuV58UQBkOdxSe0kOHDiQefPmyTMrIQqoqChwvHyCbbyEI7EM5pcMg60Sl6jGOfbSkvsUNwg2mXlEmIrRz9yWLFliinIIISzEiNZn2UE7ihDJW/zBMeoRZNeUkglqsF0uXBHfuGCqcY6NvEIitpTnGg/R/wexzDwiTMnocIuNjWXGjBns3LmTO3fukJKSorf/ypUrOVY4IYT5JSbCzJnw9dfgEfMve2iLO/cYyK8coAlBdk0plXAbUIPNJy6E2pxkHV2wRssbLNcLtrffhu++kxqbMC2jw+3tt99mz5499O3bFw8PjyyvFiCEsDzjx+s/E0vBikRsGcqP7KElwXa+BsHWkMP40x0FDV1YxzY66M5v2hR++im370IUREaPcytSpAgbN26kadOmpiqTycg4NyGyTj/YFED9h6wjMZTgTrrBVpuTbOJlkihEZ9azmzZ614yMBFfXXLsFkc+YdLFSNzc33VRcQoj8KTHxSbCV4QZ7aUFVzgEYBNuVwhXwiQvhLiU4Tl0O0YiObDEINm9vCTaRe4wOt88//5zJkycTFxdnivIIIfKAH35Q//TgJrtpTXP20ZEtVOCKQbC9GLcf7eMJj+9TnGbsYx/N9a7n7Q1pZs4TwuSMfub2zTffcPnyZUqWLEn58uUpVKiQ3v6jR4/mWOGEELknJgbeeAM2b4aUFChFOLtpTWUuMYWprKWrXueR1GB7hY3MZTSd2EwIvqQ2X9rbQ926sGWL1NhE7jM63Lp162aCYgghzKlxYzh06Mn7EtxmF214gX/5nE/5nbcIsmuKR8It4ElT5GusZhFDuUtxYnHUnd+0Kezbl9t3IcQTRncosWTSoUQIQ08HG8AWOtCBbUxnAosZQnA6wdaLv5nPKG5Rkrbs5Cw1dOdLxxFhCiadOFkIkX/ExBgGG8AIFtCTf1jO/9INtr78zmzGcYMytGEXF6miO1c6joi8wOhw02q1zJkzh7///pvQ0FASExP19kdERORY4YQQptW375Ofi3IfNx5wmUpcphLLeYNgO98nweZQHp+4EKJwpR9L+Y+ytGEXV/DWXUM6joi8wujektOmTePbb7/lf//7H1FRUYwZM4bu3btjZWXF1KlTTVBEIYSp/Puv+mcRHrCd9uylBaUIpzxXDYPt0X7uUJIE7GnHDlqwlyt4U6gQNGmiNkVKsIm8wuhw+/PPP/npp5/48MMPsbGxoXfv3vz8889MnjyZ/fv3m6KMQggTaNwYzp4FVyLZxkvU5xhr6IY9j9IJthBGsAAfggG4SwlCKceIEeqYuP37pSlS5C1Gh9utW7eoVasWAE5OTkRFRQHw6quvsnHjxpwtnRDCJFI7kbgQxVY60IjDLOIdZjGWILtmesHm+yiYicxgMp/zDR+izlaikuVqRF5ldLh5enoSHh4OgLe3N9u2bQPg0KFD2NnZ5WzphBA5LrUTiTPRbKEjTTjILwxiBh8RZNeM0gnq/99XHMrT9FEQk/iC0czjJLXoylpSx7HJcjUiLzM63F577TV27twJwHvvvcekSZOoXLky/fr1Y9CgQTleQCFEzkrtROLOXcoSih/9+YJP9ILt6uNgm8JnjOAHjlGX1uzmLiUAWa5G5H3PPc4tJCSEkJAQKleuTOfOnXOqXCYh49yEgNq14dQp9efShGFHPPvsmusFm8+jEIbwE58zmcM04CW2oXUpSq9eslyNMJ9cHefm4+ODj4/P815GCGECWi2sXQtDh8Ldu+BAHIt5h2lM4RKVKURSusF2m1Is5h2q8C/vMZ+pc4owerR570UIYxgdbkuXLs10f79+/bJdGCFEzvH3h5491XkiAex5xFq60p4dRFKE2Ywl2M73qabIfbgQzW1KcYeS9ON3rK1h+HAz3ogQ2WB0uL3//vt675OSkoiLi8PW1pbChQtLuAmRB/j7Q48eT97bEc8autGeHaykB3N53yDYmj0KZAYT6cpaWrKHE9QFYMwYsLU1w00I8RyM7lDy4MEDvVdMTAwXLlygWbNm/PXXX6YooxDCCFotDBv25L0tCfjTnQ5sYzXdmMB09tq11Au25o/2MpOP6MfvnKYmV6gIwLhxMHOmOe5CiOdjdLilp3LlysyYMcOgVieEyH2BgXDnzpP3PzCcl9nMOjozjpl6wXbNoRzNH+1lFuPpwzICaUZHtvAQF2bNkmATlivHJk62sbHh5s2bOXU5IUQ2REXB4MH622YyHg0K05mQbrB9y4f04h/20IJX2EgsTgD8919ul16InGN0uK1bt07vvaIohIeH8/3339O0adMcK5gQwjiVKsHly+rPNiRRktuE4cm/vMA0phBi56MXbD6PQkjEllqcYhet6cx64tKsyebtnd6nCGEZjB7nZmWl35Kp0Whwd3enTZs2fPPNN3h4eORoAXOSjHMT+VXaYLMmmWW8iQ8hNCcQBU26wXYL9f/VEtzmIc48orDuetbWEBcnHUlE3mLScW4pqf2KhRB5QlSUfrAtpR+9+IedtMGOeHbZt6V0/JNga/kogJmM5ztGcZhG3KGkwTWlh6SwdLJYqRAW7pVX1D+tSeY3+vMmf7GHFozge3bZt6VMvPos/D+HsrR8FMACRvIqG7EhmTcx7OEsPSRFfmB0uI0ZMybLx3777bfGXl4IYaTQ0CfB1odl7KEF77KQnfbtDILtx8c9JzfwCgPw012jSBEYO1YNNqmxifzA6HA7duwYx44dIykpiRdeeAGAf//9F2tra+rXr687TqPR5FwphRDp0mqhcGEozU3asjPDYGv1aDeLGEoHtrGOzvTkHxo1tWPfPjPfgBAmYnS4de7cGWdnZ3777Tfc3NwAdWD3wIEDad68OR9++GGOF1IIYUh/eq2yNGMfNiQZBJvPoxAm8xkd2MYautKLv0nCFll+UeRnRveWLFOmDNu2baNGjRp620+fPs1LL72Up8e6SW9JkV/4+0OvHsnMZDzfM5KrVMSLUELsfQyCLZzSlCKc8czkI74mCVu8veHSJTPfhBBGMmlvyejoaO7evWuw/e7duzx8+NDYywkhjKTVwsihT56xleQ2E5lOsL2vXrC1ebSTokQQTmlu4cEY5gBIsIkCIVuLlQ4cOBB/f39u3LjBjRs3WLVqFYMHD6Z79+6mKKMQIo19AcnMuqsG216a8zmfEmzvi2d8GAChDl60fbSDJQwikOZU4iIAL7wAkZESbKJgMLrmtnDhQsaOHcubb75JUlKSehEbGwYPHsysWbNyvIBCCFVMDLzxejK9tz4JtiEsZqd9O4Ng82MgzQhiGb25SgUApkwBV1dz3oEQuSfbK3HHxsZy+fHIUW9vbxwdHZ9xhvnJMzdhqRo3hkOHYB6jGMX8DIOt3aPt+DEQX0L4gz4MwA/t43/D7t4NrVqZ8SaEeE65shK3o6MjtWvXzu7pQogsSg02gEW8S2lu8glfGARb+0fb+I0B+LCf3+jHIH4lBWsAypSB5s3NdQdC5L4cWfJGCGEaMTFw5JCWktwC4Cw1+IA5BsHm8yiEh7hQjPssYYBesAF89506X6QQBYXFhduCBQsoX7489vb2NGnShIMHD5q7SEKYTP+3tCylH/t5ES9C8eS6QecRn0ch3KQM4ZTGl2De5mddsFlZwapVIH29REFjUeG2YsUKxowZw5QpUzh69Ch16tShQ4cO3Em7MqMQ+YVWy6CAfvRhGdfxwoE4gu198Yq/AUCovSftH21jIUOpyzEA7lNcF2xubpCYKMEmCiaLCrdvv/2WIUOGMHDgQKpXr87ChQspXLgwv/76q7mLJkSO0iZq+a9VP16JUlfHHszP7LBvrx9s8dv5lcF0ZgMDWWJwjerVpSlSFFxZ6lDy9AKlmenSpUu2C5OZxMREjhw5wsSJE3XbrKysaNeuHSEhIemek5CQQEJCgu59dHS0ScomRE7y/0fLo15qjS012Hbat9MLtnbx6ji2pgTzG/344PEA7bRkei1RkGUp3Lp166b3XqPRkHYEQdpJkrVabc6U7Cn37t1Dq9VSsqT+2lMlS5bk/Pnz6Z4zffp0pk2bZpLyCGEK/v4wptd1DrEtw2BrH79dF2xL6WvQeQTUWUhkTJsoyLLULJmSkqJ7bdu2jbp167J582YiIyOJjIxk06ZN1K9fny1btpi6vEaZOHEiUVFRutf169fNXSQhMqTVwtCh8B/laU5gusHmGx/Cp3ypC7aBLEk32GQWElHQGT3ObfTo0SxcuJBmzZrptnXo0IHChQvzzjvvcO7cuRwtYKrixYtjbW3N7du39bbfvn2bUqVKpXuOnZ0ddnZ2JimPEDlKqyWsz0Qc7o4AyhGDEyH2Prpgu/442MLwZCLTCaMMn/ClXrA1bgzbtkmNTQjIRoeSy5cvU6RIEYPtrq6uXLt2LQeKlD5bW1saNGjAzp07ddtSUlLYuXMnPj4+JvtcIUxOq4X+/Sm7YhZf8TFluEHwU8HWNn4HzqgTk6sBN0Mv2EaOhAMHJNiESGV0uDVq1IgxY8bo1aBu377NuHHjaNy4cY4W7mljxozhp59+4rfffuPcuXMMGzaM2NhYBg4caNLPFcIUHj2Cgf20LCvUH/78k3005TMmEWzvQ9mngu0X3iaQ5lTgSrrX8vbOzZILYQEUI128eFGpWbOmYmtrq3h7eyve3t6Kra2tUqNGDeXixYvGXs5o8+fPV8qWLavY2toqjRs3Vvbv35/lc6OiohRAiYqKMmEJhXi2rl0VxYpkZSlvKQoogTRVKnNe+c/eU1FAUUAJtfdUKnNe2UszRQHlD95UrEhO3a17WVkpSkKCue9ICNMz5js8WxMnK4rC9u3bdb0Uq1WrRrt27fR6TeZFMnGyyAu6dYO1a59MgryPpgziF3bYt9OrsbWL385PvEMLAvmTN+nHUoPOIwDjxsHMmbl8E0KYgTHf4dleFQAgPj4eOzu7PB9qqSTchLk9egSFC6s/1+YE05jCeL5ON9gW8y4t2csyetOPpbrZ/dOSYBMFiTHf4UY/c0tJSeHzzz+nTJkyODk5cfXqVQAmTZrEL7/8kr0SC1FAfPRhsm4S5JPUYQQL9ILthn0ZfOODicUJL66nG2waDXz+OSQkSLAJkRGjw+2LL77Az8+PmTNnYmtrq9tes2ZNfv755xwtnBD5gVarDs4u7Z5E8x97E0RTPLhJacIIcfDRCzaf+BBu4EUYnvgQkm6NzdMTPv0U0vzvJ4R4itHhtnTpUhYvXkyfPn2wTjNxXZ06dTKcKUSIgsrfXw2hN3okMv/eG/RkJf9RjsLEqsH2SJ1Y4IZ9GdrE72QOH1CLkwDcoWS6TZFly+bqLQhhkYwOt7CwMCpVqmSwPSUlhaSkpBwplBD5gb8/9OgB1imJ/E0veuDPdtoxhMXscmhrEGyLeZfXWcVQFmZ6XZkzUohnMzrcqlevTmBgoMH2lStXUq9evRwplBCWTquFYcPAlgRW0YNurGUrL/EuC9nt0EYv2NrG72ARQ2nFHv7iDUbxXYbXlTkjhcgao6ffmjx5Mv379ycsLIyUlBT8/f25cOECS5cuZcOGDaYooxAWJzAQ7tyByvyHL8FsohMj+J49Dq0Mgm0hw2hNAMv5H335Pd2mSJA5I4UwhtE1t65du7J+/Xp27NiBo6MjkydP5ty5c6xfv5727dubooxCWJzwcPXPi1ShKUEMZ4FesIXZl8Y3PpiPma4Ltrf4wyDYrKzUOSMjIyXYhDCGUTW35ORkvvrqKwYNGsT27dtNVSYhLEZEBPj6woULT7bZ84h5vE8ZJhOGJ9G46HUeCbMvjU98CNcpyxSmEUFRxjFLL9hGjIDvv8/tuxEi/zB6ELeTkxOnT5+mfPnyJiqS6cggbpGTSpWCpxapwIE41tOZtuxiLu8zk/GEOPhQ7lEooAZb6/hdKFhxicoZXjsuDhwcTFl6ISyPSQdxt23blj179mS7cELkB+kFW2Fi2cgrtGUX//A63/KBQbC1it/NAkYSRNMMJ0Hu2lWCTYjnZXSHkk6dOjFhwgROnTpFgwYNcHR01NvfpUuXHCucEHlRRIRhsDkSwyZepgWBLOd/jGMm+xyaG9TY5jOK9uxgJT24jpfBtbt2hTVrcuEmhMjnjG6WtLLKuLKn0WjQarXPXShTkWZJkRNq1YLTp/W3raErXVnHn7zJR8wgyKGZQY1tLh/wCptYTTd68TfJFALAzg769FGfsUmNTYiMGfMdbnTNLSUlJdsFEyI/uHnTcNtnTOYGnnzFRIIdmuqC7aadBy3jA/iWD3mFTayjM/9jhS7YvLwgNDQ3Sy9EwWD0M7e04uPjc6ocQuQ5iYnw1VdQtKg6WXHqKyJC3e9KJB6oSXeUBnzBp3o1tpt2HvgkhBCPA3U4wUZepif/kMSTSSFlKi0hTMPocNNqtXqrAly5oj4Ul1UBRH4yfrzaXPjJJ/DggeH+IjxgO+3ZTWuKcp9ShBPi4EP5R/8BT4ItlHKE4UlTgujBKhKx07uOTKUlhGkYHW5ffvmlrAog8rXx42HWrIz3uxHBDtrRiMPsoSV2xBsEW7OEQMYym2qcBSAMTxKw17uOTKUlhOnIqgBCpJGYmHmwFeMeO2lLA47yI0OZyhSCHJoZBNtnTOE9vmcK09K9jkylJYRpyaoAQqTxww8Z7yvOXXbRhnoc53tG8BmTCHJoRoVH14AnwTaFz3iLP9lLcwbxq+58a2uZSkuI3GJ0b8nUVQHKlSunt11WBRD5weXLGe8rQxhlCWUeo5jOBEIcfPWCrWnCPj7lS/qzlMO2vrS4t5FYZ8eMLyiEMBlZFUCINLy9M953grrU5Tjx2BkEm29CMO+wmMH8yn6aMLneZrY5O+dOoYUQBmRVACEeS0yE6Gj9bWW4wTo6Uwp1mv947NMNtv8ozy8MZhXd6cBW/tkqkwQIYU5Gz1BiyWSGEpGR9HpIluU/dtEGb67wHt/xDz0JcfB5KtiCiMeB25TSnSedRYQwDZNOnCxEfpNesFXkMntpgTdX+JTP+YeeBKcTbKOYzzHqUQV1zRsJNiHyhiw9c3Nzc0Oj0WTpghGp0zcIYQHS6/pfhQvsog1luMk4ZvI7fQl28KHiU8E2nB8ZwxxOUoty9YpxcLeMWxMir8hSuM2dO1f38/379/niiy/o0KEDPj4+AISEhLB161YmTZpkkkIKYSpPd/23QssaulGGm7zHd/xNL0IcXjQItkX1f6LD0VlQvTq1d+9gW4niuV94IUSGjH7m1qNHD1q3bs3IkSP1tn///ffs2LGDNXl4vQ555iae9t57hiteN2E/NTnNejrrBVu4XSl8EkIYxK9M5nOoWhUCAqBkyVwvtxAFkTHf4dlaifv48eMGA7kvXbpE3bp1iYmJMb7EuUTCTWi1sGEDfPABXL36ZHs9jhKOB7fwAKAEtw2CzTchmGhcOEltbIo4UfJsAHh45P5NCFFAmbRDSbFixVi7dq3B9rVr11KsWDFjLydErvH3VydD7tZNP9heJITdtGYLHbEmOcNgu0YFIihGS/bgcmiXBJsQeZjRg7inTZvG22+/TUBAAE2aNAHgwIEDbNmyhZ9++inHCyhETvD3hx49DLc3Zy+beBk7EviMyRTjfrrB1pEt7KQtF6lCza6VcDCcgU4IkYcYHW4DBgygWrVqfPfdd/j7+wNQrVo19u3bpws7IfISrRaGDzfc3pYdrKML1mjpjj8HaUxIYR8qxl0D1GBrmhBEV9Yylw8I4UW+7hLMmjVZ6zkshDAfo8ItKSmJd999l0mTJvHnn3+aqkxC5KjAQLh9W39bB7awhm4oaOjKWo5R73Gwqe2VqcHWic3M5QPCbTypd+JP1lSXYBPCEhj1zK1QoUKsWrXKVGURwiTCww23PcCNB7jxKhsMgu2WXUmaJgTRhl0sYCQ38eDjF3djX71iLpdcCJFdRnco6datW57u7i/E04qnGYJmTTIAB2lCRa5wiloGweabEExjDrKYd7hNCdqwC8c68pBNCEti9DO3ypUr89lnnxEUFESDBg1wdNRf0mPUqFE5VjghnlfaqbX68AfjmEU7dnAPd5x5yP7CLxoE21UqUogkTlGLt/iDC1TlWCYLmAoh8h6jx7lVqFAh44tpNFy5cuW5C2UqMs6tYEkbbAP5lZ95m0iK0IZd3KR0usF2A0+SsAVAQwoKVnTtCtJYIYT5GfMdbnTN7WraAUJC5FFp54x8l4UsZBh3cKc92wnHI91nbFU5z3ba04nNXKSKBJsQFizbqwLcu3ePe/fu5WRZhMgxqXNGjmIeCxlGOKVoRYAu2Lzj1BaG1GArzzX86U4pblHF9Q5DhkBcnASbEJbKqHCLjIxkxIgRFC9enJIlS1KyZEmKFy/OyJEjiYyMNFERhTDe5ctQklt8ziRuUIaW7OEexfWC7bZtCZomBFGGMNbTGY2VBsed69kQ2YzFi8HBwcw3IYTItiw3S0ZERODj40NYWBh9+vShWrVqAJw9exY/Pz927txJcHAwbm5uJiusEFnl7Q23KUUnNnOT0sTiSHBhX71g800Mxp27bOQVbEjmp5fXMaJNGzOXXAiRI5Qsev/995WaNWsqt27dMtgXHh6u1KpVSxk9enRWL2e0L774QvHx8VEcHBwUV1fXbF0jKipKAZSoqKicLZwwm4QERfn6a0WpWlVR7O0VxVqjVSbylVKcOwooCiiKO7eVi4W9ldQNt2xLKBW5pFiRrJylqpKIjdKFNUpcnLnvRgiRGWO+w7PcLLlmzRpmz55NyXSW9yhVqhQzZ85k9erVORi7+hITE+nZsyfDhg0z2WcIyzJ+vDoR8kcfwfnzkBivZbHyNl/xMd/wIQDFuUtwYV8qxV0GntTYruBNCtZ0ZS29+BtN167SDClEPpLlZsnw8HBq1KiR4f6aNWty69atHClUeqZNmwaAn5+fyT5DWI603fwBbEjid/ryBivYTxPeZx7FuUtIYR+9YGuaGEQhkqjAFa5SkYtUoXrXKtJxRIh8JsvhVrx4ca5du4anp2e6+69evUrRokVzrGA5ISEhgYSEBN376OhoM5ZG5JS03fwB7Ijnb3rRhfXsphVdWIc98ekGmwaFXbRBizXVOEdYpBOurma6ESGEyWS5WbJDhw588sknJCYmGuxLSEhg0qRJdOzYMUcL97ymT5+Oq6ur7uXl5WXuIokckNrNP9UfvEUX1rOJTrzMpnSCzZ2miUFosWYXbShNONOZSCxOLFlihhsQQphclsPts88+48KFC1SuXJmZM2eybt061q5dy4wZM6hcuTLnzp3TNR1m1YQJE9BoNJm+zp8/b/RNpZo4cSJRUVG61/Xr17N9LZF3XL6s/34uo1lKX7qxBidinnrG5k7TxGASsGMnbfHiBmP4hh8Znu61hBD5Q5abJT09PQkJCWH48OFMnDgR5fGsXRqNhvbt2/P9998bXTP68MMPGTBgQKbHVKyY/ZnY7ezssLOzy/b5Im/y9oZi3MOKFO5SgiCaEUQzXeeRynGXgCfBFosje2hJRa7yMV8yhzF61xJC5D9GTb9VoUIFNm/ezIMHD7h48SIAlSpVyvazNnd3d9zd3bN1rihYEhNh7lzw84OEa+EE0B4t1jRjHzE4Zxhsl6lEVc7hQjSfMYnpfKy7prV1+ouYCiEsn9FzSwK4ubnRuHHjnC5LpkJDQ4mIiCA0NBStVsvx48cBNVydnJxytSwid6XtGelFKDtpS2UuMZsPicGJYtwzCLZmiUFcRl2m5jzVqM1J7qL/D6kxY8DWNldvRQiRW0w/7C5n9O/fXwEMXrt3787yNWQQt+UZN0439lqpxL/KNcoqCihTmKJAilKMu8q/hSulGaDtrlTiX8WZKOUfeigVuKw7P+1r3Dhz35kQwljGfIcbveSNJZMlbyxLYqI6SBugOmfYQTs8uMU4ZjKbcRTjHiGFfXQ1tjuPe0XepDRb6Ehz9jGdCXzMdGxtwcMDhg2DDz6QGpsQlsikS94IkVvSdvl3JQonYhjOAn5kuBpsjj5UjtUPtlDKsp7ONGcf5xr0YeKBL5hobaYbEEKYjYSbyLMuX36yYGgIvnhzmbuUyDDYrlGef+jJS2xnNd0IaOLHPGtJNiEKomyv5yaEKWm1UOXfDYTggxsRAJkG2yUqM5fRdGMtW3mJN1hOhcrybzchCir5v1/kOf7+sP6NP/kpqT/x2FOV84Tgq/aKdPTVC7Zmifu4RGUAfmA4xbnHQJagtbaTbv5CFGBScxN5ir8/7OjxA78k9SUaF9qyUy/YqsSq4ytTg+0ilSnCAwDOUoM3WMEjCks3fyEKOAk3kWdokxUu9v+CHxjBLUrRgr0cpAlFua8XbHdtiz8OtipMZSrHqEcFruiuM24czJxprrsQQuQFEm4izzi4MpQRMTO4TEWasY8z1KQo9wlx9NELtqaJQVykCmOZxRQ+I0lji2cVR77+GhISJNiEEPLMTeQh15RyjGczl6jELTwyDbZh/MAsxvMfZTk7bwd73zNcRFcIUXBJzU2YV0ICjB0L9+/j4QH7aP7MYOvLUn5gBOGUoi07ca1V1sw3IYTIa6TmJsxGGxXDg1avUfz4Dn5aqGWi/RyAdIMt9RmbIzF8zUfcoxjt2EGCZyWaNzfnXQgh8iIJN2EW63+LoOTAl2msHMCf1xgZO4PEWDLsPPIvLwAQixNt2YkDjzhLDVbNU2f3F0KItCTcRK7b9Es45d9+iVqcZgkDGMJPaLHRBdsLsf8C+sHmSxDX8eI6ZTlHdRwcYNUf0L27mW9GCJEnSbiJXKVNSqHs0JepyWm+5QPGMhsFK4Ngu1eomC7YGnOALXTkJqWpZ3OaVesK8dJLUmMTQmRMwk3kqsAgKz5L/oYmHGAGEwANbkQQ5NhUL9iaJgXxLy9QmxNsoSMOPGIcs3iUXAgHBwk2IUTmJNxE7ti7F6pVIzzcnd20YTdtAHAjgmBHX6rGXgD0g60y/7Kd9rgSxZssYz1dAAgPN9tdCCEshAwFEKb399/Qrh306IFHqSfLB6YXbM2S1KZIT66znfaU4C7vsJgVvKE7z8Mj1+9ACGFhJNyEaX3/PbzxBhQuDF98QfMWGjw8Mg62C1QFoCgROPCIscziF97WXc7TE+n6L4R4JmmWFKahKDBpEnz5JYqHB7/13sriCbUICwPHhAh2ZRJsACepQ3XOcp/iepedJ13/hRBZIDU3YRoffghffsldtypUDA9m4Le1CAmBh6ERrE9IP9jsecQvDKIs/wHoBZuDA6xaJV3/hRBZIzU3YRrdunHlnyM0ubGSe7gDalNkkJMvVWPUYLtfqKgu2GxI4m960ZkNxNs484nTPBwdoWpVdZb/du2kxiaEyDoJN5FzHjyAxEQoWZLEF1tQ6UYAChrgSbBVSxNsTZOCuEBVNKTgxwA6s4FNdGJ2yVnc+0/CTAiRfdIsKXJGWBi0aAGdOkFsLD/8QJaCDRS+YxR9WMZemvM6K7kaZktgoBnvRQhh8STcxPM7cwZ8fOD0aWjdGhwcuHxZ3VWEBwbBlrbzyMd8xUgWcIy6dGY9jygMyFg2IcTzkXATz2f3bmjaFK5fh1mz4JtvwMoKb2812IKdfAyC7TzVdKevpzN7aEEHthKNq267jGUTQjwPeeYmsu/vv+Gtt0CjIeWvFewo2ovf+sDVq2AVlXmwFSKRJGw5RW1aEQCPmzBBxrIJIZ6fhJvIPk9PcHdnz7DlvDqkOTEx6ma1xqbfFNk8KVAXbK/hz1d8TCc2c40KpA02kLFsQojnJ+EmjJOcrPaKdHcHX1/WfHOZ13rb63Y/CbbzwJNgO0d1ANqyg7/oTQJ2uPHgcbipnJ3Bz0/Gsgkhnp+Em8i6mBh1Kq3QUAgMROvkyogPMws2N71ga8wB1tCNFKzozHrul61PDWeoUwcGDIA2baTGJoTIGRJuImtu3YJXX4UjR+Dll8HamsBAuHlT3a32imz6VLDt0wVbVc6xiZexJ55urGEvLZnzAYwebab7EULka9JbUjzb+fNqV/8jR+Cdd2DtWnBy0nXXTw226jHnAMNg05DCX/SmGBEMZAkbeRVAN1xACCFymtTcROYCAuC11yAyEr76CiZMAI3aAcTDA1yJzDTYABSseIs/aEoQf9BXt93bOzdvRAhRkEi4icw5OalhtmwZ9O4NqDNsff89HN4RSbCzL9UfPgm2FmmesTkQhwvR3KYUZ6jJGWrqLmttDcOH5/7tCCEKBgk3YUirhfv3oUQJaNgQrl0DFxcAxo9Xx2k7p0QS7OSrV2NrkRTIWWoAYEMSK/gfNThDC/YShqfeR4wZA7a2uXpXQogCRJ65CX0xMWpf/FatICpK3ZYm2GbNMgy2iKeCDRQW8w6d2cA5qnGbkrrLW1mps/zPnJmL9ySEKHCk5iaeuHEDOneG48ehY0e9XYmJao3t6WdsEY+7+z8JNpjORAbiRwgvMvmFv6ntWAhPT3Ve5ffekxqbEML0JNyE6vBh6NJFnbF45EiYMwdsnvzn8cMPao0tyKkpNWLOAukH22jmMIGvOUs1XmUDqxY60qpVbt+MEKKgk2ZJARs2qNWq27dh/nz1ZaP/756bZ58dbEV4wCd8yXU86cBWIigms/sLIcxCam4CvLzA1RV+/VVdj+1pkZGM2twMzzTB1iJpr16wAUTiRnMC0aBwAy9AZvcXQpiHhFtBFR0N9+5BxYrq/FdXroCDg+FxkZFE+TbD88YZ4Emwpe3WX4+j3KYkNymjt5yNzO4vhDAXCbeC6MIF6NZN7fJ/6JBaa3scbFqtOm571y64828kE/c2o+IdNdgi7dxokaAfbFW4wDZeIpIi1OAMidjp9sns/kIIc7GIZ27Xrl1j8ODBVKhQAQcHB7y9vZkyZQqJiYnmLprl2bQJGjdWp9Tq3l0dpP2Yvz+ULAnt2sH3X0Xx/tYnwfbApgjNngq2UoSzlQ4U5z6fM0kXbM7OsGqVzO4vhDAfi6i5nT9/npSUFBYtWkSlSpU4ffo0Q4YMITY2ltmzZ5u7eJZBUeDrr+Hjj8HeXm/GEVCDrUcP9WcXoghybkrNh0+CrXlyoF6wOfGQjbxCef5jAtM58EJ/+jSE/v1ldn8hhPlpFEVRzF2I7Jg1axY//vgjV65cyfI50dHRuLq6EhUVhcvjgckFxrhxMHs2lC0La9ZAvXq6XVotlCsHYWFZCzYbklhPZzqylQUMZyTf4+mp4do1CTUhhOkY8x1uETW39ERFRVG0aNFMj0lISCAhIUH3Pjo62tTFyrv694dz52DJEnWh0TQCAzMOthbJ+k2RABW4SgOOsIaujOI7QMONG+p1ZEybECIvsIhnbk+7dOkS8+fP59133830uOnTp+Pq6qp7eXl55VIJ84iVK+HECfXnmjXV8WxPBRuo47ZdiGJfOsF2mloGx1+kCk04wJssIwVrvesIIUReYNZwmzBhAhqNJtPX+fPn9c4JCwujY8eO9OzZkyFDhmR6/YkTJxIVFaV7Xb9+3ZS3k3ckJqqrgPbsCYMHq8/bMuHprAZbrWcEW3dW4Yn6O7xKRR5RWG+/jGkTQuQVZn3mdvfuXe7fv5/pMRUrVsT28WSEN2/epFWrVrz44ov4+flhZWVcNheIZ27Xr0OvXrB/P9SqpdbeqlTJ+PioKCJ9mlLk3JNga5m8h1PU1jusA1vYwKucohYNOILy1L+LPD2RZ25CCJOymGdu7u7uuKfTTJaesLAwWrduTYMGDViyZInRwVYgbNsGffqog7P791cnhCysX7tKHccWEACF4qIY+E8zvK5nHmz1OMpKXicRW4ay0CDYQMa0CSHyFovoUBIWFkarVq0oV64cs2fP5u7du7p9pUqVMmPJ8hCtVl0k7eFD+OkntTny8YrZqfz94Z131KXa1GdszfB6eBqASBvXdIOtHNfYxMs48IjXWM1BmujtL1YMFi+WMW1CiLzFIsJt+/btXLp0iUuXLuHpqb/opYWOZMg5cXFq7czaGv76C5KT9br5p0o7js2ZaPY5N6NWmmBrkbzXINjciGAznSjFbX5p+CNOlbvQG3VNtnLl1PFsrVpJjU0IkfdY7Di37Mh3z9x+/12tre3Yoc4PmYG049iciSbIuekzgw2gBqfZQTtWOg9i2IOvJMSEEGZlzHe4PLiyRNHR0Lcv9OsHSUlqamUidRybMcEGcIaa1OU47z38ksDAHL8LIYQwGQk3S7N9uzpm7Y8/wNdXHcf28suZnhIenn6wpfeMDWAk8ynHNQBuUwrQyBg2IYRFkXCzJCtWwEsvwa1b8PnnsGeP2t74DJ4uhs/YWibv4SSGTZkD+ZX5jOI3+uttlzFsQghLYhEdSsRjr76qdkucMgVqp9+UaCA6mlofNaPIw1NA5sHWit0s4l3uUYxB/AqoHS5lXTYhhKWRmlte9vAhDBsG33+vvnd0VNeSyUKwabUQuDGasDrNKHJGDbYoG5cMg60KF/CnOylY0Y01XMFbt2/uXOkRKYSwLBJuedXu3WqILVyoLk+TkpLlU/39oWbZaFzebE6Za0+CrUXy3nSDrRj32MgruBHJIH4liGYAeHmpE5zIGDYhhKWRZsm85u5dGD8e/PzAxgYmT4ZPPlEHl2WBvz8M7BHNXpfm1Ik+CWQebH37Qj3b2xRd9ogdDaZSv1sfXi4FZcqoTZFSYxNCWCIZ55aX3LkDVavCgwfQoIE69Uf9+lk+XauFGmUfsiKmmV6wtUzewwnqGhyf+jzt6lWwfnBPnW7kqVlNhBAir5BxbpZGq1X/LFFCXR17wQI4cMCoYAMI3pr1YAN4R1mI7fVL6hi24sUl2IQQ+YY0S5rTzZswdar65/r1argsWJC9az18SMWRzSiTxWB7g79YyDAO0JgrN/cDEmxCiPxDam7mEBUFn34KlSqpkxyHhkJERPav9/Ahkb7NKHP1SbC1Sg7IMNh8CGYJA4nElf78hkdpCTYhRP4i4ZabEhPVtWG8veHLL9VnXEuWwLFj6s/Z8fAhD5o2o8hpNdiiC6nBdhzDyZNBneV/Dd2wRsvrrCLOq6qMYRNC5DvSLJmbEhLgq6/UZ2wzZ8LIkeDgYNQltFp1rsjwcPBwesgLHzbD46IabPGOLizqEsDxv9IPNkdiWEtXSnCXISxml6YtK+dKj0ghRP4j4WZKigKbN0NsLPTsCc7OsHq12iOyaFGjL+fvD++/DzdugBMPCXRphkeaZ2ytYjMONoDS3MSNB8xjFFu9hrByroxhE0LkTxJuppAaalOnwqFDai/ILl3Azk6d7Dgb/P3h9dfVS6vB1py60frP2DJqigS1kujtXYWD9kepW7kIV1tJjU0IkX9JuOWkp0NNo1G79k+apAZbNmm1ao1NP9hOAFkLtpfZxMmVlZh7owrW1sWzXQ4hhLAUEm456ehReOUV/VCrVu25LxsYmLYp0rhgq8sx/uF17t5yZ9+ui7Rsb/vc5RFCiLxOeks+j9Sa2vnz6vsGDdRekKdPq/NB5kCwgdp5xJEYg2Brnbw702ArwW3W0hV74hnOD9y8J8Em8r7r16/TqlUrqlevTu3atfnnn3/MXSRhgSTcsiM11F58UV0odPLkJ/s+/hiqV8/Rj/MsEsM+l2YGwXaMjGcwsSWBVfSgLNeZwAw28YqsySYsgo2NDXPnzuXs2bNs27aN0aNHExsba+5iCQsjzZLGUBTYskV9pnbwoNr8+MYbavOjqcTEUPOjZrgZEWyg8APDaUYQf9CH2YzDy0vWZBOWwcPDA4/H/xIrVaoUxYsXJyIiAkdHRzOXTFgSqbkZY8ECtaZ26JAaaqdPw19/5XhNTauFgAD4Z0kM4Q2a43bKmGCDYtynDbs4SCOG8BNoNLImm8gzWrZsiUajQaPRYGtrS7Vq1Vi2bFm6xx45cgStVouXl1eOl2PBggWUL18ee3t7mjRpwsGDBzM9/uHDh4wePZpy5crh4OCAr68vhw4dMvoYgLCwMN566y2KFSuGg4MDtWrV4vDhwzl6fwWeUoBERUUpgBIVFZW9C9y7pyj9+inKmTM5W7A0Vq1SFE9PRXHkoXLUpa6iqPVFJdLGRanHkdS3z3wV465SipuKl5d6TSHygpSUFMXZ2VmZPXu2Eh4erly5ckUZPXq0Ym1trVy5ckXv2Pv37yvVq1dXgoKCcrwcy5cvV2xtbZVff/1VOXPmjDJkyBClSJEiyu3btzM8p1evXkr16tWVPXv2KBcvXlSmTJmiuLi4KDdu3DDqmIiICKVcuXLKgAEDlAMHDihXrlxRtm7dqly6dCnH7zO/MeY7XMItD1m1SlE0GsNgi7JxTjfYpkxRlB07FGXZMvXPvcvDlHUzzylz5ijKH38oyu7dipKcbOabEiKNCxcuKIBy+vRp3bZTp04pgLJ582bdtvj4eKV58+bK0qVLTVKOxo0bKyNGjNC912q1SunSpZXp06ene3xcXJxibW2tbNiwQW97/fr1lU8++STLxyiKonz00UdKs2bNcupWChRjvsPlmVsekTqWrbCi9oqsF30cgGgb53SbIjUa+PVX9XGftTXqvJWtXlebSo8fh4oVc/0ehHiWI0eO4ObmRvXHTfk3btzgk08+wc7Ojtq1awOgKAoDBgygTZs29O3bN8NrffXVV3z11VeZft7Zs2cpW7as3rbExESOHDnCxIkTddusrKxo164dISEh6V4nOTkZrVaLvb293nYHBwf27duX5WMA1q1bR4cOHejZsyd79uyhTJkyDB8+nCFDhmR6L8JIps/avCMv19x271ZrbEdc6unV2OpzONPmx927H19gxAh1Q9++ipKSYsY7ESJjY8eOVaysrBRHR0fF3t5eARQHBwdlyZIlumMCAwMVjUaj1KlTR/c6efKkwbXu37+vXLx4MdNXUlKSwXlhYWEKoAQHB+ttHzdunNK4ceMMy+7j46O0bNlSCQsLU5KTk5Xff/9dsbKyUqpUqWLUMXZ2doqdnZ0yceJE5ejRo8qiRYsUe3t7xc/Pz5hfZYEkNTcLdPdqDHtdWlA/+hjwpMZ2lAaZnhceDvz2m9rZpW5dWLhQFh0VedbRo0cZMWIEo0aNIjIykrFjx9K0aVMGDBigO6ZZs2akpKQ881pFixalaDbmaM2u33//nUGDBlGmTBmsra2pX78+vXv35siRI0Ydk5KSQsOGDXW1znr16nH69GkWLlxI//79c+1+8jvpLZkXxMTQ9Gv9YGuTvOuZwQZQOeYYDB0Kbm7qBJSFC5u6tEJk29GjR/H19aVSpUo0bNiQH374ga+//ppr164Zfa2vvvoKJyenTF+hoaEG5xUvXhxra2tu376tt/327duUKlUqw8/z9vZmz549xMTEcP36dQ4ePEhSUhIV0zwCyMoxHh4eumbZVNWqVUu3rCL7pOZmbrGxPGjWgtIX9IPtCA0zPU2jAU9PaLB8nLqUzpo1UKFCLhRYiOy5cuUKkZGR1KxZU7etevXqeHt7s2zZMj7++GOjrjd06FB69eqV6TGlS5c22GZra0uDBg3YuXMn3bp1A9Ta1M6dOxk5cuQzP9fR0RFHR0cePHjA1q1bmTlzplHHNG3alAsXLugd/++//1KuXLlnfrYwQi40k+YZee6ZW0yMElHnyTO2Rw7OSkMOKRpN5t38NRr1tWqVoij37yvKihXmvhMhnunvv/9WChUqpCQkJOhtHzZsmNKwYcNcLcvy5csVOzs7xc/PTzl79qzyzjvvKEWKFFFu3bqlO2b+/PlKmzZtdO+3bNmibN68Wbly5Yqybds2pU6dOkqTJk2UxMREo445ePCgYmNjo3z55ZfKxYsXlT///FMpXLiw8scff+TOzVswGQqQgTwVbjExyv00wRbv4KQk7z+kG+eWNsysrfXfe3kpynq/e+a+AyGMMmHCBKV69eoG21etWqVoNBrl+vXruVqe+fPnK2XLllVsbW2Vxo0bK/v379fbP2XKFKVcuXK69ytWrFAqVqyo2NraKqVKlVJGjBihREZG6p2TlWMURVHWr1+v1KxZU7Gzs1OqVq2qLF682CT3mN8Y8x2uURRFMW/dMfdER0fj6upKVFQULi4u5itIbCw36rXA8+JRAB5aO9Fau5vbng2ZNw+6dk2z2raHugRccPCT980j12Hd7y1YvlydMUUIIQoAY77D5Zlbbssg2I7QEE2YuiDpypWGK2S3avX4h2vXoF5/dWCctNELIUS6pLdkboqNJaKpfrC10T7pPJJahx49Ws0uA4mJ8L//QWQkLFoENWrkSrGFEMLSSLjllthYIpq3oOgJ/WA7TCO9wxQFrl9XmyUNTJigrkYwaBC89VYuFFoIISyThFtuSA22Y5kHW1rh4U9t2LgR5sxRa2vz55uwsEIIYfkk3EztqWBLcHCirXZnpsEGGC4s6usLvXvDP//IQG0hhHgGCTdTio0lonlLXbAlOTlhs3Mn4Z6NM5whS6Mh/YVF3dxg2TKoVs20ZRZCiHxAws1UdMGmzimX5OhIoZ07sfZpzLx56iFPB1zqe72FRSdNUrv8CyGEyDIJtyxKXR37r7/UP9PtzZgqLk4v2BIdHNn10S4C4hqj1ard/FeuhDJl9E/z9HxqGMDatfDFF+orKckEdyWEEPmTDOLOAn9/da21GzeebPP0hHnzDMejERdHRLMWumCLtXGkdfIuDtHY4DytVn+wdvPmaWps165BvXpq9//Dh6U5UghR4BnzHW4xNbcuXbpQtmxZ7O3t8fDwoG/fvty8edPkn+vvrw6sThtsAGGPB1z7+6fZ+FSwxVjrB9vT51lbq4Oze/dW/9QFW9rxbD/+KMEmhBBGsphwa926NX///TcXLlxg1apVXL58mddff92kn5m6OnZ6dVuDAdfp1NjaanfqBVu656Vn0iR1PNvAgdCvX07cihBCFCgW2yy5bt06unXrRkJCAoUKFcrSOcY2SwYEQOvWz77uns1x1PikJcWOHgbUZ2zNH+3kIE0yPW/37jTTaqVKSYEePeDcOThyBBwdn10AIYQoAPL93JIRERH8+eef+Pr6ZhpsCQkJJCQk6N5HR0cb9TkGA6nT4UAc5d5vSbF/1WBLcnQk4KOdHJycebBleH0rK7XN8u5dCTYhhMgmi2mWBPjoo49wdHSkWLFihIaGsnbt2kyPnz59Oq6urrqXl5eXUZ9nMJD6KQ7Esce1JeXSBFuhHTuwbf7sYDO4vqLAvn3qzxoNlChhVFmFEEI8YdZwmzBhAhqNJtPX+fPndcePGzeOY8eOsW3bNqytrenXrx+ZtapOnDiRqKgo3ev69etGla95c7V3Y3oDrh2II6BIKxpF6QcbL76Y6XmQwUDtxYvVDd99Z1QZhRBCGDLrM7e7d+9y//79TI+pWLEitra2Bttv3LiBl5cXwcHB+Pj4ZOnzsjMUILW3JDzpDJIabI0jDwGQXLgwNjt3wosvZnoePAk8vfFs585BgwbqtFonT0Lp0lkqmxBCFCQW88zN3d0dd3f3bJ2bkpICoPdMzRRSB1ynjnPLSrCld14qT091BhJdsCUkqGMBHj1SZyKRYBNCiOdmEb0lDxw4wKFDh2jWrBlubm5cvnyZSZMmcfv2bc6cOYOdnV2WrvM8K3FrtRC0PY5yo1tT7sJBIONge/q8DAdqA3z4IXz7LQwbBj/8YFSZhBCiIDHmO9wiwu3UqVO8//77nDhxgtjYWDw8POjYsSOffvopZZ6ewyoTzxNuxMVxv2Vrih3OerA909Gj0LAhvPCC2u1fZvsXQogMWUyzZFbVqlWLXbt2ma8A8fGGwfa488hzqVtX7UDSpIkEmxBC5CCLCDdz277/GG6JKRQjTbBlsRNLpqysYOTI57+OEEIIPRY1zs0ctgceZNOegyzr9ibXu3bPmWDbuBFGjIDY2JwppBBCCD0SbpnYHniQTbtCAOjUrilea1Y9f7Ddvw9vvw2//AKhoTlQSiGEEE+TZskMpA22l9v40L5542eckQWKovaKvHULvvlGZvsXQggTkZpbOkwSbKCOY/vnH2jRQl0WQAghhElIuD3FZMF286b6nM3JCfz81M4kQgghTEKaJdMwWbABBAdDTIw6ULtChZy7rhBCCAMSbo+ZNNhAnWiyXj2oWDFnryuEEMKAtI1h4mC7fRvi49Wfvb0zXipACCFEjinw4WbSYEtJgTfeUKfYiozMuesKIYTIVIEON5M3Rf74IwQEQNWq4Oqas9cWQgiRoQIbbiYPtitX4KOPoFgxtROJNEcKIUSuKZAdSnYFH2bPgVOAiYItJQUGD1an1/rlFyhRImevL4QQIlMFsua2bY+60KhJgg1g4UK1ObJHD+jVK+evL4QQIlMFMtzAhMEG0KCBuirpggXSHCmEEGZQoJolU9dlbdawOk3qVCU6Oto0H1StGmzYoP5sqs8QQogCJvU7OytrbFvEStw55caNG3h5eZm7GEIIIZ7D9evX8fT0zPSYAhVuKSkp3Lx5E2dnZzQ53FwYHR2Nl5cX169ff+by53md3EveJPeS9+SX+wDLuBdFUXj48CGlS5fG6hnz8xaoZkkrK6tnpv3zcnFxybP/YRhL7iVvknvJe/LLfUDevxfXLI4ZLrAdSoQQQuRfEm5CCCHyHQm3HGJnZ8eUKVOws7Mzd1Gem9xL3iT3kvfkl/uA/HUvUMA6lAghhCgYpOYmhBAi35FwE0IIke9IuAkhhMh3JNyEEELkOxJuJtClSxfKli2Lvb09Hh4e9O3bl5s3b5q7WEa7du0agwcPpkKFCjg4OODt7c2UKVNITEw0d9Gy5csvv8TX15fChQtTpEgRcxfHKAsWLKB8+fLY29vTpEkTDh48aO4iZcvevXvp3LkzpUuXRqPRsGbNGnMXKVumT59Oo0aNcHZ2pkSJEnTr1o0LFy6Yu1jZ8uOPP1K7dm3d4G0fHx82b95s7mI9Nwk3E2jdujV///03Fy5cYNWqVVy+fJnXX3/d3MUy2vnz50lJSWHRokWcOXOGOXPmsHDhQj7++GNzFy1bEhMT6dmzJ8OGDTN3UYyyYsUKxowZw5QpUzh69Ch16tShQ4cO3Llzx9xFM1psbCx16tRhwYIF5i7Kc9mzZw8jRoxg//79bN++naSkJF566SViY2PNXTSjeXp6MmPGDI4cOcLhw4dp06YNXbt25cyZM+Yu2vNRhMmtXbtW0Wg0SmJiormL8txmzpypVKhQwdzFeC5LlixRXF1dzV2MLGvcuLEyYsQI3XutVquULl1amT59uhlL9fwAZfXq1eYuRo64c+eOAih79uwxd1FyhJubm/Lzzz+buxjPRWpuJhYREcGff/6Jr68vhQoVMndxnltUVBRFixY1dzEKjMTERI4cOUK7du1026ysrGjXrh0hISFmLJlIKyoqCsDi/9/QarUsX76c2NhYfHx8zF2c5yLhZiIfffQRjo6OFCtWjNDQUNauXWvuIj23S5cuMX/+fN59911zF6XAuHfvHlqtlpIlS+ptL1myJLdu3TJTqURaKSkpjB49mqZNm1KzZk1zFydbTp06hZOTE3Z2dgwdOpTVq1dTvXp1cxfruUi4ZdGECRPQaDSZvs6fP687fty4cRw7doxt27ZhbW1Nv379srTAXm4w9l4AwsLC6NixIz179mTIkCFmKrmh7NyLEDlpxIgRnD59muXLl5u7KNn2wgsvcPz4cQ4cOMCwYcPo378/Z8+eNXexnotMv5VFd+/e5f79+5keU7FiRWxtbQ22py6SGhwcnCeq+sbey82bN2nVqhUvvvgifn5+z1xHKTdl5+/Fz8+P0aNHExkZaeLSPb/ExEQKFy7MypUr6datm257//79iYyMtOgWAY1Gw+rVq/Xuy9KMHDmStWvXsnfvXipUqGDu4uSYdu3a4e3tzaJFi8xdlGwrUOu5PQ93d3fc3d2zdW5KSgoACQkJOVmkbDPmXsLCwmjdujUNGjRgyZIleSrY4Pn+XiyBra0tDRo0YOfOnboQSElJYefOnYwcOdK8hSvAFEXhvffeY/Xq1QQEBOSrYAP1v7G88n2VXRJuOezAgQMcOnSIZs2a4ebmxuXLl5k0aRLe3t55otZmjLCwMFq1akW5cuWYPXs2d+/e1e0rVaqUGUuWPaGhoURERBAaGopWq+X48eMAVKpUCScnJ/MWLhNjxoyhf//+NGzYkMaNGzN37lxiY2MZOHCguYtmtJiYGC5duqR7f/XqVY4fP07RokUpW7asGUtmnBEjRrBs2TLWrl2Ls7Oz7vmnq6srDg4OZi6dcSZOnEinTp0oW7YsDx8+ZNmyZQQEBLB161ZzF+35mLezZv5z8uRJpXXr1krRokUVOzs7pXz58srQoUOVGzdumLtoRluyZIkCpPuyRP3790/3Xnbv3m3uoj3T/PnzlbJlyyq2trZK48aNlf3795u7SNmye/fudP8O+vfvb+6iGSWj/y+WLFli7qIZbdCgQUq5cuUUW1tbxd3dXWnbtq2ybds2cxfruckzNyGEEPlO3nqAIoQQQuQACTchhBD5joSbEEKIfEfCTQghRL4j4SaEECLfkXATQgiR70i4CSGEyHck3IQQQuQ7Em4i3wkICECj0VjExMhpaTQa1qxZk2PXK1++PHPnzs2x6+W2a9euodFodNOkWerfqzAPCTdhUZ61vM3UqVPNXcRnmjp1KnXr1jXYHh4eTqdOnXK/QHnAgAEDDFYH8PLyIjw83GLXSBPmJRMnC4sSHh6u+3nFihVMnjyZCxcu6LY5OTlx+PBhcxSNxMTEdJc8yipLnIzalKytreV3IrJNam7CopQqVUr3cnV1RaPR6G1LO7v/kSNHaNiwIYULF8bX11cvBAHWrl1L/fr1sbe3p2LFikybNo3k5GTd/tDQULp27YqTkxMuLi706tWL27dv6/an1sB+/vlnKlSogL29PQCRkZG8/fbbuLu74+LiQps2bThx4gSgriU3bdo0Tpw4oatt+vn5AYbNkjdu3KB3794ULVoUR0dHGjZsyIEDBwC4fPkyXbt2pWTJkjg5OdGoUSN27Nhh1O9Sq9UyZswYihQpQrFixRg/fjz9+/fXq0Gl17RZt25dvRryt99+S61atXB0dMTLy4vhw4cTExOj2+/n50eRIkXYunUr1apVw8nJiY4dO+r+oTJ16lR+++031q5dq/udBAQEGDRLpmffvn00b94cBwcHvLy8GDVqFLGxsbr9P/zwA5UrV8be3p6SJUvy+uuvG/U7EpZLwk3kW5988gnffPMNhw8fxsbGhkGDBun2BQYG0q9fP95//33Onj3LokWL8PPz48svvwTU9ay6du1KREQEe/bsYfv27Vy5coX//e9/ep9x6dIlVq1ahb+/v+5LuGfPnty5c4fNmzdz5MgR6tevT9u2bYmIiOB///sfH374ITVq1CA8PJzw8HCDa4K6NEzLli0JCwtj3bp1nDhxgvHjx+vWBoyJieHll19m586dHDt2jI4dO9K5c2dCQ0Oz/Pv55ptv8PPz49dff2Xfvn1ERESwevVqY3/NWFlZ8d1333HmzBl+++03du3axfjx4/WOiYuLY/bs2fz+++/s3buX0NBQxo4dC8DYsWPp1auXLvDCw8Px9fV95udevnyZjh070qNHD06ePMmKFSvYt2+fbp27w4cPM2rUKD777DMuXLjAli1baNGihdH3JyyUuZclECK7lixZori6uhpsT11WZceOHbptGzduVADl0aNHiqIoStu2bZWvvvpK77zff/9d8fDwUBRFUbZt26ZYW1sroaGhuv1nzpxRAOXgwYOKoijKlClTlEKFCil37tzRHRMYGKi4uLgo8fHxetf29vZWFi1apDuvTp06BuUGlNWrVyuKoiiLFi1SnJ2dlfv372fxt6EoNWrUUObPn697X65cOWXOnDkZHu/h4aHMnDlT9z4pKUnx9PRUunbtmuk16tSpo0yZMiXD6/7zzz9KsWLFdO9Tl066dOmSbtuCBQuUkiVL6t73799f73MVRVGuXr2qAMqxY8cURXny9/rgwQNFURRl8ODByjvvvKN3TmBgoGJlZaU8evRIWbVqleLi4qJER0dnWFaRf8kzN5Fv1a5dW/ezh4cHAHfu3KFs2bKcOHGCoKAgXU0N1Ga6+Ph44uLiOHfuHF5eXnh5een2V69enSJFinDu3DkaNWoEQLly5fRWAj9x4gQxMTEUK1ZMryyPHj3i8uXLWS778ePHqVevHkWLFk13f0xMDFOnTmXjxo2Eh4eTnJzMo0ePslxzi4qKIjw8nCZNmui22djY0LBhQxQjV8HasWMH06dP5/z580RHR5OcnKz7PRYuXBiAwoUL4+3trTvHw8ODO3fuGPU5Tztx4gQnT57kzz//1G1TFIWUlBSuXr1K+/btKVeuHBUrVqRjx4507NiR1157TVcmkb9JuIl8q1ChQrqfNRoNgF6z3rRp0+jevbvBeanPzrLC0dFR731MTAweHh4EBAQYHFukSJEsX/dZqzmPHTuW7du3M3v2bCpVqoSDgwOvv/46iYmJWf6MrLCysjIIu6SkJN3P165d49VXX2XYsGF8+eWXFC1alH379jF48GASExN1QZL27wLUvw9jQ/RpMTExvPvuu4waNcpgX9myZbG1teXo0aMEBASwbds2Jk+ezNSpUzl06JBRfxfCMkm4iQKpfv36XLhwgUqVKqW7v1q1aly/fp3r16/ram9nz54lMjKS6tWrZ3rdW7duYWNjQ/ny5dM9xtbWFq1Wm2n5ateuzc8//0xERES6tbegoCAGDBjAa6+9Bqhf9NeuXcv0mmm5urri4eHBgQMHdM+hkpOTdc8IU7m7u+v1UI2Ojubq1au690eOHCElJYVvvvkGKyv1Ef7ff/+d5XKkysrv5Gn169fn7NmzGf4dglobbdeuHe3atWPKlCkUKVKEXbt2pfuPGpG/SIcSUSBNnjyZpUuXMm3aNM6cOcO5c+dYvnw5n376KQDt2rWjVq1a9OnTh6NHj3Lw4EH69etHy5YtadiwYYbXbdeuHT4+PnTr1o1t27Zx7do1goOD+eSTT3RDFMqXL8/Vq1c5fvw49+7dIyEhweA6vXv3plSpUnTr1o2goCCuXLnCqlWrCAkJAaBy5cq6TiwnTpzgzTff1NVKs+r9999nxowZrFmzhvPnzzN8+HCDAdJt2rTh999/JzAwkFOnTtG/f3+sra11+ytVqkRSUhLz58/nypUr/P777yxcuNCocqT+Tk6ePMmFCxe4d++eXu0wIx999BHBwcGMHDmS48ePc/HiRdauXavrULJhwwa+++47jh8/zn///cfSpUtJSUnhhRdeMLp8wvJIuIkCqUOHDmzYsIFt27bRqFEjXnzxRebMmUO5cuUAtdls7dq1uLm50aJFC9q1a0fFihVZsWJFptfVaDRs2rSJFi1aMHDgQKpUqcIbb7zBf//9R8mSJQHo0aMHHTt2pHXr1ri7u/PXX38ZXMfW1pZt27ZRokQJXn75ZWrVqsWMGTN0wfLtt9/i5uaGr68vnTt3pkOHDno1rqz48MMP6du3L/3798fHxwdnZ2ddTTDVxIkTadmyJa+++iqvvPIK3bp103t2VqdOHb799lu+/vpratasyZ9//sn06dONKgfAkCFDeOGFF2jYsCHu7u4EBQU985zatWuzZ88e/v33X5o3b069evWYPHkypUuXBtRmYH9/f9q0aUO1atVYuHAhf/31FzVq1DC6fMLyaJTnbfgWQuQbAwYMIDIyMkenARPCHKTmJoQQIt+RcBNCCJHvSLOkEEKIfEdqbkIIIfIdCTchhBD5joSbEEKIfEfCTQghRL4j4SaEECLfkXATQgiR70i4CSGEyHck3IQQQuQ7/wcR3laX4iNmsAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pg.qqplot(df['logHeartRate']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb1c9031-3e44-4598-926b-39634faf996f",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Perform an omnibus normality test (`normaltest`) on the `logHeartRate` variable for the different levels of variable `Sex`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5ea7bf3-b792-4a0b-8e18-f94c4c97ab94",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9e3b3b74-4cac-4a5a-b071-9b8e279d5d3d",
+   "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>W</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>normal</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>4.903007</td>\n",
+       "      <td>0.086164</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>2.431140</td>\n",
+       "      <td>0.296541</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               W      pval  normal\n",
+       "Sex                               \n",
+       "Female  4.903007  0.086164    True\n",
+       "Male    2.431140  0.296541    True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pg.normality(df, dv='logHeartRate', group='Sex', method='normaltest')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dada4aa-3ead-4fc3-8792-3805d1100afb",
+   "metadata": {},
+   "source": [
+    "### Bonus\n",
+    "\n",
+    "Normality test with SciPy (more convenient if your data are a NumPy array):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ed0042b6",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NormaltestResult(statistic=4.903007118532398, pvalue=0.0861639364704519)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sex = df.groupby('Sex').groups\n",
+    "logHeartRate_female = df.loc[sex['Female'], 'logHeartRate']\n",
+    "stats.normaltest(logHeartRate_female)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d61f454a",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Perform a Welch *t*-test on `logHeartRate` between males and females."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b076e8e6",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "fd70b092-f188-430e-8587-964607e88221",
+   "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>T</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>CI95%</th>\n",
+       "      <th>cohen-d</th>\n",
+       "      <th>BF10</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T-test</th>\n",
+       "      <td>6.361645</td>\n",
+       "      <td>774.706846</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>3.411624e-10</td>\n",
+       "      <td>[0.05, 0.09]</td>\n",
+       "      <td>0.447305</td>\n",
+       "      <td>2.188e+07</td>\n",
+       "      <td>0.999995</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               T         dof alternative         p-val         CI95%  \\\n",
+       "T-test  6.361645  774.706846   two-sided  3.411624e-10  [0.05, 0.09]   \n",
+       "\n",
+       "         cohen-d       BF10     power  \n",
+       "T-test  0.447305  2.188e+07  0.999995  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# define your significance level first!\n",
+    "significance_level = 0.05\n",
+    "\n",
+    "# define the two groups whose means are to be compared\n",
+    "logHeartRate_female = df.loc[sex['Female'], 'logHeartRate']\n",
+    "logHeartRate_male = df.loc[sex['Male'], 'logHeartRate']\n",
+    "\n",
+    "# test whether the group means equal or differ\n",
+    "pg.ttest(logHeartRate_female, logHeartRate_male, confidence=1-significance_level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da439d15-3b30-45d2-ac4c-d109d4fb7f9b",
+   "metadata": {},
+   "source": [
+    "Note: T>0 implies the first group's mean is greater than the second group's mean.\n",
+    "\n",
+    "With SciPy, usage is very similar, although per default group variances are assumed equal. Welch *t*-test can be selected with `equal_var=False`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a03c0966-4d77-427c-8371-0edae12fa097",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=6.361644660156658, pvalue=3.41162382079428e-10, df=774.7068455961578)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_result = stats.ttest_ind(logHeartRate_female, logHeartRate_male, equal_var=False)\n",
+    "test_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "472c03d6",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_result.pvalue <= significance_level"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbe5f273-8b48-464a-8fcf-6271fdf728f5",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "Instead of taking the log of `HeartRate` and perform a parametric *t*-test, we could have performed a non-parametric Mann-Whitney *U* Test, for example.\n",
+    "\n",
+    "Check we also find a difference between males and females' means with this latter test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d2cb07c-50b6-43e5-aee3-ba065052fd66",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4721b937-35e1-4fd3-bf9e-fe5401874ef1",
+   "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>U-val</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>RBC</th>\n",
+       "      <th>CLES</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>MWU</th>\n",
+       "      <td>104106.5</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>5.006134e-10</td>\n",
+       "      <td>-0.251408</td>\n",
+       "      <td>0.625704</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        U-val alternative         p-val       RBC      CLES\n",
+       "MWU  104106.5   two-sided  5.006134e-10 -0.251408  0.625704"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HeartRate_female = df.loc[sex['Female'], 'HeartRate']\n",
+    "HeartRate_male = df.loc[sex['Male'], 'HeartRate']\n",
+    "pg.mwu(HeartRate_female, HeartRate_male)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7d98432",
+   "metadata": {},
+   "source": [
+    "# Comparing two distributions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f5453a9",
+   "metadata": {},
+   "source": [
+    "Now let proceed to comparing age between people living with kids and those living without kids."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0aeaeee7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='LivesWithKids', y='Age', data=df)\n",
+    "sns.swarmplot(x='LivesWithKids', y='Age', hue='LivesWithKids', data=df, linewidth=1, size=3, legend=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02cbfb3c",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Looking at the distributions, it does not make sense to compare the group means. Let us perform a two-sample goodness-of-fit test instead.\n",
+    "\n",
+    "Bin the two groups from 20 to 70 years (included) with 5-year-wide bins (hint: use Pandas' `cut` function) and proceed to performing a $\\chi^2$ test of homogeneity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4338fe92",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "db11c1ed-8ba0-4c66-bcf3-48c88ded986e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lives_with_kids = df.groupby('LivesWithKids').groups\n",
+    "bins = np.arange(20, 70+1, 5) # note the increment for value 70 to be included; whatever value between 0 (excluded) and 5 is alright"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b810fdd-21e9-4346-9422-0829753d0786",
+   "metadata": {},
+   "source": [
+    "Using Pandas' `cut`, we create a new column `AgeBin` in the dataframe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "26044940-50cd-4561-b96a-a314165acb70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1      20-25\n",
+       "2      25-30\n",
+       "3      20-25\n",
+       "4      20-25\n",
+       "5      25-30\n",
+       "       ...  \n",
+       "812    20-25\n",
+       "813    35-40\n",
+       "814    30-35\n",
+       "815    35-40\n",
+       "816    55-60\n",
+       "Name: BinnedAge, Length: 816, dtype: category\n",
+       "Categories (10, object): ['20-25' < '25-30' < '30-35' < '35-40' ... '50-55' < '55-60' < '60-65' < '65-70']"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels= [f\"{lower_bound}-{lower_bound + 5}\" for lower_bound in bins[:-1]]\n",
+    "df['BinnedAge'] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)\n",
+    "df['BinnedAge']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d32ab67-de07-4b73-964e-2a9b8bd27358",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "We perform the test (note the warning):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "449991a4-4b3d-49e3-ae2a-698189a135c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/flaurent/Boxes/jammy-1/Projects/scientific_python/lib/python3.10/site-packages/pingouin/contingency.py:150: UserWarning: Low count on observed frequencies.\n",
+      "  warnings.warn(\"Low count on {} frequencies.\".format(name))\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>test</th>\n",
+       "      <th>lambda</th>\n",
+       "      <th>chi2</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>cramer</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearson</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>228.006119</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>4.325974e-44</td>\n",
+       "      <td>0.528601</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cressie-read</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>233.852507</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.539239e-45</td>\n",
+       "      <td>0.535335</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>log-likelihood</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>257.380521</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.755341e-50</td>\n",
+       "      <td>0.561620</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>freeman-tukey</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>292.581567</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.767389e-58</td>\n",
+       "      <td>0.598795</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>mod-log-likelihood</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>358.659177</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8.887881e-72</td>\n",
+       "      <td>0.662973</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>neyman</td>\n",
+       "      <td>-2.000000</td>\n",
+       "      <td>795.042049</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.481759e-165</td>\n",
+       "      <td>0.987075</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 test    lambda        chi2  dof           pval    cramer  \\\n",
+       "0             pearson  1.000000  228.006119  9.0   4.325974e-44  0.528601   \n",
+       "1        cressie-read  0.666667  233.852507  9.0   2.539239e-45  0.535335   \n",
+       "2      log-likelihood  0.000000  257.380521  9.0   2.755341e-50  0.561620   \n",
+       "3       freeman-tukey -0.500000  292.581567  9.0   9.767389e-58  0.598795   \n",
+       "4  mod-log-likelihood -1.000000  358.659177  9.0   8.887881e-72  0.662973   \n",
+       "5              neyman -2.000000  795.042049  9.0  2.481759e-165  0.987075   \n",
+       "\n",
+       "   power  \n",
+       "0    1.0  \n",
+       "1    1.0  \n",
+       "2    1.0  \n",
+       "3    1.0  \n",
+       "4    1.0  \n",
+       "5    1.0  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_, frequencies, results = pg.chi2_independence(df, x='BinnedAge', y='LivesWithKids')\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2bab2b8-11ab-48f2-839f-c8cae9dff0f0",
+   "metadata": {},
+   "source": [
+    "We have too few parents younger than 25:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7b49344f-2708-4f15-a25e-d0f60bc97b1c",
+   "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>LivesWithKids</th>\n",
+       "      <th>No</th>\n",
+       "      <th>Yes</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BinnedAge</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20-25</th>\n",
+       "      <td>75</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25-30</th>\n",
+       "      <td>32</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30-35</th>\n",
+       "      <td>26</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35-40</th>\n",
+       "      <td>23</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40-45</th>\n",
+       "      <td>22</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45-50</th>\n",
+       "      <td>30</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50-55</th>\n",
+       "      <td>39</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55-60</th>\n",
+       "      <td>56</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60-65</th>\n",
+       "      <td>94</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65-70</th>\n",
+       "      <td>61</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "LivesWithKids  No  Yes\n",
+       "BinnedAge             \n",
+       "20-25          75    2\n",
+       "25-30          32   12\n",
+       "30-35          26   44\n",
+       "35-40          23   67\n",
+       "40-45          22   65\n",
+       "45-50          30   53\n",
+       "50-55          39   57\n",
+       "55-60          56   34\n",
+       "60-65          94   16\n",
+       "65-70          61    8"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frequencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82a1dafd-f1db-4ad6-8491-1a2056e7a809",
+   "metadata": {},
+   "source": [
+    "Before we move on, to achieve a similar result, we can use SciPy's `chi2_contingency`. We can reuse the above `frequencies` dataframe, or compute it using NumPy's `histogram` instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f57a8ff6",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2, 12, 44, 67, 65, 53, 57, 34, 16,  8],\n",
+       "       [75, 32, 26, 23, 22, 30, 39, 56, 94, 61]])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parents_age = df.loc[lives_with_kids['Yes'], 'Age']\n",
+    "others_age = df.loc[lives_with_kids['No'], 'Age']\n",
+    "parents_age_freqs, _ = np.histogram(parents_age, bins)\n",
+    "others_age_freqs, _ = np.histogram(others_age, bins)\n",
+    "freqs = np.stack((parents_age_freqs, others_age_freqs))\n",
+    "freqs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b021e03f",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "χ²(9) = 228.0, p-value = 4.33e-44\n"
+     ]
+    }
+   ],
+   "source": [
+    "chi2, pvalue, dof, _ = stats.chi2_contingency(freqs)\n",
+    "print(f'χ²({dof}) = {chi2:.1f}, p-value = {pvalue:.3g}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "218a59ec",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "Repeat the procedure with 10-year bins."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abdce59a",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "3e64b5ce",
+   "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>LivesWithKids</th>\n",
+       "      <th>No</th>\n",
+       "      <th>Yes</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BinnedAge</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20-30</th>\n",
+       "      <td>107</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30-40</th>\n",
+       "      <td>49</td>\n",
+       "      <td>111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40-50</th>\n",
+       "      <td>52</td>\n",
+       "      <td>118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50-60</th>\n",
+       "      <td>95</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60-70</th>\n",
+       "      <td>155</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "LivesWithKids   No  Yes\n",
+       "BinnedAge              \n",
+       "20-30          107   14\n",
+       "30-40           49  111\n",
+       "40-50           52  118\n",
+       "50-60           95   91\n",
+       "60-70          155   24"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bins = np.arange(20, 70+1, 10)\n",
+    "labels= [f\"{lower_bound}-{lower_bound + 10}\" for lower_bound in bins[:-1]]\n",
+    "df['BinnedAge'] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)\n",
+    "_, frequencies, results = pg.chi2_independence(df, x='BinnedAge', y='LivesWithKids')\n",
+    "frequencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "e79b98f2",
+   "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>test</th>\n",
+       "      <th>lambda</th>\n",
+       "      <th>chi2</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>cramer</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearson</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>207.953598</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>7.320543e-44</td>\n",
+       "      <td>0.504822</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cressie-read</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>212.097886</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.399650e-45</td>\n",
+       "      <td>0.509827</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>log-likelihood</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>226.885551</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.180405e-48</td>\n",
+       "      <td>0.527301</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>freeman-tukey</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>245.353457</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.523214e-52</td>\n",
+       "      <td>0.548341</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>mod-log-likelihood</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>272.619752</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>8.691099e-58</td>\n",
+       "      <td>0.578008</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>neyman</td>\n",
+       "      <td>-2.000000</td>\n",
+       "      <td>369.592455</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.030517e-78</td>\n",
+       "      <td>0.673002</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 test    lambda        chi2  dof          pval    cramer  \\\n",
+       "0             pearson  1.000000  207.953598  4.0  7.320543e-44  0.504822   \n",
+       "1        cressie-read  0.666667  212.097886  4.0  9.399650e-45  0.509827   \n",
+       "2      log-likelihood  0.000000  226.885551  4.0  6.180405e-48  0.527301   \n",
+       "3       freeman-tukey -0.500000  245.353457  4.0  6.523214e-52  0.548341   \n",
+       "4  mod-log-likelihood -1.000000  272.619752  4.0  8.691099e-58  0.578008   \n",
+       "5              neyman -2.000000  369.592455  4.0  1.030517e-78  0.673002   \n",
+       "\n",
+       "   power  \n",
+       "0    1.0  \n",
+       "1    1.0  \n",
+       "2    1.0  \n",
+       "3    1.0  \n",
+       "4    1.0  \n",
+       "5    1.0  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "949edcfb",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "### Bonus\n",
+    "\n",
+    "To avoid binning, we can also perform a two-sample Kolmogorov-Smirnov test, available in SciPy only:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "7ea01f76",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KstestResult(statistic=0.31230026103290964, pvalue=7.791487838171347e-18, statistic_location=55.58, statistic_sign=1)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.ks_2samp(parents_age, others_age)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "404b5a1d-4d24-497b-8a59-9e7b37d5f8fe",
+   "metadata": {},
+   "source": [
+    "# Multiway ANOVA\n",
+    "\n",
+    "## Q\n",
+    "\n",
+    "Explain variations in heart rate using age and sex as factors (beware: there is a trap!)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cd25a18-5f88-4b2b-9c42-a50eb9a28620",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "c1ae931b-5da2-4e9b-8019-8a1599e95970",
+   "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>SS</th>\n",
+       "      <th>DF</th>\n",
+       "      <th>MS</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>BinnedAge</td>\n",
+       "      <td>745.574390</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>186.393598</td>\n",
+       "      <td>2.332798</td>\n",
+       "      <td>5.426628e-02</td>\n",
+       "      <td>0.011445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Sex</td>\n",
+       "      <td>2684.914945</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2684.914945</td>\n",
+       "      <td>33.602890</td>\n",
+       "      <td>9.697564e-09</td>\n",
+       "      <td>0.040022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BinnedAge * Sex</td>\n",
+       "      <td>1089.804405</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>272.451101</td>\n",
+       "      <td>3.409845</td>\n",
+       "      <td>8.909789e-03</td>\n",
+       "      <td>0.016641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Residual</td>\n",
+       "      <td>64400.456945</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>79.901311</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Source            SS     DF           MS          F         p-unc  \\\n",
+       "0        BinnedAge    745.574390    4.0   186.393598   2.332798  5.426628e-02   \n",
+       "1              Sex   2684.914945    1.0  2684.914945  33.602890  9.697564e-09   \n",
+       "2  BinnedAge * Sex   1089.804405    4.0   272.451101   3.409845  8.909789e-03   \n",
+       "3         Residual  64400.456945  806.0    79.901311        NaN           NaN   \n",
+       "\n",
+       "        np2  \n",
+       "0  0.011445  \n",
+       "1  0.040022  \n",
+       "2  0.016641  \n",
+       "3       NaN  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pg.anova(data=df, dv='HeartRate', between=['BinnedAge', 'Sex'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6dd75bf-aa69-4230-b65a-c0a347722ca6",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "We find a significant interaction while the effect of age fails to come up as significant by a short margin. Let us first draw an interaction plot."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb03922e-0914-429e-850a-b9247de0d642",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7d4dee15-1178-4793-a970-82bc9ed49d74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pg.plot_paired(df, 'HeartRate', 'Sex', 'BinnedAge');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "52c337c2-d5a0-4284-91d7-a11af70e5a9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pg.plot_paired(df, 'HeartRate', 'BinnedAge', 'Sex');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90404029-628c-49c9-a480-94609dcf75b2",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "For the purpose of performing multiple comparisons and some *p*-value correction, let us conduct separate $t$-tests for each age interval and organise the results into a dataframe.\n",
+    "\n",
+    "If comfortable enough with Python, define a \"Pingouin-like\" function `stratified_ttests` that takes a dataframe `data`, a dependent variable name `dv`, a between factor name `between` and a stratum factor `strata`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c4f62b3-a716-4bb7-a14e-8a758abea7fd",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "556d5072-a813-4551-9cb5-e5cbba528bed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def stratified_ttests(data, dv, between, strata):\n",
+    "    # convert `strata` into a dictionary with the levels as keys and the corresponding rows as values\n",
+    "    strata = data.groupby(strata).groups\n",
+    "    # list the levels of the between factor, so that we input the groups in `ttest` in a consistent order\n",
+    "    levels = data[between].unique()\n",
+    "    # the present function only supports binary between factors, because we call `ttest`\n",
+    "    assert len(levels) == 2\n",
+    "    level1, level2 = levels\n",
+    "    # loop over the different strata\n",
+    "    results = []\n",
+    "    for stratum, rows in strata.items():\n",
+    "        # pick the corresponding rows of data\n",
+    "        stratum_data = data.loc[rows]\n",
+    "        # make the two groups\n",
+    "        group1 = stratum_data.loc[stratum_data[between]==level1, dv]\n",
+    "        group2 = stratum_data.loc[stratum_data[between]==level2, dv]\n",
+    "        # perform the test\n",
+    "        result = pg.ttest(group1, group2)\n",
+    "        # `result` is a single-row dataframe; set the index label so that we can concatenate the rows afterwards\n",
+    "        result.index = [stratum]\n",
+    "        results.append(result)\n",
+    "    # concatenate the rows and return the resulting dataframe\n",
+    "    return pd.concat(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "45de8b53-30cc-48ab-adc6-945970d356af",
+   "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>T</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>CI95%</th>\n",
+       "      <th>cohen-d</th>\n",
+       "      <th>BF10</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20-30</th>\n",
+       "      <td>4.330759</td>\n",
+       "      <td>113.025032</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.000032</td>\n",
+       "      <td>[3.67, 9.86]</td>\n",
+       "      <td>0.794361</td>\n",
+       "      <td>616.37</td>\n",
+       "      <td>0.990899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30-40</th>\n",
+       "      <td>2.949648</td>\n",
+       "      <td>145.875465</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.003707</td>\n",
+       "      <td>[1.48, 7.51]</td>\n",
+       "      <td>0.472859</td>\n",
+       "      <td>8.857</td>\n",
+       "      <td>0.841595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40-50</th>\n",
+       "      <td>2.423362</td>\n",
+       "      <td>165.504774</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.016455</td>\n",
+       "      <td>[0.63, 6.19]</td>\n",
+       "      <td>0.372634</td>\n",
+       "      <td>2.454</td>\n",
+       "      <td>0.674634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50-60</th>\n",
+       "      <td>-0.156658</td>\n",
+       "      <td>157.910756</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.875714</td>\n",
+       "      <td>[-2.77, 2.36]</td>\n",
+       "      <td>0.022758</td>\n",
+       "      <td>0.161</td>\n",
+       "      <td>0.052733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60-70</th>\n",
+       "      <td>3.916719</td>\n",
+       "      <td>175.516065</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.000128</td>\n",
+       "      <td>[2.46, 7.46]</td>\n",
+       "      <td>0.585964</td>\n",
+       "      <td>165.561</td>\n",
+       "      <td>0.973418</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              T         dof alternative     p-val          CI95%   cohen-d  \\\n",
+       "20-30  4.330759  113.025032   two-sided  0.000032   [3.67, 9.86]  0.794361   \n",
+       "30-40  2.949648  145.875465   two-sided  0.003707   [1.48, 7.51]  0.472859   \n",
+       "40-50  2.423362  165.504774   two-sided  0.016455   [0.63, 6.19]  0.372634   \n",
+       "50-60 -0.156658  157.910756   two-sided  0.875714  [-2.77, 2.36]  0.022758   \n",
+       "60-70  3.916719  175.516065   two-sided  0.000128   [2.46, 7.46]  0.585964   \n",
+       "\n",
+       "          BF10     power  \n",
+       "20-30   616.37  0.990899  \n",
+       "30-40    8.857  0.841595  \n",
+       "40-50    2.454  0.674634  \n",
+       "50-60    0.161  0.052733  \n",
+       "60-70  165.561  0.973418  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = stratified_ttests(df, 'HeartRate', 'Sex', 'BinnedAge')\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a96fa2f-d966-4caf-b02c-70721b57d124",
+   "metadata": {},
+   "source": [
+    "Simpler but not-reusable implementation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9b060b84-c930-4f46-a39d-c1e4237358f5",
+   "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>T</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>CI95%</th>\n",
+       "      <th>cohen-d</th>\n",
+       "      <th>BF10</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20-30</th>\n",
+       "      <td>4.330759</td>\n",
+       "      <td>113.025032</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.000032</td>\n",
+       "      <td>[3.67, 9.86]</td>\n",
+       "      <td>0.794361</td>\n",
+       "      <td>616.37</td>\n",
+       "      <td>0.990899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30-40</th>\n",
+       "      <td>2.949648</td>\n",
+       "      <td>145.875465</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.003707</td>\n",
+       "      <td>[1.48, 7.51]</td>\n",
+       "      <td>0.472859</td>\n",
+       "      <td>8.857</td>\n",
+       "      <td>0.841595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40-50</th>\n",
+       "      <td>2.423362</td>\n",
+       "      <td>165.504774</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.016455</td>\n",
+       "      <td>[0.63, 6.19]</td>\n",
+       "      <td>0.372634</td>\n",
+       "      <td>2.454</td>\n",
+       "      <td>0.674634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50-60</th>\n",
+       "      <td>-0.156658</td>\n",
+       "      <td>157.910756</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.875714</td>\n",
+       "      <td>[-2.77, 2.36]</td>\n",
+       "      <td>0.022758</td>\n",
+       "      <td>0.161</td>\n",
+       "      <td>0.052733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60-70</th>\n",
+       "      <td>3.916719</td>\n",
+       "      <td>175.516065</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.000128</td>\n",
+       "      <td>[2.46, 7.46]</td>\n",
+       "      <td>0.585964</td>\n",
+       "      <td>165.561</td>\n",
+       "      <td>0.973418</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              T         dof alternative     p-val          CI95%   cohen-d  \\\n",
+       "20-30  4.330759  113.025032   two-sided  0.000032   [3.67, 9.86]  0.794361   \n",
+       "30-40  2.949648  145.875465   two-sided  0.003707   [1.48, 7.51]  0.472859   \n",
+       "40-50  2.423362  165.504774   two-sided  0.016455   [0.63, 6.19]  0.372634   \n",
+       "50-60 -0.156658  157.910756   two-sided  0.875714  [-2.77, 2.36]  0.022758   \n",
+       "60-70  3.916719  175.516065   two-sided  0.000128   [2.46, 7.46]  0.585964   \n",
+       "\n",
+       "          BF10     power  \n",
+       "20-30   616.37  0.990899  \n",
+       "30-40    8.857  0.841595  \n",
+       "40-50    2.454  0.674634  \n",
+       "50-60    0.161  0.052733  \n",
+       "60-70  165.561  0.973418  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = []\n",
+    "for stratum in ('20-30', '30-40', '40-50', '50-60', '60-70'):\n",
+    "    stratum_data = df[df['BinnedAge']==stratum]\n",
+    "    group1 = stratum_data.loc[stratum_data['Sex']=='Female', 'HeartRate']\n",
+    "    group2 = stratum_data.loc[stratum_data['Sex']=='Male', 'HeartRate']\n",
+    "    result = pg.ttest(group1, group2)\n",
+    "    result.index = [stratum]\n",
+    "    results.append(result)\n",
+    "results = pd.concat(results)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c9c6f6-b875-4796-b3cc-3199b6759d86",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "Correct the *p*-values, for example using the Holm procedure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6feae507-f3d4-4946-9579-edb1eeac6682",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "e5411a4f-31a5-452c-b644-286ef34412da",
+   "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>T</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>CI95%</th>\n",
+       "      <th>cohen-d</th>\n",
+       "      <th>BF10</th>\n",
+       "      <th>power</th>\n",
+       "      <th>corrected p-val</th>\n",
+       "      <th>significance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20-30</th>\n",
+       "      <td>4.330759</td>\n",
+       "      <td>113.025032</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.000032</td>\n",
+       "      <td>[3.67, 9.86]</td>\n",
+       "      <td>0.794361</td>\n",
+       "      <td>616.37</td>\n",
+       "      <td>0.990899</td>\n",
+       "      <td>0.000162</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30-40</th>\n",
+       "      <td>2.949648</td>\n",
+       "      <td>145.875465</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.003707</td>\n",
+       "      <td>[1.48, 7.51]</td>\n",
+       "      <td>0.472859</td>\n",
+       "      <td>8.857</td>\n",
+       "      <td>0.841595</td>\n",
+       "      <td>0.011121</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40-50</th>\n",
+       "      <td>2.423362</td>\n",
+       "      <td>165.504774</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.016455</td>\n",
+       "      <td>[0.63, 6.19]</td>\n",
+       "      <td>0.372634</td>\n",
+       "      <td>2.454</td>\n",
+       "      <td>0.674634</td>\n",
+       "      <td>0.032911</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50-60</th>\n",
+       "      <td>-0.156658</td>\n",
+       "      <td>157.910756</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.875714</td>\n",
+       "      <td>[-2.77, 2.36]</td>\n",
+       "      <td>0.022758</td>\n",
+       "      <td>0.161</td>\n",
+       "      <td>0.052733</td>\n",
+       "      <td>0.875714</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60-70</th>\n",
+       "      <td>3.916719</td>\n",
+       "      <td>175.516065</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.000128</td>\n",
+       "      <td>[2.46, 7.46]</td>\n",
+       "      <td>0.585964</td>\n",
+       "      <td>165.561</td>\n",
+       "      <td>0.973418</td>\n",
+       "      <td>0.000514</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              T         dof alternative     p-val          CI95%   cohen-d  \\\n",
+       "20-30  4.330759  113.025032   two-sided  0.000032   [3.67, 9.86]  0.794361   \n",
+       "30-40  2.949648  145.875465   two-sided  0.003707   [1.48, 7.51]  0.472859   \n",
+       "40-50  2.423362  165.504774   two-sided  0.016455   [0.63, 6.19]  0.372634   \n",
+       "50-60 -0.156658  157.910756   two-sided  0.875714  [-2.77, 2.36]  0.022758   \n",
+       "60-70  3.916719  175.516065   two-sided  0.000128   [2.46, 7.46]  0.585964   \n",
+       "\n",
+       "          BF10     power  corrected p-val  significance  \n",
+       "20-30   616.37  0.990899         0.000162          True  \n",
+       "30-40    8.857  0.841595         0.011121          True  \n",
+       "40-50    2.454  0.674634         0.032911          True  \n",
+       "50-60    0.161  0.052733         0.875714         False  \n",
+       "60-70  165.561  0.973418         0.000514          True  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "significance, corrected_pvalues = pg.multicomp(results['p-val'], method='holm')\n",
+    "results['corrected p-val'], results['significance'] = corrected_pvalues, significance\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c91bde97-d6ea-4eb6-953b-391df9b3576e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": false,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/pingouin_scipy_cours.ipynb b/notebooks/pingouin_scipy_cours.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..85a9a2002a67d739e5d1374edc80e33a9a131bae
--- /dev/null
+++ b/notebooks/pingouin_scipy_cours.ipynb
@@ -0,0 +1,3505 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ccafd3d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "!\"{sys.executable}\" -m pip install scipy ipywidgets\n",
+    "import scipy_material"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d2c6ebd",
+   "metadata": {},
+   "source": [
+    "<h1 align='center'>Statistical tests with the Pingouin and SciPy libraries</h1>\n",
+    "\n",
+    "<div style='text-align:center'><img width=600 src='https://github.com/raphaelvallat/pingouin/raw/master/docs/pictures/logo_pingouin.png' /></div>\n",
+    "<div style='text-align:center'><img width=300 src='https://docs.scipy.org/doc/scipy/_static/logo.svg' /></div>\n",
+    "\n",
+    "The [Pingouin](https://pingouin-stats.org/build/html/index.html) library features a selection of commonly-used statistical operations. The provided functions have verbose output and can be used independently of one another."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1f8a7a30-48e3-4f5a-8836-3912b049b5bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pingouin as pg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b46252c6-5c09-40e2-890d-9abeccd4e7be",
+   "metadata": {},
+   "source": [
+    "In contrast, [SciPy](https://docs.scipy.org/doc/scipy/reference/#api-definition) is a collection of mathematical tools aiming at diverse fields. It is the one of the oldest Python libraries. Its functionalities are split in several modules:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "42342d74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import (\n",
+    "    cluster,     # Clustering algorithms\n",
+    "    constants,   # Physical and mathematical constants\n",
+    "    fftpack,     # Fast Fourier Transform routines\n",
+    "    integrate,   # Integration and ordinary differential equation solvers\n",
+    "    interpolate, # Interpolation and smoothing splines\n",
+    "    io,          # Input and Output\n",
+    "    linalg,      # Linear algebra\n",
+    "    ndimage,     # N-dimensional image processing\n",
+    "    odr,         # Orthogonal distance regression\n",
+    "    optimize,    # Optimization and root-finding routines\n",
+    "    signal,      # Signal processing\n",
+    "    sparse,      # Sparse matrices and associated routines\n",
+    "    spatial,     # Spatial data structures and algorithms\n",
+    "    special,     # Special functions\n",
+    "    stats,       # Statistical distributions and functions\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a37e843-892d-4bfd-93a2-46e94c2a45e8",
+   "metadata": {},
+   "source": [
+    "We will make a brief overview of the `scipy.stats` module only, in particular basic functionalities that cannot be found in Pingouin."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d715671-e4ad-4d4d-b60f-9397e2b80b90",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Outline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5162d7b2-625a-4699-922e-92c5c2bfa769",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Descriptive statistics are well covered by Pandas and the plotting libraries.\n",
+    "This course focuses merely on statistical tests.\n",
+    "\n",
+    "* Distributions\n",
+    "* Student $t$ tests\n",
+    "    * compare a sample mean against the population mean\n",
+    "    * compare means of two independent samples\n",
+    "    * compare the means of paired samples\n",
+    "* Analyses of variance\n",
+    "    * compare more than two group means\n",
+    "* Tests for other tests' assumptions\n",
+    "    * normality tests\n",
+    "    * homoscedasticity tests\n",
+    "* $\\chi^2$ tests for categorical variables\n",
+    "    * homogeneity and independence tests\n",
+    "    * goodness-of-fit tests\n",
+    "* Effect sizes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d15967d-ecf9-469a-a740-c7fe9cbf04d2",
+   "metadata": {},
+   "source": [
+    "# Distributions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "164b1fe2-412d-48f8-a6f0-d2352a9d575a",
+   "metadata": {},
+   "source": [
+    "For this section, the related utilities are provided by `scipy.stats`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "78db8576-5c83-4967-97b8-19dd4ff9b3b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c99b2f0-8832-40cb-a63e-d4abdef4f257",
+   "metadata": {},
+   "source": [
+    "Reminder about module loading:\n",
+    "\n",
+    "Example: how to access the `sem` function defined in the `scipy.stats` module?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "898957c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "skipping\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%script echo skipping\n",
+    "\n",
+    "import scipy.stats\n",
+    "scipy.stats.sem\n",
+    "\n",
+    "from scipy import stats\n",
+    "stats.sem\n",
+    "\n",
+    "from scipy.stats import *\n",
+    "sem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4dc592bf-9900-4838-ab03-98039c17cfd8",
+   "metadata": {},
+   "source": [
+    "## Confidence intervals\n",
+    "\n",
+    "Common information such as the sample mean or standard deviation are trivial to obtain. For example, we have seen Pandas' `describe`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0a56b871-8335-42b1-b8e0-f1cdc68b5281",
+   "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>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>244.000000</td>\n",
+       "      <td>244.000000</td>\n",
+       "      <td>244.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>19.785943</td>\n",
+       "      <td>2.998279</td>\n",
+       "      <td>2.569672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8.902412</td>\n",
+       "      <td>1.383638</td>\n",
+       "      <td>0.951100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3.070000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>13.347500</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>17.795000</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>24.127500</td>\n",
+       "      <td>3.562500</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>50.810000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       total_bill         tip        size\n",
+       "count  244.000000  244.000000  244.000000\n",
+       "mean    19.785943    2.998279    2.569672\n",
+       "std      8.902412    1.383638    0.951100\n",
+       "min      3.070000    1.000000    1.000000\n",
+       "25%     13.347500    2.000000    2.000000\n",
+       "50%     17.795000    2.900000    2.000000\n",
+       "75%     24.127500    3.562500    3.000000\n",
+       "max     50.810000   10.000000    6.000000"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataframe = pg.read_dataset('tips')\n",
+    "dataframe.describe()\n",
+    "#dataframe.describe(exclude=np.number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b311609",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "To report the value of the population mean and account for the uncertainty that results from the fact the true value is actually unknown (the sample mean above is our best guess), we can give a confidence interval instead.\n",
+    "\n",
+    "Reminder: the population mean follows a normal distribution centered at the sample mean, with standard deviation equal to the standard error of the mean (or, equivalently, the standard deviation of the sample divided by the square root of the sample size)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e60ba8f3",
+   "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": [
+    "scipy_material.illustration_confidence_interval(19.785943, stats.sem(dataframe['total_bill']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2b36e29",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "Computing a confidence interval with SciPy involves instantiating the normal distribution with the `norm` function and calling the `interval` method of the returned object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e911524f",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "X = dataframe['total_bill']\n",
+    "mu = np.mean(X)\n",
+    "sigma = stats.sem(X)\n",
+    "distribution_of_the_mean = stats.norm(mu, sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2494dc66-d5bf-4235-b2f7-02daf5da7d2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(18.668922839262997, 20.902962406638643)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "distribution_of_the_mean.interval(0.95)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db72ca26-00df-45be-9700-2f9af50228d0",
+   "metadata": {},
+   "source": [
+    "Note again that we have set the scale parameter `sigma` equal to the sem. In contrast, if variable `total_bill` followed a normal distribution, we could define its distribution as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1fad2898-81e4-4814-b496-146f54d21e29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normal_distribution = stats.norm(X.mean(), X.std())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d77e3ca-f4af-4135-bfee-92054898eaa6",
+   "metadata": {},
+   "source": [
+    "The objects `norm` returns (*e.g.* `distribution_of_the_mean`) feature numerous other methods:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1341b01d",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2704798697499871"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# probability density function\n",
+    "distribution_of_the_mean.pdf(19.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d6bbb149",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0839406210836206"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# cumulative distribution function\n",
+    "distribution_of_the_mean.cdf(19.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b05f3b3",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "See [scipy.stats.rv_continuous](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.html#scipy.stats.rv_continuous) for more methods.\n",
+    "\n",
+    "As another example, we can make use of the inverse survival function `isf` to re-implement the calculation of the $1-\\alpha=95\\%$ confidence interval based on the following formula:\n",
+    "\n",
+    "$$\n",
+    "\\bar{x} \\pm z_{1-\\alpha/2}\\frac{\\sigma}{\\sqrt{n}}\n",
+    "$$\n",
+    "\n",
+    "Indeed, $z_{1-\\alpha/2}$ is calculated as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "62f75d44",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.9599639845400545"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alpha = 0.05\n",
+    "z = stats.norm().isf(alpha / 2)\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7728089e",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "For a $95\\%$ confidence interval, we usually take $z\\approx 1.96$. Note we took the standard normal distribution, with null mean and unit standard deviation (`stats.norm()` is equivalent to `stats.norm(0, 1)`).\n",
+    "\n",
+    "$\\frac{\\sigma}{\\sqrt{n}}$ is the standard deviation of the sample mean, or standard error of mean, that we have already calculated using the `sem` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "99fe274d",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bills are 19.79 ± 1.12 on average\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Bills are {mu:.2f} ± {z * sigma:.2f} on average')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91a55884-2ca8-4cb0-9190-6c42bea7047b",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "## Fitting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72a7068e-ac7c-4940-b5e1-103ecf4cec2c",
+   "metadata": {},
+   "source": [
+    "We have seen how to fit a normal distribution explicitly passing a mean and standard deviation. More generally, for any distribution from `scipy.stats`, we can get the required parameters using the `stats.<distribution>.fit` method. For example, for distribution `stats.norm` with sample `X`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "11cb5a3f-c48d-457d-9134-c56665b5e169",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normal_distribution = stats.norm(*stats.norm.fit(X))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c483874-e9db-440a-b1c8-7dd47a06008a",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "Now, unlike the population mean, there is no guarantee a sample follows a normal distribution.\n",
+    "\n",
+    "To determine what distribution a sample best follows, we can fit various distributions to the data and visually appreciate how well these distributions match with the data by plotting a scaled histogram and the probability density functions of the fitted distributions on top of the histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "578edd4a-bcb1-4f9e-a8ba-d3e63bbabb71",
+   "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": [
+    "# plot the histogram\n",
+    "import seaborn as sns\n",
+    "sns.histplot(X, bins=20, stat='density')\n",
+    "\n",
+    "# fit a log-normal distribution\n",
+    "lognorm = stats.lognorm(*stats.lognorm.fit(X))\n",
+    "\n",
+    "# draw the probability density function\n",
+    "from matplotlib import pyplot as plt\n",
+    "grid = np.arange(1, 52, .1)\n",
+    "plt.plot(grid, lognorm.pdf(grid));\n",
+    "\n",
+    "# fit and overlay more distributions\n",
+    "#weibull = stats.weibull_min(*stats.weibull_min.fit(X))\n",
+    "#plt.plot(grid, weibull.pdf(grid));\n",
+    "#t = stats.t(*stats.t.fit(X))\n",
+    "#plt.plot(grid, t.pdf(grid));\n",
+    "#chi2 = stats.chi2(*stats.chi2.fit(X))\n",
+    "#plt.plot(grid, chi2.pdf(grid));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1fc8931-8459-46cf-b085-765298aaac0a",
+   "metadata": {},
+   "source": [
+    "Note that plotting histograms is good practice anyway, because it helps to spot data distributions with multiple modes. Multiple modes in a sample are a red flag for tests that compare estimates of central tendency (*e.g.* means)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb8febd8",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "...and we can test whether `total_bill` follows a log-normal distribution in our sample with the one-sample Kolmogorov-Smirnov test:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5b1b8872",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7387575212859724"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "statistic, pvalue = stats.kstest(X, lognorm.cdf)\n",
+    "pvalue"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddeacee0-2062-476d-b7ec-9e0f036d6c4b",
+   "metadata": {
+    "heading_collapsed": true,
+    "tags": []
+   },
+   "source": [
+    "# Statistical testing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f2fcb2e-1097-4bb7-ba77-fceaa3b55702",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "\n",
+    "> What have we just done?\n",
+    "\n",
+    "We compared our **observations** `x` with some **expectation**.\n",
+    "\n",
+    "We actually formulated a so-called *null hypothesis*, denoted $H_0$, that models the situation such that \"nothing is going on\", *i.e.* the observations meet the expectation.\n",
+    "\n",
+    "We also implicitly defined an alternative hypothesis, usually denoted $H_1$ or $H_A$, that can simply be the opposite of $H_0$.\n",
+    "\n",
+    "For example:\n",
+    "\n",
+    "$$\n",
+    "\\left\\{\n",
+    "\\begin{array}{ l l l }\n",
+    "H_0: & X \\sim \\mathcal{N}(\\mu, \\sigma^2) & \\mbox{with } \\mu \\mbox{ assumed to be } \\bar{x} \\mbox{ and } \\sigma^2 \\mbox{ as } \\frac{1}{n-1}\\sum_{i=0}^{n-1} (x_i - \\bar{x})^2 \\\\\n",
+    "H_A: & \\mbox{not } H_0\n",
+    "\\end{array}\n",
+    "\\right.\n",
+    "$$\n",
+    "\n",
+    "A test consists in contrasting the two incompatible hypotheses.\n",
+    "\n",
+    "If we had a single observation – say $z=1.4$ – to compare with a distribution – say $\\mathcal{N}(0,1)$ – we would simply compute the probability for this value to be drawn from this distribution (or not):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8346ad8e",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "z = 1.4\n",
+    "\n",
+    "N = stats.norm(0, 1)\n",
+    "\n",
+    "onesided_pvalue = N.sf(z) # sf= survival function\n",
+    "twosided_pvalue = 2 * min(N.cdf(z), N.sf(z))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "404476b6",
+   "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": [
+    "scipy_material.illustration_onesided_probabilitymass(z, N, onesided_pvalue)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99a007ac",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "In practice, all tests boil down to comparing a single value with a reference distribution. Basically, a test expresses the discrepancy between the observations and the expectation in the shape of a *statistic*, and this statistic is supposed to follow a given distribution under $H_0$.\n",
+    "\n",
+    "This is used as a basis to calculate a *p*-value that estimates the probability of erroneously rejecting $H_0$.\n",
+    "\n",
+    "The experimenter also defines a significance level $\\alpha$, with common values $\\alpha=0.05$ or $0.01$, that sets the maximum tolerated risk of making a *type-1 error*, *i.e.* of rejecting $H_0$ by chance.\n",
+    "If the obtained <em>p</em>-value is lower than $\\alpha$, then s·he can conclude there is sufficient evidence to reject $H_0$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b162e92e",
+   "metadata": {
+    "hidden": true,
+    "hide_input": true,
+    "jupyter": {
+     "source_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>table#typeoferrors { text-align: center; font-size: large; margin-left: 1px;} #typeoferrors td { text-align: center; font-size: large; border-right: solid 1px black; border-bottom: solid 1px black; } #typeoferrors td.border { font-size: small; border-left: solid 1px black; border-top: solid 1px black; } #typeoferrors td.wrong { color: orange; } #typeoferrors td.ok { color: green; } #typeoferrors span.sub { font-size: x-small; } #typeoferrors td.footnote { text-align: left; font-size: xx-small; border-right: 0px; border-bottom: 0px; } </style> <table id=\"typeoferrors\">     <tr><td rowspan=\"2\" colspan=\"2\"></td><td colspan=\"2\" class=\"border\">Conclusion about $H_0$<br />from the statistical test</td></tr>    <tr><td>accept</td><td>reject</td></tr>     <tr><td rowspan=\"2\" class=\"border\">Truth about $H_0$<br />in the population</td><td>true</td><td class=\"ok\">Correct</td><td  class=\"wrong\">Type 1 error<br /><span class=\"sub\">observe difference<br />when none exists</span></td></tr>     <tr><td>false</td><td class=\"wrong\">Type 2 error<br /><span class=\"sub\">fail to observe difference<br />when one exists</span></td><td class=\"ok\">Correct</td></tr>     <tr><td colspan=\"4\" class=\"footnote\"> <a href=\"https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting\">https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting</a>     </td></tr> </table>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%html\n",
+    "<style>table#typeoferrors { text-align: center; font-size: large; margin-left: 1px;} #typeoferrors td { text-align: center; font-size: large; border-right: solid 1px black; border-bottom: solid 1px black; } #typeoferrors td.border { font-size: small; border-left: solid 1px black; border-top: solid 1px black; } #typeoferrors td.wrong { color: orange; } #typeoferrors td.ok { color: green; } #typeoferrors span.sub { font-size: x-small; } #typeoferrors td.footnote { text-align: left; font-size: xx-small; border-right: 0px; border-bottom: 0px; } </style> <table id=\"typeoferrors\">     <tr><td rowspan=\"2\" colspan=\"2\"></td><td colspan=\"2\" class=\"border\">Conclusion about $H_0$<br />from the statistical test</td></tr>    <tr><td>accept</td><td>reject</td></tr>     <tr><td rowspan=\"2\" class=\"border\">Truth about $H_0$<br />in the population</td><td>true</td><td class=\"ok\">Correct</td><td  class=\"wrong\">Type 1 error<br /><span class=\"sub\">observe difference<br />when none exists</span></td></tr>     <tr><td>false</td><td class=\"wrong\">Type 2 error<br /><span class=\"sub\">fail to observe difference<br />when one exists</span></td><td class=\"ok\">Correct</td></tr>     <tr><td colspan=\"4\" class=\"footnote\"> <a href=\"https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting\">https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting</a>     </td></tr> </table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "446ba63e-df67-46ca-8921-eca686462c93",
+   "metadata": {
+    "heading_collapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## *t* tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f9602b8-5f64-438b-8051-c71f9c8b0b14",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "*t* tests derive a statistic that is supposed to follow the [Student's *t* distribution](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html) under $H_0$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "67d046f0-ca64-4a12-8e32-58b8e7e142a0",
+   "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": [
+    "scipy_material.illustration_t_pdfs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "701ee597-2eec-4404-8a0e-601ae2121e19",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "At high degrees of freedom, the *t* distribution approaches the normal distribution. At lower degrees of freedom, the *t* distribution exhibits heavier tails and is less sensitive to extreme values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1606aca-1582-44a8-8f17-4804b416e005",
+   "metadata": {},
+   "source": [
+    "There exist several *t* tests. Pingouin's [`ttest`](https://pingouin-stats.org/build/html/generated/pingouin.ttest.html#pingouin.ttest) function provides several of them:\n",
+    "\n",
+    "`pg.ttest(x, y, paired=False, alternative='two-sided', correction='auto', r=0.707, confidence=0.95)`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca6bf548-cadf-4c75-8130-fe0c3ef8de9a",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "### One-sample *t* test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9fdde2b-8764-4f9f-b249-6af66347b3b1",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "This test compares a sample's central tendency (*sample mean*) with a reference value (*population mean*).\n",
+    "\n",
+    "<table style=\"text-align: center;\"><tr><td>\n",
+    "<img src='img/8mice.svg' />\n",
+    "</td><td>\n",
+    "<img src='img/Scientific_journal_icon.svg' width=\"96px\" />\n",
+    "</td></tr><tr><td><center>\n",
+    "<code>x=[49.5 81.9 64.0 17.3 59.8 94.6 69.9 12.4]</code>\n",
+    "</center></td><td><center>\n",
+    "<code>&mu;=50</code>\n",
+    "</center></td></tr></table>\n",
+    "\n",
+    "Let us call $\\mu$ this reference value. Our expectation is that the sample mean $\\bar{X}$ is close enough to $\\mu$.\n",
+    "In other words, $H_0: \\bar{X} = \\mu$.\n",
+    "The statistic is:\n",
+    "$$\n",
+    "\\frac{\\bar{X} - \\mu}{\\mathrm{SEM}} \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\sim t(n-1) \\mbox{ } \\textrm{under} \\mbox{ } H_0\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bb6f0e09-f529-47bb-8470-66130193a791",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array([49.47257879, 81.93967205, 64.030398, 17.25423608, 59.80082512,\n",
+    "              94.56012514, 69.91672899, 12.39640637])\n",
+    "\n",
+    "mu = 50"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ff75a9a-9f15-4be6-9312-388fb0b62a4a",
+   "metadata": {},
+   "source": [
+    "With Pingouin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ec4b0084-93c3-40b9-94c5-d05679a1dc45",
+   "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>T</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>CI95%</th>\n",
+       "      <th>cohen-d</th>\n",
+       "      <th>BF10</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T-test</th>\n",
+       "      <td>0.602406</td>\n",
+       "      <td>7</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.565899</td>\n",
+       "      <td>[31.95, 80.4]</td>\n",
+       "      <td>0.212983</td>\n",
+       "      <td>0.391</td>\n",
+       "      <td>0.08203</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               T  dof alternative     p-val          CI95%   cohen-d   BF10  \\\n",
+       "T-test  0.602406    7   two-sided  0.565899  [31.95, 80.4]  0.212983  0.391   \n",
+       "\n",
+       "          power  \n",
+       "T-test  0.08203  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pg.ttest(x, mu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e96a45be-1ab1-4278-a5a3-e49574e0133a",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "For completeness, SciPy's one-sample *t* test is [ttest_1samp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_1samp.html):\n",
+    "\n",
+    "`scipy.stats.ttest_1samp(a, popmean, axis=0, nan_policy='propagate', alternative='two-sided')`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b471633d-c9ad-455e-84e1-d32af085b32a",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=0.6024056396957578, pvalue=0.5658990587680466, df=7)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.ttest_1samp(x, mu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2144869e-9e4a-4e63-ae58-81c5a6be7205",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "### *t* test for independent samples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b892f33-761a-40b6-873d-ff9f7758cef3",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "This test compares the means of two samples or groups, *e.g.* a control sample and a sample from a mutated population: $H_0: \\bar{X_1} = \\bar{X_2}$.\n",
+    "\n",
+    "<table style=\"text-align:center;\"><tr><td>\n",
+    "<img src=\"img/8mice.svg\" alt=\"sample of the control population\" />\n",
+    "</td><td>\n",
+    "<img src=\"img/8mutants1.svg\" alt=\"sample of a mutated population\" />\n",
+    "</td></tr><tr><td><center>\n",
+    "<code>x<sub>1</sub>=[49.5 81.9 64.0 17.3 59.8 94.6 69.9 12.4]</code>\n",
+    "</center></td><td><center>\n",
+    "<code>x<sub>2</sub>=[64.2 96.6 101.9 85.3 66.5 63.9 127.6 55.0]</code>\n",
+    "</center></td></tr></table>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8cc7f8c2-75d3-447f-b27c-4ed6350e4075",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1 = x\n",
+    "x2 = np.array([64.22723692, 96.56483856, 101.94191774, 85.31918879,\n",
+    "               66.4952999, 63.88841224, 127.63861749, 55.00527005])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16c87bf3-dd77-487f-8b96-9560a1ef3315",
+   "metadata": {},
+   "source": [
+    "With Pingouin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7dae4d48-36b9-40df-87fa-a3c73ca42a19",
+   "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>T</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>CI95%</th>\n",
+       "      <th>cohen-d</th>\n",
+       "      <th>BF10</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T-test</th>\n",
+       "      <td>-1.961743</td>\n",
+       "      <td>14</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.069989</td>\n",
+       "      <td>[-55.4, 2.47]</td>\n",
+       "      <td>0.980872</td>\n",
+       "      <td>1.42</td>\n",
+       "      <td>0.447175</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               T  dof alternative     p-val          CI95%   cohen-d  BF10  \\\n",
+       "T-test -1.961743   14   two-sided  0.069989  [-55.4, 2.47]  0.980872  1.42   \n",
+       "\n",
+       "           power  \n",
+       "T-test  0.447175  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pg.ttest(x1, x2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce4cf3a8-190f-4436-9044-743e985fe748",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "SciPy's *t* test for independent samples uses the statistic $t=\\frac{\\bar{X_1}-\\bar{X_2}}{\\sqrt{(\\frac{1}{n_1}+\\frac{1}{n_2})\\mbox{ }\\textrm{PooledVariance}}}$ with $\\textrm{PooledVariance} = \\frac{1}{n_1+n_2-2}\\sum_{j\\in\\{1,2\\}}\\sum_i (x_{ij}-\\bar{x_j})^2$ and is available as [ttest_ind](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html):\n",
+    "\n",
+    "`scipy.stats.ttest_ind(a, b, axis=0, equal_var=True, nan_policy='propagate', permutations=None, random_state=None, alternative='two-sided', trim=0)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6231e214-ac36-4c4f-8a16-e1551c8484b4",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=-1.96174329619957, pvalue=0.06998888828308221, df=14.0)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.ttest_ind(x1, x2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b579c8e-1062-4083-bcc1-49009f0c65a1",
+   "metadata": {},
+   "source": [
+    "Results are consistent.\n",
+    "\n",
+    "However, if the two samples were of different sizes, Pingouin's `ttest` would automatically use Welch's *t* test instead.\n",
+    "\n",
+    "SciPy's `ttest_ind` also provides this variant of the *t* test, passing argument `equal_variance=False`. It also provides a less common variant known as Yuen's *t* test, with `equal_var=False` and `trim=0.2` (requires more data).\n",
+    "\n",
+    "Note that SciPy's default *t* test does not require equal numbers of observations per group. However, it assumes the groups are normally distributed (but is relatively robust to non-«extreme non-normality») and, more importantly, have [similar variances ($0.5<\\frac{s_{X_1}}{s_{X_2}}<2$)](https://en.wikipedia.org/wiki/Student%27s_t-test#Equal_or_unequal_sample_sizes,_similar_variances_(1/2_%3C_sX1/sX2_%3C_2))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1083c04c-1221-446f-84a0-7084413a7722",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "### *t* test for paired samples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7dbe59b-7e10-4bd6-b455-37e4a517b30f",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "<img src='img/paired1.svg' />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa3d8377-0704-4371-ab1f-e8567e66f112",
+   "metadata": {},
+   "source": [
+    "Let us now assume `x1[i]` and `x2[i]` are measurements from a same animal `i`, under two different experimental conditions.\n",
+    "\n",
+    "With Pingouin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "70ee9cae-b8ad-4b51-b443-03befe14606c",
+   "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>T</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>CI95%</th>\n",
+       "      <th>cohen-d</th>\n",
+       "      <th>BF10</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T-test</th>\n",
+       "      <td>-2.361598</td>\n",
+       "      <td>7</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.050223</td>\n",
+       "      <td>[-52.96, 0.03]</td>\n",
+       "      <td>0.980872</td>\n",
+       "      <td>1.892</td>\n",
+       "      <td>0.664343</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               T  dof alternative     p-val           CI95%   cohen-d   BF10  \\\n",
+       "T-test -2.361598    7   two-sided  0.050223  [-52.96, 0.03]  0.980872  1.892   \n",
+       "\n",
+       "           power  \n",
+       "T-test  0.664343  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pg.ttest(x1, x2, paired=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cfc1090-95e0-4f80-8eec-9e81b21082ef",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "SciPy's *t* test for paired samples is [ttest_rel](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html):\n",
+    "\n",
+    "`scipy.stats.ttest_rel(a, b, axis=0, nan_policy='propagate', alternative='two-sided')`\n",
+    "\n",
+    "This is actually a one-sample *t* test of the between-group differences against a population mean equal to zero (compare [1](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L6450-L6460) and [2](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L5647-L5656))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "234dc53a-dce0-426e-8f57-4a4f9bfa38fc",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "### Effect sizes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed009c59-e22b-4771-bfc9-f113dc365047",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "Very low *p*-values are not measurements of the strength of an effect. One should consider the *effect size* instead.\n",
+    "\n",
+    "A common measure of effect size for two independent samples is [Cohen's $d$](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d): $d = \\frac{\\bar{X_2}-\\bar{X_1}}{\\sqrt{\\textrm{PooledVariance}}}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "3beb1fbb-a1ac-40aa-b4ce-b97f151392f5",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9cd92a37628451bbcadc336f0bdb0bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.5, description='cohen_d', max=4.0), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scipy_material.illustration_cohen_d();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8e1face-4845-459d-95c2-8674181d39b5",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "With large enough sample sizes, one can find significant effects of size $0.1$ for example, which may not be of practical interest. Statistical significance does not imply practical significance.\n",
+    "   \n",
+    "Measurements of effect size were proposed together with [tables](https://core.ecu.edu/wuenschk/docs30/EffectSizeConventions.pdf) for interpreting size values. For example, for Cohen's $d$:\n",
+    "   \n",
+    "| $|d|$ | size of effect |\n",
+    "| :-: | :-- |\n",
+    "| $0.2$ | small |\n",
+    "| $0.5$ | medium |\n",
+    "| $0.8$ | large |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a060217-5a49-49b5-b8df-ec2609084c1a",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "Pingouin's [compute_effsize](https://pingouin-stats.org/build/html/generated/pingouin.compute_effsize.html#pingouin.compute_effsize) provides various effect sizes (default is Cohen's *d*) for the comparison of two means, for dependent or independent (default) samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2ba983bc-ef4c-4a15-ac23-776fffd88afb",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.980871648099785"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pg.compute_effsize(x1, x2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b0fd532-87d0-4747-b3a3-322d77c2bfbd",
+   "metadata": {
+    "heading_collapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## Analysis of variance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b03b649e-32e0-421a-8ba9-fcb81b01404a",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "### One-way ANOVA\n",
+    "\n",
+    "Comparing three or more group means reads $H_0: \\bar{X_0} = \\bar{X_1} = ... = \\bar{X_k}$ and is usually carried out with an *analysis of variance*.\n",
+    "\n",
+    "The total variance ($SS_{\\textrm{total}}$) is decomposed as the sum of two terms: *within-group* variance ($SS_{\\textrm{error}}$) and *between-group* variance ($SS_{\\textrm{treatment}}$).\n",
+    "\n",
+    "$$\n",
+    "\\underbrace{\\sum_j\\sum_i (x_{ij} - \\bar{\\bar{x}})^2}_{SS_{\\textrm{total}}} = \\underbrace{\\sum_j\\sum_i (\\bar{x_j} - \\bar{\\bar{x}})^2}_{SS_{\\textrm{treatment}}} + \\underbrace{\\sum_j\\sum_i (x_{ij} - \\bar{x_j})^2}_{SS_{\\textrm{error}}}\n",
+    "$$\n",
+    "Many statistical tools give the following detailed table:\n",
+    "\n",
+    "| Source | Degrees of<br />freedom | Sum of squares | Mean squares | $\\mbox{ }F\\mbox{ }$ | $p$-value |\n",
+    "| :- | :-: | :-: | :-: | :-: | :-: |\n",
+    "| Treatment | $k-1$ | $SS_{\\textrm{treatment}}$ | $MS_{\\textrm{treatment}}$ | $\\frac{MS_{\\textrm{treatment}}}{MS_{\\textrm{error}}}$ | $\\mbox{ }p\\mbox{ }$ |\n",
+    "| Error | $N-k$ | $SS_{\\textrm{error}}$ | $MS_{\\textrm{error}}$ | | |\n",
+    "| Total | $N-1$ | $SS_{\\textrm{total}}$ | | | |\n",
+    "\n",
+    "The statistic $F = \\frac{MS_{\\textrm{treatment}}}{MS_{\\textrm{error}}}$ follows the Fisher's [F](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f.html) distribution under $H_0$.\n",
+    "\n",
+    "More about it at: https://www.coursera.org/learn/stanford-statistics/lecture/pskeN/the-idea-of-analysis-of-variance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5329bbf-ee6c-432d-a130-dca099e18258",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "If we define three samples or groups:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "e917eacc-d454-4476-ac0e-404d67ba5f47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# copied-pasted from https://www.statology.org/bartletts-test-python/\n",
+    "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]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "671a6865-7bf5-4ff4-b4ff-ec8ee4454994",
+   "metadata": {},
+   "source": [
+    "To perform an ANOVA with Pingouin, we will have to represent the above data in a single DataFrame, in the so-called *long format*, with one row = one observation, and each variable (both dependent and independent) as a column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "47010494-cac1-4af9-9490-77fd0cf0d5b8",
+   "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>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>20</th>\n",
+       "      <th>21</th>\n",
+       "      <th>22</th>\n",
+       "      <th>23</th>\n",
+       "      <th>24</th>\n",
+       "      <th>25</th>\n",
+       "      <th>26</th>\n",
+       "      <th>27</th>\n",
+       "      <th>28</th>\n",
+       "      <th>29</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>response</th>\n",
+       "      <td>85</td>\n",
+       "      <td>86</td>\n",
+       "      <td>88</td>\n",
+       "      <td>75</td>\n",
+       "      <td>78</td>\n",
+       "      <td>94</td>\n",
+       "      <td>98</td>\n",
+       "      <td>79</td>\n",
+       "      <td>71</td>\n",
+       "      <td>80</td>\n",
+       "      <td>...</td>\n",
+       "      <td>79</td>\n",
+       "      <td>78</td>\n",
+       "      <td>88</td>\n",
+       "      <td>94</td>\n",
+       "      <td>92</td>\n",
+       "      <td>85</td>\n",
+       "      <td>83</td>\n",
+       "      <td>85</td>\n",
+       "      <td>82</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0   1   2   3   4   5   6   7   8   9   ...  20  21  22  23  24  25  \\\n",
+       "response  85  86  88  75  78  94  98  79  71  80  ...  79  78  88  94  92  85   \n",
+       "group      A   A   A   A   A   A   A   A   A   A  ...   C   C   C   C   C   C   \n",
+       "\n",
+       "          26  27  28  29  \n",
+       "response  83  85  82  81  \n",
+       "group      C   C   C   C  \n",
+       "\n",
+       "[2 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame(dict(\n",
+    "    response = np.concatenate([A,B,C]),\n",
+    "    group    = np.repeat(['A','B','C'], [len(A),len(B),len(C)]),\n",
+    "))\n",
+    "\n",
+    "df.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec8b9c7e-8ba4-4f47-9ff7-33d70d33145f",
+   "metadata": {},
+   "source": [
+    "With Pingouin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "e36f108d-4674-4a1c-89d1-eca3360b1ae0",
+   "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": [
+       "  Source  ddof1  ddof2         F     p-unc      np2\n",
+       "0  group      2     27  2.357532  0.113848  0.14867"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pg.anova(df, dv='response', between='group')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a85b93e4-596d-47c9-a0c5-e8c43150dca7",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "SciPy also provides a [one-way ANOVA](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/mstats_basic.py#L2937-L2967) with function [f_oneway](https://docs.scipy.org/doc/scipy/reference/reference/generated/scipy.stats.f_oneway.html):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "2bf0aafa-d23f-44fe-b9e0-cf4bf13e7314",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "F_onewayResult(statistic=2.3575322551335636, pvalue=0.11384795345837218)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.f_oneway(A, B, C)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "164aa1ae-5446-4680-9571-c783edaf4101",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "The ANOVA is an *omnibus* test and does not tell which groups exhibit differing means. Specific differences are later identified using *post-hoc tests* (more about it in next session)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ea4e6c8-4851-4704-b9ca-6257239ab07b",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "### Size effect"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39925882-deb4-43e1-8d82-9fb1e68df0ba",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "Pingouin's `anova` provides the partial $\\eta_p^2=\\frac{SS_{\\textrm{treatment}}}{SS_{\\textrm{treatment}}+SS_{\\textrm{error}}}$ in the returned table.\n",
+    "\n",
+    "You can pass argument `effsize='n2'` to get the $\\eta^2=\\frac{SS_{\\textrm{treatment}}}{SS_{\\textrm{error}}}$ instead."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b59d33f-5f79-4e57-a629-bce754da1c7b",
+   "metadata": {},
+   "source": [
+    "### Multi-way ANOVA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2194415d-c032-4133-b051-4018f1f05417",
+   "metadata": {},
+   "source": [
+    "Pingouin's [`anova`](https://pingouin-stats.org/build/html/generated/pingouin.anova.html#pingouin.anova) can take a __list__ of factors as argument `between`. Beware that it treats the designated columns as categorical variables and does not warn if the data are continuous. Pay attention to the reported numbers of degrees of freedom."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "a00e151c-d532-49cf-bfa2-25aa003324cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plant_data = pd.DataFrame({\n",
+    "    'water':  np.repeat(['daily', 'weekly'], 15),\n",
+    "    'sun':    np.tile(np.repeat(['low', 'med', 'high'], 5), 2),\n",
+    "    'height': np.array([\n",
+    "                        6.3, 6.8, 5.5, 5.1, 6.0, 6.1, 5.0, 6.1, 3.6, 5.4,\n",
+    "                        6.4, 5.7, 8.3, 7.7, 7.0, 2.9, 3.2, 2.3, 3.9, 4.1,\n",
+    "                        3.5, 5.3, 5.8, 4.6, 3.6, 5.2, 6.2, 5.1, 6.7, 7.0,\n",
+    "])})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "e9d46687-c35e-41fb-8452-087c100c8253",
+   "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>SS</th>\n",
+       "      <th>DF</th>\n",
+       "      <th>MS</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>water</td>\n",
+       "      <td>15.552000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15.552000</td>\n",
+       "      <td>19.117394</td>\n",
+       "      <td>0.000205</td>\n",
+       "      <td>0.443380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sun</td>\n",
+       "      <td>21.424667</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10.712333</td>\n",
+       "      <td>13.168203</td>\n",
+       "      <td>0.000138</td>\n",
+       "      <td>0.523208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>water * sun</td>\n",
+       "      <td>5.694000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.847000</td>\n",
+       "      <td>3.499693</td>\n",
+       "      <td>0.046376</td>\n",
+       "      <td>0.225791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Residual</td>\n",
+       "      <td>19.524000</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.813500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Source         SS  DF         MS          F     p-unc       np2\n",
+       "0        water  15.552000   1  15.552000  19.117394  0.000205  0.443380\n",
+       "1          sun  21.424667   2  10.712333  13.168203  0.000138  0.523208\n",
+       "2  water * sun   5.694000   2   2.847000   3.499693  0.046376  0.225791\n",
+       "3     Residual  19.524000  24   0.813500        NaN       NaN       NaN"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pg.anova(plant_data, dv='height', between=['water', 'sun'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9fbad44-1b73-411d-a861-b3c09e824c9f",
+   "metadata": {},
+   "source": [
+    "Note that Pingouin's `anova` defaults to using type-2 sums of squares. Matlab `anovan` function uses type-3 ss instead.\n",
+    "\n",
+    "In the above table, we observe an interaction effect between the two factors. If we plot the data, we should the effect of *e.g.* `water` depends on the other factor `sun`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "83732c12-c360-42ae-94cf-06d1bf02247b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "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');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae728113-0020-4d50-80bc-9dcb11a3dd42",
+   "metadata": {},
+   "source": [
+    "Alternatively, Pingouin provides an interaction plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "ccc23571-46dd-4250-9c11-feb4eeb2b2e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pg.plot_paired(data=plant_data, dv='height', within='water', subject='sun');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bf2c9ad-68fd-4f4b-b68c-180de69d3522",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "### Assumptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd484cda-1d0b-4468-a092-f362744f8aa0",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "The standard *F* test requires the data to exhibit the following properties:\n",
+    "\n",
+    "* independent observations,\n",
+    "* normally distributed residuals,\n",
+    "* all groups have equal population variance (*homoscedasticity*),\n",
+    "* at least 5 observations ($n \\ge 5$) per group (and equal number)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "305a944c-3a67-49df-9ff5-9a3b8c36cfed",
+   "metadata": {},
+   "source": [
+    "Pingouin provides [more forms of analyses of (co-)variance](https://pingouin-stats.org/build/html/api.html#anova-and-t-test)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e38c759e-8807-4975-9620-9c2100b964a8",
+   "metadata": {
+    "heading_collapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## Checking for common assumptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af5b8858-496a-4045-83de-db7fbb463634",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "Most parametric tests make assumptions or have requirements on the distribution of the dependent variable or the residuals.\n",
+    "\n",
+    "The desired properties can be checked with dedicated statistical tests that Pingouin conveniently groups into three functions with self-explanatory names:\n",
+    "* [`normality`](https://pingouin-stats.org/build/html/generated/pingouin.normality.html#pingouin.normality)\n",
+    "* [`homoscedasticity`](https://pingouin-stats.org/build/html/generated/pingouin.homoscedasticity.html#pingouin.homoscedasticity)\n",
+    "* [`sphericity`](https://pingouin-stats.org/build/html/generated/pingouin.sphericity.html#pingouin.sphericity) (not mentioned any further in this material)\n",
+    "\n",
+    "However, visual checks for the desired properties are often preferred."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96dc6c80-6816-4088-9ceb-1ddd58759a98",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "### Normality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ecd70e2-0860-4dc4-9620-b34f2f0e8c31",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "Having this property is usually not critical, because most tests are fairly robust to non-normality.\n",
+    "We only need to avoid cases of \"extreme non-normality\".\n",
+    "\n",
+    "Beware however that, in the case of residuals (prediction errors of a model), a departure from normality may be an indication of systematic errors in some groups."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "b24c3e20-6cbc-4f03-b189-780c609a3320",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(1245619531)\n",
+    "\n",
+    "x_normal = 2 * stats.norm.rvs(loc=0, scale=1, size=30) # generate 30 observations from the standard normal distribution\n",
+    "x_not_normal = stats.norm.rvs(loc=[-1,1], scale=[1,3], size=(15,2)).ravel() # generate 30 observations from a mixture of normal distributions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5fa2537-0d3b-4b1d-bba4-a479cdf2c0a9",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "#### Graphical approaches"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c977f26f-df05-4ead-a6cd-5ad9f318db0f",
+   "metadata": {},
+   "source": [
+    "Pingouin provides Q-Q plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "7b4317d7-248a-462f-a252-a815215f4072",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yOyEhsr9gQZlabE/s6e0NdUqdgx714Ow/AEQVK8awVl0Iz5jZowNZAIthJDXInTZFRETg7+9PeHg42bNnN7s5SqVvV65A0aKS8GnfviRv+Ka5dAlq14aDB6FUKVi7Nu6RqCcEsnqvUypt0t9tpdzYqVNQuTKEh8OaNVCnTpIf27gR7HmGtWFDsqdwzMWLcsEDBwCIDnyEbzp053KWbG4dyNp7v9ORWaWUOXLmhB075KbvToHs1atSc+3gQdm+eTNuvYsnBLJKKaWUMkH+/NJ/aNDgvlHogzINWyzy/t1JnVLk8mVp051A9nahQnzb7nW3D2QdocGsUsq1DEMCxMyZZdTTnURGQtOmsGePbBcqBOvXQ2CgBrJKKaWUSl7GjFKCJ7nUw3fYkjq1aSMfTRjQPiipk0PCw6FxY7izrv92/vwMb9+NS9n800wgCx6UAGro0KE8/fTTZMuWjXz58tGyZUuOHDlidrOUUo6aMgWeegr++svsliR28yY0bw7bJWAlb15Ytw6KF9dAVimllFL3Mgzo0wfGj5ftBwSyNg+T1Mku167Jw/nffwcgJk8eRrbvznn/XGkqkAUPCmY3bdpEz549+e233wgODub27ds899xzXL9+3eymKaXstXcv9OolU3lz5DC5MQlER8sj0g0bZDtHDggOhlKlNJBVSimlVNLGjJHXpEnSl3BAwkzDc+bInydOOCGQvXEDmjWDbdsAiMmZi1Ed3iAsV540F8iCB00zXrVqVaLt6dOnky9fPnbv3k2tWrVMapVSym4REdC2LURFweLF8vjRHcTEwEsvwYoVsp01K6xaBeXKaSCrlFJKqaT9+iu8+64sSVq2DHx9HT6Ft7eTkjzZ3LoFLVvCpk0AWP39GdOxO6F58qXJQBY8KJi9W3h4OAC5cuVK9jNRUVFERUXFbUdERKR6u5RSSTAMSVV/7Bh88oms4XAHsbHw2muwcKFsZ8okqfSrVtVAVimllFJJ278fOnSQdbLLlt07X9gM0dHw4osyswywZs3GDx26czpfwTQbyIIHTTNOKDY2lr59+1KjRg3KlCmT7OeGDh2Kv79/3CswMNCFrVRKxRk3Dn75RR4/fv652a0RhgE9e0pxc5AnqosWQe3aGsgqpZRSKmkXLsALL0jSyJ9+gooVzW4R3L4twfWdWWaxmTMzvmM3ThYMSNOBLHhoMNuzZ0/++usv5s2bd9/P9e/fn/Dw8LjXmTNnXNRCpVQi9erJa84c8HGDCSGGAe+/DxMmyLa3N/z8MzRqpIGsUkoppZKXI4fUbf3mG2jVyuzWyHKpV16RB/JAbMaM/NihG8cLF0nzgSx44DTjXr16sXz5cjZv3kzAA9bc+fn54efn56KWKaWSVbq0ZAZ2F59/DiNGyN8tFhmdbdlSA1mllFJK3Z+vL0ybZnYrhG251M8/y6avL5M7vM7fjxRPF4EseNDIrGEY9OrVi0WLFrF+/XqKFStmdpOUUvdjGNC7N/zxh9ktSWzYMPjii/jtSZOgY0cNZJVSSimVvPfekwKxIA/C7SzDk2piY6FHj7jlUkaGDExr15VDRUumm0AWPGhktmfPnsyZM4clS5aQLVs2zp07B4C/vz+ZMmUyuXVKqXsMHw4//ABnzkj2Yncwdix89FH89ujR8PrrGsgqpZRSKnljxsiMrrJlJYDMmNHc9tjq206eLJve3sxs04W/Hi2drgJZ8KCR2fHjxxMeHk6dOnUoWLBg3OvnO8PqSik3snUrfPyxZPe7c6M13bRpUuPW5quvoE8fDWSVUkoplbxly6BvXyhYUMrxuEMg+/778oAeMLy8mP3iK+x9vEy6C2TBg0ZmDcMwuwlKKXtcvAjt28v0m/nzIU8es1ska0m6dYvf/uQT6N9fA1mllFJKJW/3bskSbCvdZ3ZlFMOATz+Ny/thWCzMa9WJ3U+UT5eBLHhQMKuU8gCxsfDyyxAaCt99B9Wrm90ieaL68svSNoB33oHBgzWQVUoppVTyzp6FZs3g1i1YssShEjxWK4SEQFiYDOjWrCmFEx7al1/KzLI7FjTvwM6yldJtIAsazCqlnOniRQlkW7SAfv3Mbo0UDm/TRtLWg4zOjhxJ8JbfNZBVSimlVPLy5pV6suXKyZ92CgqS5+b//hu/LyBA0nS0bv0Q7Rk2DAYOjNtc9EJbtlWomq4DWdBgVinlTPnzw44dEjyaneUvJESC6uho2X7pJZgwQQNZpZRSSiXPMKQPkyED/PjjPf2Z+426BgXJM/S7V0eGhsr+BQtSGNCOHp0ogeWyxi3ZXLlGug9kwYMSQCml3FhYWHwJnixZwN/f3Pb8/js8/zzcvCnbrVrBjBkEb9utgaxSSimlkmYY0L07fPttfFCbQFAQFC0KdevKM/K6dWU7KEiC3HfeuTeQtZ0WJI+U1epgmyZMkAPvWNGgGeufqaOB7B0azCqlHs7t29CunayPPXDA7NbAn39Co0Zw7ZpsN24Mc+cSvP0PDWSVUkoplbwvvoApU2QI1Taz6w7bqGvC6cMQP+o6ZMi97yVkGFKtMCTEgfZMmwZvvRW3GVynMcHP1k8fgaydyX81mFVKPZz334ctW+DFF+GJJ8xty5Ej0LAhXLki27Vrw8KFBO/cp4GsUkoppZI3bRp8/jkULy7JI/384t6yZ9R19Gj7LhMWZmd75syB11+P29zwbANW1G6UPgLZs2dl6NsOGswqpVLup5/g++/hqadg4kRz18meOAH168OFC7JdtSosW0bw7r80kFVKKaVU8tasgTfegNy5YdUqyJcv0dshIQ8edb182b5LFSxox4cWLIDOneMi5ZBqdVha/3nKln407QeyADNnwooVdn1Ug1mlVMrs2yc3/hw5ZO5NlizmtSU0VALZ0FDZLl8eVq4keO8hDWSVUkoplbyjR2V2mY+P1JItWfKej9g7mporV/LP9S0WKVNbs+YDTrJ0KXTsGLe4dvvTzxL0XIu0H8ieOgVRUfL3996DqVPtOkyDWaVUynz2mdRemz0bSpQwrx0XLkCDBjIyC1C6NKxZQ/BfRzWQVUoppdT9FS8Or74q03qfeSbJj9g1mopMRYZ7A1rb9qhRD6g3u2oVtG0bV1JwZ8Vn+KVJ67QdyFqtMsvvySdh6FDZlyGDPGCwgwazSqmUmT0bFi6Epk3Na8Ply7JG9vBh2S5eHNauJfjwCQ1klVJKKZU8Ww16b28YM0YqHySjZk2pFfugUddPPpEZwoULJ34/IMCOsjzr1kkb7iSe+uOpysx7oR1lniiZdgPZQ4egVi15CpApU4pyr2gwq5RyjG2uTZYs973xp7qICGjSRLIXg3yLrFtH8PF/NZBVSimlVPKiouC55+Crr+zKmuvtHZ/g6UGjrq1bw8mTsGGDDPZu2CCTx+4byIaEQPPmMuMN2PdkeWa36Jh2A9nbt+W/ffnysG2bJHs6eFCqYzhIg1mllP2WLZPRz59/NrcdN27ACy/Azp2ynT+/jMieuaCBrFJKKaWSFxsLXbtKlLlvn90lYFq3tn/U1dsb6tSRpa916jxgavFvv8kstxs3ADhQqiwzW7/Ck08+ljYDWZCBiE8/hbx5ZY3w7Nny9xTwcXLTlFJp1dGj8PLL4OVlbgmeqCgZEbYVasuVSwLZ81c1kFVKKaXU/X30EcydK3OHZ8yQfo2dWreGFi2kCxIWJmtpa9Z8QLB6P7t3Q+PGEBkJwOGSpZnWpkvaDGRv3ZLlYYUKQaVKMG8eNGoE/v4PdVoNZpVSDxYZKXfwiAiZM1O2rDntuH0b2reXFPoA2bNLsqcrNzSQVUoppdT9jRoF330nyYaWLIGMGR0+hW3U9aH9+adMdQ4PB+Bo8ceY2q4rT5R5PO0Fslu3Ss3cXLnkSYC3d4qmFCdFpxkrpe7PMKBbN/jrL+jbV+bMmMFqlZprS5bIdubMsGIFwTesGsgqpVxu8+bNNGvWjEKFCmGxWFi8ePEDj9m4cSMVK1bEz8+PRx99lOnTp6d6O5VSd2zZAu++K/OEV66EnDnNa8uhQ1KJ4U5x2n+KFGdyh9cpVbZ02gpkIyMluVPNmjLDr1q1+MRbTqLBrFLq/mbMkDWytWrBsGHmtCE2Vmrazpsn235+sHQpwbEZNJBVSpni+vXrlCtXjrFjx9r1+RMnTvD8889Tt25d9u7dS9++fenWrRurV69O5ZYqpQCoXh0+/ljK3wQGmteOo0ehfn24eBGAUwFFmdjxDR5/6om0FcgGB8tMvu+/l7KJ27bB8OHSh3MinWaslLq/du1g717o31/qfrmaYciIsK14to8PLFxIsG82DWSVUqZp0qQJTZo0sfvzEyZMoFixYgwfPhyA0qVLs2XLFkaOHEmjRo2SPCYqKoqoqKi47YiIiIdrtFLp0ZUrMgrr5RVfx9QsJ05AvXpxlSH+LRTIhE5v8Fj5J9NWIBsVJdOKw8Jg4ED43/+cHsTa6MisUipptmkgmTPLGpP8+c1pxyefSP03kC+iOXMIzp5XA1mllEfZvn07DRo0SLSvUaNGbN++Pdljhg4dir+/f9wr0MzRJKU80alTUKYMfPGF2S2BM2ckkP33XwDC8hdi/MtvUrJC2bQTyJ45I3/6+UmG4t27YdCgVAtkQYNZpVRSIiPhmWdgwgRz2zFkSOKnqFOnElygiAaySimPc+7cOfLf9VAwf/78REREcPPmzSSP6d+/P+Hh4XGvM7aOolLqwf77TzIFnz0riYfsZLXCxo2S8HjjRtl+aGFhEsiePAnA+bwFGPfKW5So+FTaCGTPn5cEnU8+GResU7MmPPVUql9apxkrpRKLjYVXX5WnaTVrmteOUaOkBpnNuHEEFy+tgaxSKt3w8/PDLxVHNJRKs27cgObN4fBhWSfbq1fcW1Zr8qV1goIkX5EtHgOpIzt6dOI6sg65cEHWyB47BsDFXHkZ1/ktilUu7/mBrGHICOw770gyq5o1pfKEC2kwq5RKbMgQWLhQbrzffmtOGyZOlIyDNt99R3CZShrIKqU8VoECBTh//nyifefPnyd79uxkypTJpFYplQbFxMgo4bZt8Mor8NVXcW/dL1gFaNNG4rOEQkNl/4IFKQho//tPshYfOiSbOXMzrsvbFHm6oucHsmfOwJtvwooVkDUrjBsHPXo4VLfXGTSYVUrFW7JEFuoXKyYZjH1MuEX89JPcHG0+/5zgKjU1kFVKebRq1aqxYsWKRPuCg4OpVq2aSS1SKo2aMQOWL4cmTWDKFLBYAAlkkwtWX3wRcue+9z2QfRaL5KJs0SJ+FPeBrl6VOrL79wNwxT8nYzu/TWDVyp4fyAIMGCCBbOPG8OOP8MgjpjRD18wqpcSRI/Dyy5AliwS1uXO7vg1BQTLF2fZt8sEHBNdtrIGsUsrtREZGsnfvXvbu3QtI6Z29e/dy+vRpQNa7du7cOe7zb775Jv/88w8ffvghhw8fZty4ccyfP593E85CUUo9vK5dZaj1l1/iqjBYrTIim1ywCjKImhzDkIHIkBA72xARIUHeH38AEJ4tO+M6v01AtSqeHcjeKScEwDffwKxZEtCaFMiCBrNKKZsiReSR5cyZUhfM1VauhA4d4jMtvP02wS+8yIoNvwEayCql3MuuXbuoUKECFSpUAKBfv35UqFCBgQMHAhAWFhYX2AIUK1aMX3/9leDgYMqVK8fw4cOZPHlysmV5lFIO2rdP/vTygj595OH8HSEhiacWp9Sdijr3d/06PP887NgBwLUs2RjX+W0K1njGcwNZw4Dp06FECXlIAFLl4uWX40a+zaLTjJVSImNGmDbNnGtv3CgLUWxJA159leB2r2ggq5RyW3Xq1MFIapjnjunTpyd5zJ49e1KxVUqlU3PmQKdOkuvj/ffveduuINQOBQs+4AM3b0riqS1bALieKTPjO79F/po1PDeQvXgR3ngDFi+GHDlMD17vpiOzSqV3H38sdVzv0ylLVdu3wwsvwK1bst2uHcFd3mDFRnmiqYGsUkoppZK1Zg106QI5c8o62SQ8MAh9AIsFAgMfUOQhKgpatYL16wG4mTETE155izy1a3puILt8udTpXbxYEoPu3y+z+NyIBrNKpWczZ8qahx9/jA8mXWnPHvniuX5dtps1Y+2b77Bi005AA1mllFJK3cfOnTKzK0MGCbyefDLJj9WsKVmLkxtUtFgkVYjFcu9nbNujRt0n+VN0NLRtC6tXA3DLLyMTXu5Bznp1PDeQ3bQJmjWT9b+jR8tDg4AAs1t1Dw1mlUqvtm2D7t3lSebixeDq0hAHD0qWv/Bw2W7QgHV9PuDXzb8DGsgqpZRS6j4OH5a1qbduwfz5UL16sh/19o4vv5NcsDpxopTfKVw48fsBAQ8oyxMTAy+9BMuWARCdwZeJL72Bf4P6nhnI2mbq1aoF770Hu3fLGmQXl9yxl66ZVSo9OnUKWraE2FipKfvoo669/rFjUnft0iXZfvZZ1n3wKcu3StY/DWSVUkopdV8zZkg/Yto0Wa70AK1bS1CaVJ3ZUaPig9UWLSRhVFiYTE+uWfM+I7JWq0xxXrgQgNs+GZjUsRtZGzX0vEA2Ohq++AIuX5aasRYLfPed2a16IA1mlUpvIiMlOcHFizBhAtSt69rrnz4t6y5s2RgqV2bD/waxfPteQANZpZRSStnhq6+k/E3t2nYf0rr1g4NVb2+oU8eOk8XGQrduknwKiPH2YUqH18jUtInnBbIHD8Irr0gpoRIlZGpx9uxmt8ouGswqld74+sLTT8vNv0cP1147LEwCWVu5ijJl2PjZVyzdKQXFNZBVSimlVLIiI2HRIgm8LBaHAlkbu4PV+zEMePttKVcDWL28mN7uVXxfeMGzAtnYWPjhB/joI5mu/cYbMHw4ZM1qdsvspsGsUumNry9MmuT67MWXLsnU4mPHAIgo+BhLug3jj90HAfsDWavVgek/SimllEobbNmC166VGrLJLmJNZYYBfftK8kwkkJ3ZpgteLVp4ViBrGDJMvXw55Msn9WPtmK7tbjSYVSq9+OknuHAB3n036XR9qenqVUn2dFAC15MUoVfgd5S8+jcARfLbF8gGBSW91mX0aPO+05RSSimVyqxWePllCWRbt5blUmYwDBnF/P57AGItFma36oTRurVnBbIg/cB69cDHR7Jf5c1rdotSxD3TUimlnGv7dnj9dfjySzh/3rXXjoyEpk2lDA8QSiH6Vh5Jyab/ALBtXTX6vV2FoKD7nyYoSEqbJQxkAUJDZf+DjldKKaWUBzIM6NlTsjfVrQuzZ0sAZobPP4dvv43bnNe8AzFt23lOIHv5Mrz/Pty8Kdt9+0oHykMDWdBgVqm07/Tp+MzFCxZAgQKuu/bNm/L0dPt2AC6Ql76VRlLsBVkzu3VdNXaGyIhs377y4DUpVquMyCY1M9q2737HK6WUUspDffqpTOmtVElKCWbMaE47vvpKsv3eMf+Fttzq+JLnBLLBwVC2rKyJnThR9rl6pl4q0GBWqbTMlrn4wgUYM0amk7hKdLQMmW7YAMAVcvBuhREENJMsxlvXVeP3O4GsYcCZM7IWNikhIfeOyCb0oOOVUkop5YEMA/77Dx5/HFauNC/D7vDh8MkncZtBjVsR+XJnzwhko6Nlidlzz0n+kmHDoFcvs1vlNLpmVqm0ymqFjh1h3z65ab35puuubSsgvmIFALczZqXf48PJ1+I/IHEgm5CtWo+9+1P6OaWUUkp5AIsFxo+X3Bs5c5rThrFjZWruHUsbNufqq695RiB7+jS0awc7dkCZMjJF+6mnzG6VU+nIrFJplWFAYCA0aQIjR7ruurGx8NprcQXEyZSJVX2/JUera0DygSxIdmJH9qf0c0oppZRyY7/8Al9/LX+3WMwLZCdNSjSKuaJuUy693t0zAlmAc+ekdmzXrhLQprFAFnRkVqm0y8dHnibevu26RAm2JA2zZsl2hgz8Mfhr1kdGAZLsKalA1mKRrMQ1ayZ92po15f3Q0KTXzT7oeKWUUkp5iFWroFMnyJwZOneGQoXMacfMmdCjR9zmmpoNOdfjLfcPZK1WmU6cPz9UqQJ//gmlSpndqlSjI7NKpTWrV8PQoRL1WSxSV9YVDEOm4UyYINve3uz9fAizIiX6LJK/Gr9vqXJPngHb9qhRydeL9faW8jsJP+/I8UoppZTyAFu3SukdHx/49VfzAtmff5bRzDtP0DdUq8u/b/d2/0D23Dlo2FDWx9oyFqfhQBY0mFUqbTlwQNZGfPEFHD/u2mt//jmMGCF/t1jY/7/PmHFbAumm9arR980qLFgAhQsnPiwgQJIsP6hObOvWPNTxSimllHJj+/bB889L3o2gIKhRw5x2LFokI8OxsQCEVKnJiT7v0rltU/cOZDdtggoVJPHmY4/Jf8d0QKcZK5VWnD8vXwIREbLW5NFHXXftYcMSpas/+EF/pnpLxsGm9arRsKZMLW7dGlq0kKzDYWGyxrVmTftHVB/2eKWUUkq5odBQaNRI+jDz5kHjxua049dfoX37uFp/2ytW42jf9907kI2NlX7YJ5/ET2Xr3dvjS+7YS4NZpdKCmzelluypU1IHrU0b11177Fj46KO4zcO932VS5nxA4kDWxtsb6tRJ+eUe9niH2KZqK6WUUir1FCwoT6wrVJAZZmZYswZefFFyjQC/l3uaQ+9/TOd2z7tvIAuSq2TCBEn6+csvULWq2S1yqYeeZmy1Wtm7dy9XrlxxRnuUUo6KjZV1Hb/9Bq++Ch9/7LprT5uWKMvf0e5v8WPuIkDSgazHuHBBvkznzjW7JUopB2ifRCkPY1vX6eUF48ZB9+7mtGPjRhkUiJKElX+UqcBfH33i/oEsSAWJFi1gz550F8hCCoLZvn37MmXKFEC+NGrXrk3FihUJDAxk48aNzm6fUupBDEOeaNauDT/+6LqRxJ9/hm7d4jb/eeVVxhV+HPDgQNYwZHrTk0/K001beSGllFvSPolSHuzyZcm2O2RI0qUKXGXbNnjhhbjA+s9ST7Hv44G80u4F9wxkDUNq7/79t2w//TQsXgy5c5vaLLM4HMwuWLCAcuXKAbBs2TJOnDjB4cOHeffdd/nkk0+c3kCl1AN4e0sd2dWrXZe5eOlSePnluOQIp9q0Z0xxuS94bCB765ZMcerYUb7Qxo6VgFYp5ba0T6KUh7p2TdbF/vUX/Pefee34/Xdo0gSuXwfgQMkn+OPTz3mlQzP3DGSvXZN+yttvJ5oZl545HMxeunSJAgUKALBixQratm3LY489xmuvvcb+/fud3kClVDJWr4ZBg+KfZvr5uea6wcHQtm1clrx/n2/BqCefAYuF0kWrUa+6BwayIP/9vL2hXj3Yv1++KLw04btS7kz7JEp5oJs3oVkzCSS7dYPhw83JT7F3r5SwiYgA4Ejxx9k1YDAvd2junoHs0aMyjfjnn6FWLZgxw+wWuQWHe2r58+fn4MGDWK1WVq1aRcOGDQG4ceMG3u74P16ptGjfPgkov/kGjh1z3XVDQmRdRnQ0AIefbsSISrXBYmHrumr0eLUKRYtKRn2PEBoqX6IgX6QzZsDatVCsmLntUkrZRfskSnmY6GhJUrlpE3ToIImLzAhk//oLGjSAq1cBOFr0UX777Es6vdTCPQPZ1atlSvahQ/D++7BunSwxU44Hs127dqVdu3aUKVMGi8VCgwYNANixYwel0nhRXqXcQmiolOCJjITZs6FkSddc9/ff5bp31pQcK1+HSY2fw/DyYuu6avweUiWueW3auHlAaxiSvOrJJ+VLwba2LksWzV6slAfRPolSHmbqVFixQkZmZ840p7be4cMSyN6Z3nwisBjbP/+KTp1auWcgGx0Nb70ly6F++gm+/RZ8tCCNjcP/JT7//HPKlCnDmTNnaNu2LX53pjZ6e3vzsSuzqCqVHl27JgFlaCiMGAGtWrnmun/+KfXfrl0D4FKVakx47gVivb0TBbIQX82mb18ZxHW774UzZ+CNN2DVKsieHaZMkeRZSimPo30SpTzMG29I6Ztu3SBDBtdf/9gxqF8fzp8H4FShR9gyaCgvvdzaPQNZkHwoQUFS+7ZSJbNb43YshpHy9GG3bt0iY8aMzmxPqoqIiMDf35/w8HCyZ89udnOUckxMDDRvDitXyqL/7793zSjikSOyNuPCBQAul6/I1007cNvX955A9m4bNriwJqw9Zs2SemzXrkHTppL9OSDA7FY5nd7rVHrkaX2SlNDfbeWRDEOmxd6ZOWGaU6ekP3P6NAD/FijMxi+/o8Orbd0vkD1zRsoujh0Ljz9udmtMYe/9zuFpxlarlcGDB1O4cGGyZs3KP//8A8CAAQPi0uMrpVKBlxeULSvp40eNck0ge+KEPMG8E8hefeJJhjVpZ1cgCxAWlvpNdMjNmzJUPGMGLF+eJgNZpdIT7ZMo5eYMA957Dxo2lJlQLmK1ygqiuXPlT+upfyXB451ANixvATZ98Y17BrJbtkDlyvIAYP58s1vj9hwOZocMGcL06dMZNmwYvgnKgJQpU4bJkyc7tXFKqQS8vCTh06JFrpm7GxoqgWxoKAARJR9jWLNORPllpHTRBwey4Aa5CWJjZX3OnUyFdO8uI82dO+vaWKXSALP7JGPHjqVo0aJkzJiRqlWrsnPnzmQ/O336dCwWS6JXWh9JVoqBA6V8YJky0LKlSy4ZFARFi0LduvDSS9CxbhgnH60Pdx52nc+djw2Dh9H+tQ7uF8hOmiRB9+XLUkt2wACzW+T2HA5mZ86cycSJE+nUqVOiTIHlypXj8OHDTm3c3TZv3kyzZs0oVKgQFouFxYsXp+r1lHILs2ZB//5xNV1dsuj/wgWZDnTiBACRRYoyrGVnbmbKTNN61Xj95SoEBCQfD1osEBgINWumflOT9c8/Eoy//rp8mdoali+fiY1SSjmTmX2Sn3/+mX79+vHZZ5/xxx9/UK5cORo1asSFOzNZkpI9e3bCwsLiXqdOnUrVNiplqq+/hi+/hMcek0oBuXOn+iWDgiQJ5b//ynYeLrKWBpSI+RuAiznzsGHwMNp1e8m9AtnoaCkJ+MYbkCOHjMq++abZrfIIDgezoaGhPProo/fsj42N5fbt205pVHKuX79OuXLlGDt2bKpeRym3sXYtvPYaTJwIZ8+65pqXL8t0oDsdwYi8hfn2xa5cz5KVJnWq0bBmFby9YfRo+fjdAa1te9Qok5I/xcbKeuKyZWVuUevW8jBAKZXmmNknGTFiBN27d6dr16488cQTTJgwgcyZMzN16tRkj7FYLBQoUCDulT9//lRto1Km+f57+e4tVkwCMxf8W7da4Z13ZGYzQE4uE0xDnuQgAJf9czKw/je0ev1l9wpkQSpUrFoFFSrArl2ytlfZxeFg9oknniAkJOSe/QsWLKBChQpOaVRymjRpwpdffkkrV2VwVcpMf/4pgZiPDyxb5pr1nRER0KSJXBu4kDE/o156jYjs/mxdJyOytpI7rVvDggVQuHDiUwQEyP7WrVO/uff45x/JOPXOO5A5sxQWX7DAJV+iSinXM6tPEh0dze7du+NKAQF4eXnRoEEDtm/fnuxxkZGRFClShMDAQFq0aMGBAwfue52oqCgiIiISvZRye4Yh/YjChSWQdVF+ipCQ+BHZ7ISzmkaUZx8AV7P58+Gzw5i4qAvbt7lRIBsVJX/mygXBwbJe9pFHzG2Th3F4vuLAgQPp0qULoaGhxMbGEhQUxJEjR5g5cybLly9PjTamWFRUFFG2fySgXwLKc5w5I9l2IyMlGKtePfWveeOG1H27s+brvwy5mNztda7kzB2X7Mlikek7tmC1dWspvxMSIsmeChaUqcWmPfCMjpb2t28PY8ZA3rwmNUQp5Qpm9UkuXbqE1Wq9Z2Q1f/78yU5vfvzxx5k6dSpPPfUU4eHhfPfdd1SvXp0DBw4QkExnf+jQoQwaNMjp7VcqVVksMqPs3DkoVMhll7UlnczKNVbShKfZBUBE1ux8VPMbpq3qSqzV232SU/76K/ToISOyZcpAiRJmt8gjOTwy26JFC5YtW8batWvJkiULAwcO5NChQyxbtoyGDRumRhtTbOjQofj7+8e9AgMDzW6SUg8WGSmBbGioJE1wxRBnVJTUrN28GYCIDNmZ3K07F/PkS5S12DZ1p29fmc4DErjWqQMdO8qfLg9kjxyBPXvk76VKwV9/wbx5GsgqlQ54Up+kWrVqdO7cmfLly1O7dm2CgoLImzcvP/74Y7LH9O/fn/Dw8LjXmTNnXNhipRw0dy5895383cvLpYEsyAP1zFxnOS9QHZkhEZk5Cx8/O5TJq7sRa/WO+5ypDEOmYTdvDleuxGVYVimTokwyNWvWJDg42Nltcbr+/fvTr1+/uO2IiAgNaJX7y5JFMv41bCjTZVPb7dsykrlmDQDRmbIw6ZXunMtfMMnyO4YhA8chISbXkI2JgREjJLlT0aKwf78UYE9i/ZxSKu0yo0+SJ08evL29OX/+fKL958+fp0CBAnadI0OGDFSoUIFjx44l+xk/Pz/8/Pweqq1KucTChfDKK5A9u1QMMCHZYs3KN1nl14KaUfJg/nrGzPR/dig/Bvcg1uqNxSIznk1NThkTI327ceMk2F+2DCpWNLFBns/hkVlP4ufnR/bs2RO9lHJbtmFPiwUGD4bhw1P/mlYrdOkCS5bIZsaMTOj4Ov8WCnxgHVlTp+kcOCBTrz/6SNaZDBsmgaxSSrmAr68vlSpVYt26dXH7YmNjWbduHdWqVbPrHFarlf3791PQ9GEipR7SsmXQoYM8jF+zxpyqAVFReLdvQ80o+Z286ZeRT2t+ybh1b8cFsmBickqA8HB44QUJZCtUkGVRGsg+NLtGZnPmzInFzpqMly9ffqgGKZUuGYbM3fXxgW+/lek5qV0HNTZW1mrMnQuA1deXH9u9xolHij8wkAWTpunExso3Uf/+sj721VdldDZnThMao5Qyg7v0Sfr160eXLl2oXLkyVapUYdSoUVy/fp2uXbsC0LlzZwoXLszQoUMB+OKLL3jmmWd49NFHuXr1Kt9++y2nTp2iW7duqdZGpVLd6tWSTMPPT9Z+Vq7s+jbYZpitWAHALV8/vqw/mO9X94mbWhwQIN0HU5JT2sTESKLK5s1h9mzImtXExqQddgWzo0aNSuVm2CcyMjLRdJwTJ06wd+9ecuXKxSOa+Ut5su++k/UTTz0liZhS+wZnGPDuuzBlCgCx3t5MfbELR4s/RpM61VgwWZI92QaLEzJ1mk50NEydKqOxU6bI2mKlVLriLn2S9u3bc/HiRQYOHMi5c+coX748q1atiksKdfr0aby84ifAXblyhe7du3Pu3Dly5sxJpUqV2LZtG0888YRZP4JSD2f/flkW5e0tyYzsnJXgLFYrbNkYQ7FPOvHIDplhFu2TgXX9P2fg/96l4TZv90hOGRkp/brcuSU3Sd68JjYm7bEYRlLdVfe0ceNG6tate8/+Ll26MH369AceHxERgb+/P+Hh4TrlWLmPn36SdSaPPALbt7smYcL//gd3RgtiLV7MevEV9papQNN6UkfWVnQcEge0tsEQl5feOXYsfi3s339LMJsnjwsb4Fn0XqdU2qS/28qtWK3Qq5d0CFyccC0oCN7tY+XL0Fd5hZ8AuO3tw/zun9H+h/7uU0d23jzo3RvWroVy5cxujUex935n18hsRERE3EkeVN4mNW+uderUwYNib6UeLDgYunaVabKrVrkmkB0yJC6QBZjXogN7y1Tgr9+rUbOsTC221ZB95534mm1gwjSdiAjo00emQu/cKV8Ejz3moosrpdyRu/RJlEq3bMOd3t4wfrzLLx8UBG1fjOVHesQFsjFe3gyp8QlfTupPpobe5k4nBhkJ+PJLSVLp7y9Zi1WqsGtk1tvbm7CwMPLly4eXl1eSa1UMw8BisWC11etwQ/pEU7mVU6ekrlhMjBQVd0Ut2VGjZHrxHQuatmFrlWfZuq4au7ZIIJtw1NVqNbGG7NatMmJ94gRUrSoj2Jqp2C56r1NpWVrpk6SE/m4r023bBo0awRdfJOpPuIrVCkWLGHwc2ouejJN9Fi+G1ujPZ9sHYcR6ExAgXQfTBmdv35acJNOmQfHiMgW7VCmTGuO5nDoyu379enLlygXAhg0bnNNCpdK7Rx6BDz+UdbKuCGQnTkz0xbOkYfO4QNaW7MlikTxULVrIl4CthqxL3b4tX5JffSXbn30Gn3yi2YqVUoD2SZQyzY4d0Lgx3LoFJUqY0oSQzQbvhr4XF8jGWix8W+0DPts+KC7Zk6nlAyMioG1byer8zDOwdKnWvU9ldgWztWvXjvt7sWLFCAwMvOdJqGEYWsxbKXtERkr6eosFBgxwzTV/+gnefDNuc1XtRmysUe+erMVuUUN2xAiZmlO8uLTbxQkllFLuTfskSplg1y4Zkb1xA+bPl4y8rmYY5B31Cf0YCUAsFkZWeZdPdgyJC2RtTCsfaLVKR6plS8lYnDmzSQ1JPxyuM1usWDEuXrx4z/7Lly9TrFgxpzRKqTQrPFzm6vbpI2VmXCEoSErY3FlRsL56XVbXaXzf8jsu/xIwjPhMU717S+mdvXs1kFVK3Zf2SZRygT17JMFTZKTksDBrQergwTy5ND7nxw9P9+LDXcPuCWTBhPKBt27JnzlzwsaNsmZLA1mXcDiYta1DuVtkZCQZM2Z0SqOUSpNu3ZIndXv3ynZq15EFrL+uIrZ9B3lSCGypXINlDZuzdX31+9aRdemXwMWL8t9l7FjZzpxZphhny+bCRiilPJH2SZRygalTZfrsrFkyhdYM33wjy47uGF/xTd79Y+Q9gazFAoGBLi4fuH69TLvetUu28+XT0jsuZNc0Y5Di4AAWi4UBAwaQOcHTBqvVyo4dOyhfvrzTG6hUmmC1wssvy9O6du1g9OhUD2Y3f7GRKp+1IiO3AdhZ7mmCmr7Int+qxyV7upvLa8iuXCnZnM+fl+2ePV0S5CulPJv2SZRyoVGjoEMHqFHDnOuPHg0ffxy3uabF6/Re/gNG7L2BLEhzXRZL/vQTvPaaXPzMGahc2UUXVjZ2B7N79uwB5Cno/v378fX1jXvP19eXcuXK8f777zu/hUp5OsOQOmwLF0L9+jBzJng5PCki2czCSe3f/O1vVP6sGRmRaS97nyjPz807sHVDDXYmSPaUVA1Zl3wJ3Lghya/GjgU/P7lo794ayCql7KJ9EqVS2b59krn4rbekU2BWIDt+vGSmvGNXx87Um/Uj85d4m1s+0DDg66/hf/+TqcVLlrh4OFjZ2FWaJ6GuXbsyevRoj0wJryntlSlWroSmTaFiRdiwAVLwby8oKOmarx07yvKVhPsb5t3L/Et1yWFcBeDAY08yrV1XNm+qye8hVbBYIFcuyJQp8XGBgS76EoiIkOzNBw5A2bKSIKFs2VS+aPqi9zqVXnhynyQl9HdbucS+fVCvHly9Kt/VZpWVmToVXn89bnPPix0p+/MsfO48cTetfGBMjAxS/PgjFC0q/TwtveN09t7vHA5mPZl+CShTGIY8WWzTRtZROCgoSA615ze1NAfZRG3ycgmAv4s9xqSXurNpc6171siuXSs3fVNqyHbvLhmdv/4adF2b0+m9Tqm0SX+3Varbt09mkV2+DDNmSL13M8yejfHKK1judH72tWjDkwvm4uNj96TS1BMeDs8+KzPLli+HAgXMblGa5NQ6swldv36dr7/+mnXr1nHhwgVi78rI+s8//zjeWqXSon37pIasxQJvv52iU1itMiJrTyBbgmOspUFcIPtPYDGmdHw9yUAW4MIFGdl1idu34eefoVMn+e/x448pmmqtlFIJaZ9EKSf688/4QHb6dPMC2V9+wejcOS6Q/atpC/cIZA1D+jD+/lJHNls2yJrV3DYpx4PZbt26sWnTJl555RUKFiyYZBZBpdK9lSulBlu/fpKBL4VCQhJPBU5OIKdZR30KITV1zhQMZFKnN9gQUifZrMUuy1gcFgbt28sPExMjZYI0kFVKOYH2SZRykhMnZGrx5cswbRp07mxOO5YswXjpJSx3HkwdatiEUksWmB/I/vsvvPiiJKN65hkTav+o5Dj8L2PlypX8+uuv1DBrIbhS7m7bNrnh+fpKyZmHYE+91/ycYy0NKMJpAM7mK8iEV95k3dZ6SQayLs1YvHmzZG8+f16Ggdu0ccFFlVLphfZJlHKSRx6R/B716kGXLua0YeVKjLZtscTEAHCkbkNK/rrE/ED20CFo1EiyFa9aJcGschsO/+vImTMnuXLlSo22KOX5/vwTnn9eptUuWwbVqj3U6R704C83l1hLAx7jKAAXcudlwitvEby9QbKBLLggY7FhwPDhkkrfywvGjNGyO0opp9M+iVIP6dIlyJNHOgUzZpj3Pb1uHbEtW+J1W8oJHnu2DiVWLccnQwZz2mOzfTu88IKMWH/3Hbz3nrntUfdweK7f4MGDGThwIDdu3EiN9ijluY4dg+eek8QAM2dC48YpOo3VKuVo586VvwcEJP3dkp1wVtOIMhwA4LJ/TsZ3fptVOxuxa0sVcueWYxMKCIAFC1yQsXj9evjgA0mKsGmTZP3TQFYp5WTaJ1HqIezaBY89Bj/8INtmfU9v3kzsCy/gFR0NwIlnalB07Sp8EpTcMsXy5bKGOCICZs3SQNZNOTwyO3z4cI4fP07+/PkpWrQoGe56YvLHH384rXFKeZSgIJlOO368XZmVkkopv2TJvSV4cueOzzlgSwSVhUhW0JRKyO/b1Wz+jOvSk193NWXXFhmRnTgRWrQwKW19/fqyrqRDhxRlcFZKKXton0SpFPr9d2jYEK5dk5FZs2zfjrVpU7xv3QLgVOWqBG5Yi4+fn3ltAoiNhUGDZHbZsmUpHqBQqc/hYLblQ64BVCrN+vBDiRbtmFqcVN3Y3Lnhv//u/ezly/JnrlzyfkZusoQW1GAbANcyZ2V857dYuqcZv4dUuadebJ06D/dj2W32bFi3DqZMkci7Tx8XXVgplV5pn0SpFNixQ2aSXb8u08DatTOnHbt2YX2uEd7XrwNwpnwlCm9aj487lOzz8oKlSyE0FCpXNrs16j60zqxSD+PaNZlS/Pbbdk/PcaRurI3FAoULw4xJ0Tz+v9YU3vMrADcyZmJsl574V2tLNt8qrq8XC5Kh+P33ZSTW3x/++AOKF3dhA9Td9F6nVNqkv9vqoW3fLqOMN25IIGtWYsZ9+7DWro13eDgAZ8uUI9/2LfiYWerGMGDAAPnv8+yz5rVDAalYZ1YpdcetW5KteP16CeJefvmBhzhSNzYhw4Cwf2Mo83Un8t0JZG/5+vHjyz0o36ktDWsmXX4n1V25ImV3goOlpu6iRRrIKqWUUu5q2jQJZH/+2QUJNJJx8CAxdevhcyeQPVfqSfJt3WxuIBsTA2++KbPLNmyALVs014eHcDiYtVqtjBw5kvnz53P69Gmi7yzWtrlsmxOpVFp2+7asB12/Xp5q2rFGFuyvG3s3C7FM4XXybVoAQLRPBia/1J0ynTuaF8gePgzNmkniq1atZIRai4crpVxI+yRKOWjcOHjtNfPKy/z9NzF16uBzRX43L5R8nDzbt+Bj5kyDW7egUyeZOle1qkwv1kDWYziczXjQoEGMGDGC9u3bEx4eTr9+/WjdujVeXl58/vnnqdBEpdxMbCx07SrZmho1gp9+snterz11Y+9lMJaedGEmADFe3kxr/xqPv/aKeYEsyJqbY8dkSs6CBRrIKqVcTvskStlh9WqYMEH+7uNjXiB74gS3a9fB5+JFAC4Vf5Rc27fikyNHsockrPCwcaNsO9W1a1JSMShIEmKtXStJTJTnMBxUvHhxY/ny5YZhGEbWrFmNY8eOGYZhGKNHjzY6duzo6OlcKjw83ACM8PBws5uiPFVsrGG8+aZhgGHUrGkY1687dPiGDXKo/a9Y41vei9sRY/EyprR/zVizeUfq/HwPEhtrGFFR8dt79pjTDnVfeq9T6YUn90lSQn+3lcOWLTMMX1/DyJzZMM6eNa8dp04ZUYULx/Vn/nukqHH7/Pn7HrJwoWEEBCTuFwUEyH6nee01OXGbNoZx65YTT6welr33O4dHZs+dO0fZsmUByJo1K+F35ru/8MIL/Prrr86LspVyV7lyQcWKkqo9c2aHDq1ZM/m6sUn5jEG8z3AAYrEwp1UnAnv2MGdENipKRqRfeklGpwHKl3d9O5RS6g7tkyh1H4sWybrYDBlgxQqp0WeGs2eJrlkL39BQAK4EBJL9t2343Kd0ny1Z5t1Ls0JDZX9QkJPaNnQoDBwI8+aB2eWAVIo4HMwGBAQQdmeuZIkSJVizZg0Av//+O376j0CldRYLDBkiiQH8/R0+3Ntbkv7aTnX3qSF+dsv7fMvnDIp7f36zdhTo28ucQPbcOahbF2bMkG+Sa9dc3wallLqL9kmUSsb8+dC2LWTMKNOMa9c2px3nzxNVsxa+p08BEF6gENm2b8PnPoH1/ZJl2vb17fsQU46PHoWtW+Xv+fJJPVmXloFQzuRwMNuqVSvWrVsHQO/evRkwYAAlS5akc+fOvPbaa05voFJu4Ycf4IMP4u+imTLZfejd6z1atJAlpoULJ/5cQAAsXAjnz8ORPmP5lg/j3lvUuBW5P+hnTiD7xx/w9NOSzr9zZ8nyl4JAXimlnE37JEolISREElNmzSrVBmrUMKcdly5xq1Zt/P45DsC1fPnJ8ts2fAIC7nvYg5JlGgacOSOfc9iff8o0uaZN4cKFFJxAuRuHsxl//fXXcX9v3749jzzyCNu3b6dkyZI0a9bMqY1Tyi1MnQq9e0OhQlJPNX9+uw8NCpKniwlvygEBMjp78qTciMPCSFwfdvp0Hvu+V9znf633PFn/97E5gezixTKtOCoKvvsO+vXTDH9KKbehfRKlklCtmmQsfvNNqFTJnDZcucKt2nXI+PcRACJz5yHTtq34FCnywEPtTZbpcFLNHTugSRO4elUSYt1nmrPyHA9dZ7ZatWpUq1bNGW1Ryv3MmwfdukHevJLhzsFAtk2be6fJ2NZ7LFiQRIm3n3+G11+P2wx+tgG+nw80L2txZKRkPlywQJ5iKqWUG9M+iUrX9u2DcuXke3vSJPPaERHBzTp1yXTwAADXc+Yi49Yt+JQoYdfh9i7tdWgJ8IYNUk7w1i2YPdvukorK/VkMI6kZ6cmbOXPmfd/v3LnzQzUoNUVERODv7094eDjZzaxnpTzDkiXw4ouQLZvcBB1IdmS1QtGiyU+TsVhkhPbEiQTLNJYtk+g2JgaAzVVrETVsGA1rVX2oH8NhMTFSR9c2lfriRQnmlcfQe51KL8zuk4wdO5Zvv/2Wc+fOUa5cOcaMGUOVKsk/fPzll18YMGAAJ0+epGTJknzzzTc0deBBof5uq2R99RV88gnMmgUvv2xeOyIjuVGnLpl37wLgpn8OMmzdgs+TT9p9ClsfKjQ06XWzSfah7mfDBhmRBfjlFwlqlduz+37naJrkHDlyJHplyZLFsFgshp+fn5EzZ84UJl92DU1pr+x28KCkss+a1TB++83hw+0twbNhw50DgoPlenfe2F7hGWPNxu3O/InsExFhGE2bGkbz5oYRE+P66yun0HudSi/M7JPMmzfP8PX1NaZOnWocOHDA6N69u5EjRw7jfDLlRrZu3Wp4e3sbw4YNMw4ePGh8+umnRoYMGYz9+/fbfU393Vb3iI01jE8+kf5DsWKGceKEeW25ft2IrPpMXF/mZrZsxu0UlvBbuNAwLBZ5Jew32fY5VJ7nwgXDePppw1i3LkVtUeZItdI8V65cSfSKjIzkyJEjPPvss8ydOzelwbdS7qVUKejfH5Yvh6r2j4zakj0tXGjf58PCkMzILVpAdDQAu8tW4tqIETSs7eKi5qGhUKuWpO+3WmWdrFJKuTEz+yQjRoyge/fudO3alSeeeIIJEyaQOXNmpk6dmuTnR48eTePGjfnggw8oXbo0gwcPpmLFivzwww+p2k6VhhmG5LIYMgQef1wScRQtak5bbt0islFjsuz4DYCoLFnwWb8enxSW8GvdOvlkmUku00rK+fPyZ968sl62Xr0UtUW5N4eD2aSULFmSr7/+mnfeeccZp1PKPGfOyJeDxQKff+5QKvugIPkOqVtXkh/b47GIXbIW9cYNAP4sVZYro7+nYR0Xr/nat0+C9r17oWdPSfzkYA1dpZRyB67ok0RHR7N7924aNGgQt8/Ly4sGDRqwffv2JI/Zvn17os8DNGrUKNnPA0RFRREREZHopRQg9d7fegtGjYKyZWHTpnsjP1eJjuZa0+fJukXSC0dnyoR3cDA+lSs/1Glbt5ZkmRs2wJw58ueJE3YGsqNGQcmS8Pvvsq3JK9MspwSzAD4+Ppw9e9ZZp1PK9f74Q74QPvrI4UOTK+6dHIsFGuTfT8X/NYqr2Xro0VJc+GEcDepWd/j6D2XlSnj2WTh7FkaOhDFjJHmEUkp5qNTuk1y6dAmr1Ur+u5IC5s+fn3PnziV5zLlz5xz6PMDQoUPx9/ePewUGBj5841XaYBjw33+SrXjDBocSVDrV7dtENGtBtg3rZdMvI16rVuHjpERs3t5Qp47ka6pTx841sl99Be++K2UEtZRgmudwj3Xp0qWJtg3DICwsjB9++IEaZtWxUuph7d8Pzz0ngaWDaezvV9w7KRYLPGYcYdmtBljCLwNwrEgJzo77kQb1n3W05Q8vPFwaHxQELVu6/vpKKZVCab1P0r9/f/r16xe3HRERoQFtehcbC15eEtXNni3Zec1KBma1Et7qRfzXrAIgJoMvXr8ux7tWLXPaYxgwYIBMuy5WDNatkz9VmuZwMNvyrs6uxWIhb9681KtXj+HDhzurXUq5zuHD0KABXL4M06dD+/YOHf6g4t53q1bgBGuj6pPxshTrPlm4CKd/nET9hi68+RuGrInNmBE6dJC50WY91VVKqRQyq0+SJ08evL29OW9bk3fH+fPnKVCgQJLHFChQwKHPA/j5+eHn5/fwDVZpQ2SkPHRu3x66dwdfX3mZITaWq23bk+PXZQBYfXywLFmMd/365rTHtn541Ch47DEJZAMCzGmLcimHpxnHxsYmelmtVs6dO8ecOXMo6FDBJ6XcwLFjkhDgwgUpoJ2CMg72Fu3u1Qu2zg9lS8b6ZLocCkBo/kKcmDiZeo3qOnzdFLt9W2rZtm4tfwcNZJVSHsmsPomvry+VKlVi3bp1idqybt26ZOvcVqtWLdHnAYKDg7UurrLPlSvQsKEEaWvX2j8dLDUYBldeepkciyTbpdVb6tF728rfmOHaNVizRpaLbd6sgWw6ogvjVPo2f75Eo99/D2+8kaJT2Ntf6lDvAtX/10CyFwDn8+Tj6MQp1G3a4AFHOlFkJLRrJ+tkq1aV7Zw5XXd9pZRKI/r160eXLl2oXLkyVapUYdSoUVy/fp2uXbsCUuO2cOHCDB06FIB33nmH2rVrM3z4cJ5//nnmzZvHrl27mDhxopk/hvIE58/LUqg//4SuXWHiRPMSGhkGl7t0JdfPki081ssL5s3Fu0ULc9pjkz27BPm+vpA7t7ltUS7lcDCbcO3Gg4wYMcLR0yvlWv37SzmaZ1O+VrVmTXkAeL/i3k8WvEz1zxvKlGbgUs7cHPlxCnWaN07xdR124QI8/zzs2gUvvAA//6wZi5VSHs3MPkn79u25ePEiAwcO5Ny5c5QvX55Vq1bFJXk6ffo0Xl7xE+CqV6/OnDlz+PTTT/nf//5HyZIlWbx4MWXKlHFqu1Qac/q0jMj+/bck6BgxQtbMmsEw+K97D3LPmgFArMULY9YsvNu0Mac9t29Djx4yGPHMM/aPLqg0xWIYjs1TqFu3Lnv27OH27ds8/vjjAPz99994e3tTsWLF+BNbLKxfv965rX1IERER+Pv7Ex4eTnazFssr84WGwrx5srbCSU82bdmMIXFAa7FANiOCEyUbkuvoTgCuZM/BX5OmUbNdS6dc2y7Hj0PjxjKtunt3GDdOMxanYXqvU+mFJ/dJUkJ/t9Ohvn1h9GgYOFBKBppYYuZSz97kGSe1Bw2LhdipU/F+9VVzGhMVJWuHlyyRZVMLF5rTDpVq7L3fOdybbdasGdmyZWPGjBnkvDM98cqVK3Tt2pWaNWvy3nvvpbzVSqW2sDBZI/v33/DUU/K00wlsxb3feSdxMqhHC91gW/YXyHVIAtmILNk4OH6SawNZkKlJx4/DoEGS6U/rrSml0gDtk6g0b9gwmQL24oumNuPCu++R704gCxA7frx5geytW/LfY8UKaNQIfvrJnHYot+DwyGzhwoVZs2YNTz75ZKL9f/31F88995xb15rVJ5rp3PnzkrX30CH49FMYPNjpl7BaJbtxWBgUyh1Fre+aYwleA8D1TJn5c9wkqr36ktOvmyzDiA9cDx6EJ55w3bWVafRep9ILT+6TpIT+bqcTGzfKTKpu3cxuCQDnP/4f+b8ZGrdtHT0a7z59nHqNhP2nggUlfk+ypuyNG5LROThYlkz98otUZlBpTqqNzEZERHDx4sV79l+8eJFr1645ejqlXOPSJSm/c+gQfPQRfPFFqlzGVtyb27ehbXu4E8je9MvIX6PHuTaQnT8fZs2SqTe+vhrIKqXSHO2TqDRnyRKZPuvlJXkuTF4Hem7g5xRIGMgOG+b0QDYo6N6ZbQEBMru6deu7PtyvnwSyrVrJkjGzShMpt+FwMNuqVSu6du3K8OHDqVKlCgA7duzggw8+oPU9/+KUcgORkRLI/vWX3ASHDk3dabZWK3TpIl9IQFQGX/aP+J6q3buk3jXvNm6c1ALKnl2mVGuCEaVUGqR9EpWmTJ8uo7GZMsHSpaYHsmFffkXBwYPitq1ffIH3Bx888LjkRlmT2r9kieQcuXueaGio7F+w4K6A9vPPIVs2+OoryJDBOT+o8myGg65fv2689dZbhp+fn+Hl5WV4eXkZvr6+xltvvWVERkY6ejqXCg8PNwAjPDzc7KYoV4qNNYx+/QyjTx/5e2qyWg3j9dcNQ+7LRrS3j7Fr5A+pe82EYmMN47PP5PoFCxrGvn2uu7ZyG3qvU+mFJ/dJUkJ/t9Ow4cPluztPHsP4/XezW2OEfvOdYbVY4voz1k8+seu4hQsNIyAg7jADZPuDD+7dX7iwYeTOnXhfwpfFYhiBgYYR899Vw/jjj1T+iZW7sfd+5/CaWZvr169z/PhxAEqUKEGWLFmcFmCnFl1rks5cvy6lZyyW+Ed+qTkiaxiSdfD77wGwennx51ffUuEj+0tHPJTYWOjTB8aOhRIlZBpOsWKuubZyK3qvU+mNJ/ZJUkJ/t9OoX36RGvCBgbBmDZQqZWpzQkeNoWC/d/C603eKfe89vL799oF9KFtlh5RFFknLwRVOPf4c2c8dhd27pX+j0oVUWzNrkyVLFp566qmUHq5U6vrvP5la3LQpfPmla7L3fvJJXCAba7FwYOBg1wWyADt3wvjxUL48rFoFd2odKqVUWqd9EuXRWraE3r3h/ffhkUdMbcq/YydQ8L2+8YFs7952BbJWq6x7dWYgm5PLrOE5sh/ZLdOv9QG9SoIWmlRpz+XLUnJn716oUcM11xwyRNbi3nHw4wE89dn/XHNtm2eegUWLoHZt8Pd37bWVUkopZb+bN2HtWmjWTNZ+3nkYbqYzE6dQ8J1eeMfGAhD7xht4jR6dbCCbcA3s+fOJEzg9rFz8RzANqcgezjbrQaEfx0lSLKXuov8qVNpiC2T37IG334YxY1J/VHbUKCn1c8ehdz+gzFeDkv+8M129Ch9+KMXDAZo310BWKaWUcmdXrkh91ObNZUmQGzg9bSYFer6Jj9UKQGyXLniNH59sHyooCIoWlYqHL70E777rvLbk5hLrqE9F9jAzy1vkX6iBrEqejsyqtMM2tXjvXnjzTfjhh9QPZCdNSnQH//ut3pQeMSx1r2lz4YJ8Ge7dK9OSevVyzXWVUkoplTL//gtNmkiFhZdektlUJjs1ex6F3uhGhpgYAGI7dMBrypRkA8jUWBubUAZuk4mb/EAvCs34Hu8MLlgqpjyWPuZQacf06RLYvf22lKZJxUDWaoWDn8zG6NEjbt/xV7vx2DgXTRM6c0Zy2u/dCz17ys+slFJKKfd16BBUrx5fKnDWLNPrpJ78eQEFXutChpjbAMS2aoXXrFlSSycJzl4ba7FA7txSVxbkpOcoSJvCv1Fowfe0flEDWXV/do3MLl261O4TNm/ePMWNUeqh9OsnyQFatUrVQDYoCFa+EcT4/7pguXPjnV+8Ez4vTMQlOfaOHpUR6NOnJenU4MGuSXCllFJuQPskyiP9/Tc8+6wsh/r2W0n2ZLITCxdTsMsr+EVHAxD7/PN4zZsHPvHhwd21Ya1W562NtXVdJk6EFtUucK1xG35rP4qM1StSs2bO5OJppRKxK5ht2bJlom2LxULCij6WBB1p65259kq5xMWL8PPPMjppsdxVWds5Et7Ijx6F3z5byRJLe3yQf+sLCr5Ih39mQVvLvcW9ne36dVmgEhoKw4aBHcXLlVIqLdE+ifJIxYvL93eLFvDKK2a3hhNLlpP/5ZfIGHULgNiGDfFasCDRSHFQkIzCJgxec+Vy/FqBgdChA8ydm/hcAQGSdqT1sxegXj1yHDhA45ZLoE7FFP5UKj2ya5pxbGxs3GvNmjWUL1+elStXcvXqVa5evcqKFSuoWLEiq1atSu32KhXvwgX5YujdG1avTpVL3J3gYP1nGwnyakUGQ9aVLM/3PO3C5mMgnae+fSX4TTVZskjm5B9/1EBWKZUuaZ9EeZQDB+RPHx+pJ+sGgew/v64i30sdyHzrJgCxdergtXgxZMwY9xnbuti7R2EvX7bvGiNHwpw5sGEDnDghz99PnpTthPtb17wI9evLf6d+/eDzz53yM6p0xHDQk08+aYSEhNyzf/PmzUapUqUcPZ1LhYeHG4ARHh5udlPUwzp71jBKlzYMMIz33zeM2FinX2LhQsOwWOQSYBhV2W5c98oUtyM4T33Dm9tx79teGzY4vSmGsWuXYdy8mQonVmmR3utUeuHJfZKU0N9tDxIbaxiffy4dg9mzzW5NnGOr1hoRWbLFdVpiq1c3jGvXEn0mJsYwAgKMe/o39rwsFsMIDJRzPNCFC4ZRpowc+O67qdKXU57L3vudwwmgjh8/To4cOe7Z7+/vz8mTJx8ytFbKDv/+K9n/Dh2Cjz6Sx31OWjNqtcLGjTB7tiREts1cK88egr0bkjlWnmJuyVWDppdWYE1ipn5YmFOaEm/1alln07mzk0+slFKeTfskyi3dvg3duskoY4kS8PTTZrcIgGPrNpKnbWuyXb8GgPH001hWroSsWRN9LiQkZetibV2xUaOSzR+VWI8ekgyrb18YPlzzf6gUcTiYffrpp+nXrx/nz5+P23f+/Hk++OADqlSp4tTGJWXs2LEULVqUjBkzUrVqVXbu3Jnq11Ru5L//JJA9ehQGDoShQ51280s4pfjll2U5LkBpDrLepx7ZrJEA7MzxNA0ur+U2SWcgLFjQKc0RixdLHTqLBbp3d+KJlVLK85ndJ1HqHteuQbNmMHUqVK0K27dDyZJmt4pjG7eQ+8XW+F+LAMCoUAHL6tWQPfs9n7X3ofzd62cDAnAsd8iYMdKPGzFCA1mVYg7XmZ06dSqtWrXikUceITAwEIAzZ85QsmRJFi9e7Oz2JfLzzz/Tr18/JkyYQNWqVRk1ahSNGjXiyJEj5MuXL1WvrdxErlxSn61gQcnk6yTJ1UwrwTE2+dYmZ/RVAPZmf4q6VzcQRcZ7zmGxyI28Zk0nNWruXFlbkykT/Por1KrlpBMrpVTaYGafRKl7XL4s6z/37pVET3PmQObMZreKoyHbyNW6JTnDrwBglC2LZc0ayJkzyc/b+1B+/nwZgbVlOq5Z044R2f/+g7NnoWxZKFwYPv7YgZ9EqXtZDMPxSlGGYRAcHMzhw4cBKF26NA0aNEiUQTA1VK1alaeffpoffvgBkCQQgYGB9O7dm4/t+GWIiIjA39+f8PBwsifxJEq5sUuXpBCZxSIRpxP/rVmtMiJ795SaQE6zw68qBaPOAXAoaymeifyNCPzvOYetOU7LZjx1qkxR8veXacY6wqAcoPc6lZ6Y1Scxg/5uuzmrFdq1g0KFHJhrm7qObttJjuYvkPc/mW5mlCqFZeNGyJ8/2WNs/aLQ0KTrydoe3p844eCPaAv2T52SgP+RRxz5UVQ6Y+/9zuGRWZC098899xy1atXCz8/PJV8Y0dHR7N69m/79+8ft8/LyokGDBmzfvj3JY6KiooiKiorbjoiISPV2qlRw8KDc/N56S6YWO/nfW1JrQwoQxpaMNSh4SwLZY5lL8GzkliQDWUiQXt5ZZXkyZIB8+SSQLVfOSSdVSqm0x4w+iVKJnD4tgZm3N9jqtLrBv8O/d+zCv2WL+EC2RAks69bdN5AF+TFGj5YZa7YxBBuH18XaXL0Kzz0nQWyvXlKvRykncHjNbGxsLIMHD6Zw4cJkzZqVEydOADBgwACmTJni9AbaXLp0CavVSv67fgHz58/PuXPnkjxm6NCh+Pv7x70C9RfH8+zZI2tkz52DvHmdempbsqeFCxPvz80ltmR+lkduSYR7OlMgz97YwmVyA/E38kGD7kov74xANjZW/nzlFSmwroGsUkoly6w+iVJx5syRNbHTp8t2hgzuEcj+vodsLVqQ/6L0kY0iRbCsXy+jxnZo3VpmmxUunHi/w+tiAcLDoVEj2L1bsmt+/71b/DdSaYPDweyXX37J9OnTGTZsGL4JCiuXKVOGyZMnO7VxD6t///6Eh4fHvc6cOWN2k5QjfvsN6tWTaSnTpsnIrJMkTPZ0Z9Y6AP5cJSTrs5S48Q8AZ/0KUuPmVs5TIO4zAQESAA8cCB07Qp06TppJ9M030LIlREfLtk4hU0qp+/KkPolKYwxDkhd16iTrYosXN7tFcY788SeZW7ag4PmzABgBARLIOjitt3XrZGrDOhLIRkRA48awc6csnxo7VgNZ5VQOTzOeOXMmEydOpH79+rz55ptx+8uVKxe3XiU15MmTB29v70QZC0GyFhYoUCDJY/z8/PDz80u1NqlUtHGjZAO8dUvuoO3bO+3UySV7ykIkm7LXpnTEEQAu+uahZlQI/xJI3rxSALxwYTsTHDhq8GCJjosUkTTKdz8KVUopdQ+z+iQqnYuOlrIy06fL9/bKlVC6tNmtAuDI3r/I2KIFAWdlAMcoUECmFj8g2LZaZdnV3cmcvL3loX2KXbsm/ZpXX4UffwQvh8fRlLovh/9FhYaG8uijj96zPzY2ltu3bzulUUnx9fWlUqVKrFu3LtE1161bR7Vq1VLtusoky5fLl8XChU4NZK1WeOedewPZjNxkg39dykX8CcAVnxzUjt7ECUsJLBaYMEEevjptFNbGMGDAAAlkixeHzZs1kFVKKTuZ1SdR6VhkpKz9nD5dSu/s2OE2gezhPw+SoWVLivx7EgAjTx4JZB977L7HJZyt9tJL8mfRorL/oRUuLOWJJk/WQFalCof/VT3xxBOEhITcs3/BggVUqFDBKY1KTr9+/Zg0aRIzZszg0KFDvPXWW1y/fp2uXbum6nWVCYYNg127pMaqEyWV7CkD0QTnasjT4bsAiPDJRt2YDRziiZStDbGXYUhK+i+/lC+azZs1s59SSjnAzD6JSqcyZ4YCBeRB+4YND0ym5CqH/zqMd6tWFD91HAAjZ04sa9fCE0/c9zjbbLW7+0ahobI/RQHtzZvy32ffPtnOm9ctMjurtMnhacYDBw6kS5cuhIaGEhsbS1BQEEeOHGHmzJksX748NdoYp3379ly8eJGBAwdy7tw5ypcvz6pVq+5JCqU81Ny5cOyYjFR6eUkNMie7uxC4NzGszNOEZy9tBeC6d2aei1lDzV7lGfViKk0pttm1C779Vp7orl8vX45KKaXsZmafRKUz//wjM6i8vGDmTMlY7CYjjYcPHIHWrSn5z98AGNmzYwkOfmASyeRmq0F8FcS+faVkrt19oagoaNVKqjFkzw6TJjn2wyjlKCMFNm/ebDRo0MDImzevkSlTJqNGjRrG6tWrU3IqlwoPDzcAIzw83OymqLuNH28YFoth5MxpGGFhqXaZDRsMQ27RhmHBaizP1zRux00vP6MWGw2Qz7nEL78YxvnzLrqYSi/0XqfSE0/tk6SE/m6bZOpUw8iQwTAmTjS7Jfc4ePBv469SZeP6MrFZsxrG9u12HZuwT3S/l919oqgow2jWTA5q3twwoqNT+mMpZff9zqFHSjExMXzxxRcUK1aM4OBgLly4wI0bN9iyZQvPPfdcasTaKj345hvJVJwvH2zalKojlDVrSjZiCwbzC7bj+QsrALht8aFl7GJCLLUJDJTPpQrDgClT4jMWt2kjP7dSSimHmNknuXz5Mp06dSJ79uzkyJGD119/ncjIyPseU6dOHSwWS6JXwqRVyg3FxspyoNdek1HGUqXMblEihw4fI7ptO548vB8AI3NmLCtWwDPPJHuMrSzh3LmQIA3Nfd09qy1JMTGy4HbZMslePH++lClSKpU5FMz6+PgwbNgwYmJiUqs9Kj0xDOjfX74oihSBLVtSZWpxQt7eMHqUwazCL9MmTArMxuBNO2M+ayyNgRQUAreXYUDv3pKafsCAVLiAUkqlH2b2STp16sSBAwcIDg5m+fLlbN68mTfeeOOBx3Xv3p2wsLC417Bhw1zQWpUiN25A27bywL1UKUn0lGpPuh136MhxbrbvSLkDewEw/PywLF163zbenejpyy/tu1bBgnZ86OOPJWlnvXpyIa0molzE4TWz9evXZ9OmTRQtWjQVmqPSlaAg+Ppr+ZIIDpYhUxcot/wNSoTOASAWC12YwWJaERgggWyqJHuKjYVevWD8eChfHj78MBUuopRS6YsZfZJDhw6xatUqfv/9dypXrgzAmDFjaNq0Kd999x2FChVK9tjMmTMnW05QuZGICKhfX3Jb1K8Pv/wCOXOa3ao4h/7+h2svvUyVPyVxpeHri2XRImlrMpIrS3g/Fot0zeyK4fv0kRI848ZBpkz2X0Sph+RwMNukSRM+/vhj9u/fT6VKlciSJUui95s7OfusSsNatZLHgm+8IZnuXODvt3vz2PTJcdtH35/ICxU70b1gKiZ7io2Fnj2lvk/FihK458qVChdSSqn0xYw+yfbt28mRI0dcIAvQoEEDvLy82LFjB61atUr22NmzZ/PTTz9RoEABmjVrxoABA8icOXOyn4+KiiIqKipuOyIiwjk/hLq/bNkkOWPFivDDD241XfbQ0RNceaUL1f/4DQDDxwfL/PnQpEncZ+6uGVu9evKJnpJjscif952tZhhw9qyU33nkEZgxI2U/lFIPweFg9u233wZgxIgR97xnsViwWq0P3yqVdt28CYsWyfwWLy/45BOXXfpQvw8pPf6H+B2jRvH4O914PLUv3Lu3BLKVKkkg60ZPd5VSypOZ0Sc5d+4c+e7KdeDj40OuXLk4d+5csse99NJLFClShEKFCvHnn3/y0UcfceTIEYLuU/tk6NChDBo0yGltVw/w22+y3tRigalTJYqzRXVu4NDRE1zs8hq1dm4BwPDywjJnjqQbviMoSALXhKV28uSBS5ccu1bAg2arGQa89x7MmiUlisqUcewCSjmJw8FsbGxsarRDpQdXr0rd2JAQecrZtq3LLv3X/wZSZuS38TuGDJG7vStUqACVK8OaNRrIKqWUEzmzT/Lxxx/zzTff3Pczhw4dSvH5E66pLVu2LAULFqR+/focP36cEiVKJHlM//796devX9x2REQEgYGBKW6DSkZMDHz0EYwYARMnQvfuUnrHjRw6eoKw19+g3vaNABgWC5aZMxP1pZKbSmxvIPvpp1KWtqA9s9UGDoSRIyWItWtRrVKp46F+U2/dukXGjBmd1RaVlp07J9nt9u2TUdkETxFT25+DvqLM1wmyHPzvf/JKTYYh83x8fCTh06uvut0Xo1JKpSUP2yd57733ePXVV+/7meLFi1OgQAEuXLiQaH9MTAyXL192aD1s1apVATh27Fiywayfnx9+mkgndV2+DB06yMypJ5+UBEZu5tCxk5zp0ZPnQtbG7bNMngydOsVt369mrL3q14c6dez44NChskzsscdg7VrInTvlF1XqITlc7dlqtTJ48GAKFy5M1qxZ+eeffwAYMGAAU6ZMcXoDVRpw/DjUqCGBbJ8+MiXF19cll97z9XCe/GIAXra7e58+9qfvSynDkOt06AC3b8s+DWSVUsrpnNknyZs3L6VKlbrvy9fXl2rVqnH16lV2794dd+z69euJjY2NC1DtsXfvXgAK6qiWeQ4cgCpVJJBt2RK2b4dkHiyY5dCxk5x4uw/PbVgZv3P8eCkXlEBISOKpxY6wWLC/LOHo0TIgUKyY1PbJnz9lF1XKSRwOZocMGcL06dMZNmwYvgkCkjJlyjB58uT7HKnSpdBQCWT/+QcGD5YFGF4O/7NLkd0jf6Dspx/hbZuG1q2bXD81178YBvTtKwkjjh2DB9QdVEoplXJm9ElKly5N48aN6d69Ozt37mTr1q306tWLDh06xGUyDg0NpVSpUuzcuROA48ePM3jwYHbv3s3JkydZunQpnTt3platWjz11FOp0k71AP/8I+tjjx+Hzz6TsjLZspndqkQOHTvJ0T79aBq8LH7nqFGQRH1iu2rBJsGuRE8216/LBwsXlkDWRVUolLovw0ElSpQw1q5daxiGYWTNmtU4fvy4YRiGcejQISNHjhyOns6lwsPDDcAIDw83uynpR2ysYfToYRjjx7v0sjt/+NG4lcHXMCS8NM7Vf8mY+1OMsWGDYcTEpNJFY2MN49135ZrlyhnGpUupdCGl7k/vdSq9MKtP8t9//xkdO3Y0smbNamTPnt3o2rWrce3atbj3T5w4YQDGhg0bDMMwjNOnTxu1atUycuXKZfj5+RmPPvqo8cEHHzj8O6q/204UG2sYvXoZRlCQ2S1J0sGjJ4yFzdrF9WMMMIxvvol7PybGMDZsMIw5c+TPtWsTfzS5V968ibcDAw1j4UIHGnb6tGEcPuzsH1epe9h7v3N47mNoaCiPPvroPftjY2O5bZtSqdSBA7L2xGKRTL4uYrXCksEzaDq0D363owFYlbElzdZNJ2adPHIMCJBZMk6tJ2sYUjDclgxB15AopVSqM6tPkitXLubMmZPs+0WLFsVIsHgxMDCQTZs2pVp7lJ2uXYOffpKRTYsFxowxu0VJOnTsJPs/+IR2y+bH7xw0KK5GfVIZiwsXlm7H5ctJr5u11Yw9dgy2bYsv22NXWcJly2T69RNPyHxkpdyIw/M9n3jiCUJCQu7Zv2DBAipUqOCURikP9+OP8NRTkhXQhYKC4M26c2g07G0yRktdvlU0osWtecQQXyMuNFSy/d2nGoLjdu6EYcPkRr9uneTBV0oplaq0T6Lsdvw4VKsGb7/t5A6Acx06dpK9H39Gm8Vz43f27w8DBgDxGYvvXh979iz8958Esnevpko4ldjXV5I8dewofz4wkF29Gl58EZo1i88DopQbcXhkduDAgXTp0oXQ0FBiY2MJCgriyJEjzJw5k+XLl6dGG5WnMAxZdzJ4MOTLB7Vru+zSQUEw56sFTD38Jllu3gBgE7VoTRDRJM4EabvR9+0rSZUfeCO3R9WqMHeufDPcVX9QKaVU6tA+ibLLihXw8stw5Qq8+65LKyo44tCxk+z65As6Bc3CCxlePdO2H1vKDKHgJgvVqyefsdjWt8mVCzJlShzsPrBmbHK2bIFWrSSJ5fTpUlZRKXeTkjnMmzdvNho0aGDkzZvXyJQpk1GjRg1j9erVKTmVS+lak1QUHW0YXbvKAoySJQ3jzrolV4iJMYxOdRcZl7PniFsE8htVjKxEPHDtyJ3lTCm3aJFh3L7thJ9CKefRe51KTzy1T5IS+rvtoNu3DaN/f/nCz5jRMGbMMLtFyTp49IQxrWM3I8biFddJmZalpwGxcX2WPHnsWxe7dm3i9bQpyhWya5dhZM9uGBkyGMbKlU7+aZV6sFRbMwtQs2ZNgoODnRlTK08WFSVP7laulBT3y5dD3rwuu/zcMcsZvacbOSOuArCXcjRmFZE8OCthSrP/AfDdd/DBB/KYdNSohziRUkqplNI+iUrWihVSE7VkSViwQJZAuaFDx06y7Ytv6PLzNLwNqcAwmW68cf17IH7O8KVL9p3vwgWZRpxiBw9Co0ZSkWH+fGjc+CFOplTqck2NFJW2+fpCkSLwwguwfr1LA9mNS1fRcMjr5L76HwCHKMVzrOEqOe06PsXl/caMkUD2kUdkypJSSiml3INtHm6zZlKTddcutw5kQ4Z8R+c5k/GJtQKwIPMr9GACRgq76Q9dujhjRvD3h6lTZb2sUm7MYhhJzbxPLGfOnFjsrM15+fLlh25UaomIiMDf35/w8HCyZ89udnM834ULErhaLJJG2DBkXYWLbPg1mMdef5XC588CcJzi1GIzZyn8wGNtWf1OnEjBmtmJE6FHDyhUCDZvdrsC60rpvU6lZWmlT5IS+rv9ALGx8M03cOgQzJiRunXlneDQsZNs+HoU3WaMwzdGkitdqNOOQhtnY3U8rc3D9W3udvOmLL5VyiT23u/s+k0ZlWAK5X///ceXX35Jo0aNqFatGgDbt29n9erVDLiTaU2lAzt2yBPPd9+VLHtOyaJkv/WrN1CiR7e4QPasdwANrOvsDmTBzgLhd5sxQ1L6588vo9AayCqllEtpn0Ql6fJl6NwZfv1VZk1duCDf1W7q0LGTrBs2hm6zJsQFsrRsyfrWP2HdmLJAFlLYtwGZw9y+vdQuLFNGA1nlORxdjNu6dWtjzJgx9+wfM2aM0aJFC0dP51KaOMFJFiyQRAre3oYxaZLLL792zSbjaJES8ZkO8uc3Vo85YlgshmGxPDgxgsMFwhMaO1Yqju/f79SfSSln0nudSi88uU+SEvq7nYwdOwyjSBH5km/a1DAuXTK7Rfd18OgJY9SbHxg3/DLGdU4uVW1qxFy/ZWzYYF+Sp7x5ndi3CQ83jEqV5ESDBjnzR1Uqxey939k1zTihrFmzsnfv3nuKlB87dozy5csTGRnptEDb2XR6zkMyDBg+XIp2Z80Kv/wiCQJcaO26LRTu0Z3Sxw8DEJU1F3+O3kjFLmVZsuTeIuKBgdLkvHkdLBB+P1evQo4cD/NjKJWq9F6n0gtP7pOkhP5uJ+HHH6F3b1nuNHgwfPwxeLlvSphDx06yauQE3pg6hiy3pJRgMA1oxjLyBmRkxAjo1w9CQ5MuwWObSnzsGGzb5oS+zc2b0KQJbNokS6jGj3f76dkqfbD3fufwb3vu3LlZsmTJPfuXLFlC7ty5HT2d8hSxsfDWW5L0KCAAtm51fSC7YRv5er0dF8hGkI0akaup8npZihaVz5w8CRs2wJw58ueJE9C2rYMFwu+2ahV07RpfLFwDWaWUcgvaJ1H4+Ehx1bVr4X//c/tA9tfRk+g+/Ye4QHYTtWjBEqLISGiozPS1ZSK+O6ZMOJXY1/ch+zYg/Zp27SSQbd8exo7VQFZ5HIcn5Q8aNIhu3bqxceNGqlatCsCOHTtYtWoVkyZNcnoDlZvw8gI/P6hYEZYtk+RHLmC1QkgI/PHndmpP6s1Th/cDcJ3MNGUFu6kMyBPMNm0k877DRcHvZ9MmKTtkscj6YDfNhqiUUumR9knSqQMHpIpC1qzw2muScdfNHzQfOnaSZWOm8Oa0MWS9cR2AbVTjBZZzk8yAjMRaLDBvnlTEeffdxLPNAgIkkHVaP+eDD6ScYtOmMHOmy/OfKOUMDk8zBvmi+P777zl06BAApUuXpk+fPnFfJO5Kp+ekwKVLkDt3fMbiW7cgSxaXXDooSKYNFyr6G6Ovv8sze34D4BZ+vMBy1tEg0eedmsUP4LffoGFDiI6WAP6555xwUqVSn97rVHriqX2SlEj3v9uGIYkY335bHjTPnm12i+xy6NhJloybxpuTR5PjWjgAv1OZBqwlAv8kj9mwQaYOh4Q4cZnU3U6cgM8/l6nFmTM78cRKPTynZjO2uX37Nj169GDAgAHM9pAbiHoIf/whGYvfeAM++0zuoC4MZNu0gco1dvDt7Y/iAtnb+NCWX+4JZEG+486ckRt/nToP2YC9e2UNyc2bsHChBrJKKeVmtE+Szly+LMud5s+HnDnj5+K6uUPHThI0YSZvTR0TF8jupRyNWJ1sIAsSwHp7O6E/k5TLl2VqdrFi8nBAKQ/m0MKCDBkysHDhwtRqi3InS5dCrVpw7pzc8FzIapUR2crP7mQIn1Jrx2bZjxedmM1ymt33+LCwh2zAlSsSvIaHw08/QYsWD3lCpZRSzqZ9knRk9WooW1YC2Zo1Yc8eeOEFs1v1QIeOnWTBxJ/oMfV7coVfAeB60SdpSDBXuH/fqmDBVGrUyJHwxBOwf38qXUAp13J4lXzLli1ZvHhxKjRFuQXDgKFDoWVL2V68WLIEulBICBQstpPPMnxBwy1r4/a/zhR+od0Dj3/oL4CcOWXazeTJ0KHDQ55MKaVUatE+STpw6ZKsib10CYYNk/m3RYqY3aoHOnTsJL9MmkOPqd+T58p/svOxx8i4ZS0ZA/Imm2fJYpFKDDVrpkKjZsyQVMkZMoB/8qPCSnkShxNAlSxZki+++IKtW7dSqVIlstw17bRPnz5Oa5xysdhYKTg+e7Z8USxd6rKER7ZET2FhsPfQTvpnHsrzq3+Ne/9txjKDV+97Dtua2RR/AVy6JIGst7esx1FKKeXWtE+ShkVFSeLJPHlg2jR4/HGPScJ46NhJfp4ylzenjSHffxcBuFmoOL7B6/EuXIDRo2UplcWSuPxOwmzFTs/FtGwZvP665EFZswYeecTJF1DKHA4ngCpWrFjyJ7NY+Oeffx66Uakl3SdOsMeHH8L27bJONF8+l1zSlujp33/h6Zo7eS/HCNov+znu/Q8Yxnd8cN9z2L4AUpzN+NIlmVb91FMSzGtGP+XB9F6n0gtP7pOkRLr43Y6Jga++khp7u3ZJxmIPcujYSeZO/Zk3po0h4FwoAKd4hFpsJjagCKNHSz8lYd/HJjDQydmKbTZvlnKK3t4ysv30006+gFLOlyoJoABOnDjxUA1TbujwYXjsMSm/M3SoDJP6+rrk0rZET4YhgWzvvGNoGzQ/7v1BDEwykPX2lmbaPFS6+vBwuckfOgTNm7t1jTqllFLxtE+Sxvz9N7zyCuzcKTPETp+W9Z1uKOGMMlum4b9PnGTO9F/oPmNsXCAbSiHqsZ7TFMFyVxnBFi1SOVuxraFvvSWz75Yt00BWpTkOB7M2ly5dAiBPnjxOa4wywZw5UqPtgw9g8GC5i7poVNKW6MkWyL5Z6Edemj8HL2SywHe8x+d8nugY2wjs3LmQN68TvgBu3JAkEn/8ITf7oUO1YLhSSnkY7ZN4OMOACRPgvfekisCrr8Lo0eCmo89JjapWeuYkDest4I2Z43jk7BkAzpGfeqznH0oA8XVk+/aVQDbVshUn5O0NK1bAX39Bg3srQSjl6Rwagrp69So9e/YkT5485M+fn/z585MnTx569erF1atXU6mJKlXExkL//tCpE2TKlEqZBu4vJCR+avHrj0yl8y8z8DZiARjPm3zAt0DiwDIgQJ5otm0rXwAdO8qfKQpko6Lk0eiWLfDyy/DDDxrIKqWUh9A+SRry3nuSqyJLFokUp01z60C2TZvEgWyRR09Sq3YQr8+eQLF/TwJwkTzUZx1/83ii4xOWEUxVZ8/C0aN3GlgEnn8+lS+olDnsHpm9fPky1apVIzQ0lE6dOlG6dGkADh48yPTp01m3bh3btm0jZ86cqdZY5SQRERK8LVsmCRWWLpVpxi4WFiaBbOfiM+k6eyo+sTJveCav0JOx2ALZTz+VWUZOn4KzapWk+2/ZUr44dXqxUkp5BO2TpDGvvQanTsHYsVCggNmtSVbCGWU2RR49Sas2i+gxfyKPnjoOwGVy0pBgDvJksud66DKC93P1KjRuLOUV//zTrf+bKvWw7A5mv/jiC3x9fTl+/Dj58+e/573nnnuOL774gpEjRzq9kcqJbt2CGjVkuknjxjJfN0cOU5pyLXonLz02l+4zJ+EbcxuAX2jDa0zFSDBpoH79VJqG06IFLFkiNWV9UjzjXimllItpn8TDhYfLXNtevaBSJShTRhJPujnbjDKbIo+epGXbxXRfMJnH/zkCQDjZacRq9lH+vudKtTqyN29Cs2ZSR/bdd+Gu3w+l0hq7h6IWL17Md999d8+XBkCBAgUYNmwYixYtcmrjVCrImFFucv36wfLlpgWywSE7Cd/+C2/M/hG/29EALOd5OjEb651nLKlSa80w5AvTlj2qeXP5b6KUUspjaJ/Eg23cKJUDpk+HMWPMbo1DEo6mFnn0JC3aLuG1RVN54uhBACLJQhNW8k+up82pIxsTAx06xC+f+u47XT6l0jy7g9mwsDCefDL56RJlypTh3LlzTmmUcrKYGJg4MT6AGzIEhg83rfxMcMhOds9bxFuzxpP51k0A1lGPNizgNpJFOdVqrX39tSx2+d//nHhSpZRSrqR9Eg9065asja1XT9ZzfvklTJ5sdqvuYbVKvD13rvyZsHKCbTS1yKMnadFuCV2WzuCpw/sBuEEmnudXtlOdd96Rz90dR6ZqHVnDgDfflKVjjRvD1Km6fEqlC3b/K8+TJw8nT55M9v0TJ06QK1cuZ7RJOdO5c5K9rkcP+P572WfiU7rgkJ3sWLCMt2aOI+uN6wD893h13i60hCjiR0htiZ6cWmttwgQJYosWJe6bRimllMfRPomHOX0aKleGESMkV8dvv8Enn7jdEp+gIOki1K0LL70kfxYtKvtBRlMrPXOS5u2X8vLyn6h4YA8At/CjOUsJsdQmMFB+tAULoHDhxOdPlb6NzYULsGYNVKkCv/wCGTKkwkWUcj8Ww0i4jD15r732GsePHyc4OBjfu2qQRkVF0ahRI4oXL87UqVNTpaHOkC6KjSe0ZQu0ayfzYtq3h0mTIFs205oTHLKTbYtW0Hvq9+QKvyI7K1aE9euxZvVP3Vpr8+bJN1P+/PLfpUQJJ55cKfeS7u51Kt1JC32SlPDY3+1bt6BCBclR8fXXUkXBzSSse5+Q7fn/ggVQ+qmTTP5pKW2WzaXaH78BEE0GWrGIlZbn4z5nC1aTqkWbqpPi/v1Xlk5piSqVBth7v7M7mP3333+pXLkyfn5+9OzZk1KlSmEYBocOHWLcuHFERUWxa9cuAgMDnfZDOJvHfgk4yjBg5Ej48EO5C48YIUkWTB6RDVmyml7TxpDv8kXZ+eSTMocntW+6q1bJOuGsWWHTJlmro1Qalm7udSrdSgt9kpTwqN/t7dvh8GHo2lW2r1+X0jtuyGqVEdiEyZ0SsligUtWT1Gm6jObL5lPz9y0AxOBNW35hMa0IDJTpw6ky6no/ixdDqVLyUioNsfd+Z/f8joCAALZv387bb79N//79scXAFouFhg0b8sMPP6S5Lw2PtWuXrEspXBjmz4fq1U1tTnDITjYuX0vPWePjA9mSJWHtWtc8Pfz7b/D1hV9/1UBWKaXSAO2TuLHr12We7fffywhs8+aQO7fbBrJwb5biuz1S4iTP1F9G05VBcYGs4eXFkU9m0650K95xxahrUoKDZQZekSJw6JDbTdtWyhXsHplN6MqVKxy9U4j50Ucf9Zh1KR71RDMlDCN+9HXGDGjSBPLlM7VJwSE7Wb9iPW/PGEdg2BnZWaSIfHO4sqNx9iwUKuS66yllojR/r1MqATP6JEOGDOHXX39l7969+Pr6cvXq1QceYxgGn332GZMmTeLq1avUqFGD8ePHU7JkSbuv6/a/22vXQvfucPIklC4tSYieecbsVj3Q3LmyEikpRR49SbMOy2m2aSnPhQTLTotF+lmvvOK6Rt7tjz+gdm1J8rlmTSqlR1bKPPbe71KU5ixnzpxUqVKFKlWqeEwgm+bNmQMtW8an3evSxS0C2bWrNtJ99sT4QLZgQVi3LvUD2VOnZJp1TIxsayCrlFJpkhl9kujoaNq2bctbb71l9zHDhg3j+++/Z8KECezYsYMsWbLQqFEjbt26lYotdZHYWAliGzaUIc5PP4U9ezwikIXka77aAtkmW1fEB7IgFSLMDGT/+UcGLG7ckEhcA1mVjul8BE9386ZMKR4/Hvz9ZZpJmTJmt4rgkJ2sWb2Z7nOnUPzMCdmZJw/W1WsJOVOCsJ2pmAzhwgVJMvH333KBZs2cfAGllFLp2aBBgwCYPn26XZ83DINRo0bx6aef0qJFCwBmzpxJ/vz5Wbx4MR06dEjyuKioKKKiouK2IyIiHq7hqcXLS14VK8pobLlyZrfIITVrSqbh0ND4BFC2QPa539bQdMPK+A//8AN062ZOQwEuXpTSOxcuSJWGli3Na4tSbkALUHmyPXugUiUJZMuVk7WybhLIrl4Twqu/TOexE3/Lzhw5WPfRGoo2fSLZlPdOce0aNG0qgeyAARrIKqWUMt2JEyc4d+4cDRo0iNvn7+9P1apV2b59e7LHDR06FH9//7iXW60DvnBB6tbbor+RI2HHDo8LZEEeqo8eLX+3WOID2bq71tNs7bL4Dw4fDj17mtNIm/BwuH1b+jg9epjbFqXcgAaznmrCBKhaVUZi+/WTmm2PPmp2qwgO2cmq4C28HPQTT/59QHZmycKGj1bS8MMK9yRYCA2VVPhOCWijoqBVK9i9WwqH33lyrpRSSpnp3LlzAOTPnz/R/vz588e9l5T+/fsTHh4e9zpz5kyqttMuhgGzZ8MTT8h04kWLZH/mzB6dgKh1aymrU7GqBLI1926m9apF8R/46ivpb5nt0Ueln6N9HKUADWY9V9GiUjN17Vp5Upgxo9ktIjhkJyvXbqX90nmUP7hXdmbMiHXJcjqPfeae2m0Q/0C3b9/45b4pYhiyTnjdOnjxRZkGZGIpIqWUUp7l448/xmKx3Pd1+PBhl7bJz8+P7NmzJ3qZ6t9/ZcbTyy/LA+QffkhT01xLP3WSus8vp9r+bbRb/kv8GwMHQv/+5jXMMCRD9MGDsp0rl/ZxlLrDcx+hpUc//wzPPisldxo3lqm0blJ4PDhkJyvWbePFFQupsu932ZkhAwQFEeJd574p7w0DzpyRBMd16qSwARaLJJq4eBF++smE/PhKKaU82Xvvvcerr756388UL148RecuUKAAAOfPn6dggmxD58+fp3z58ik6p8vNmQNvvQUREdCoEfz4o1Qn8ABWq/QxwsKSz9dx6NhJps5bzlN7dtJx6bz4Nz78ED7/3KXtvcfnn8vI8B9/wMqVD/y4UumJBrOe4OpV6NVLpvW0ahU/J9fNAtlmwUt5dtdW2entLcF3kyaEzbXvPGFhKWyArSRR377Qu7cGskoppRyWN29e8ubNmyrnLlasGAUKFGDdunVxwWtERAQ7duxwKCOyqTJmlO/X6dOhc2ePGRkMCoJ33klcRzYgQNbItm4t27ZA9on9f9Bp8Wwstmlj77wDX39t7s/644/wxRdQsiTMnGleO5RyUzrN2N1t3izJFGbPhho1ZEqxGwkO2cmK9dtptGk19bZtkJ22+mutWgHJp7y/m72fS2TyZOjaVZIhgAaySimlUt3p06fZu3cvp0+fxmq1snfvXvbu3UtkZGTcZ0qVKsWiO+tJLRYLffv25csvv2Tp0qXs37+fzp07U6hQIVq66zRdqxW+/x7On5ft1q3h+HFZ0uNBgWybNtw3X4ctkH384D66LJyJV2ysfOjNNyWplZk/67Jl8Pbbsqxs1SpIpYctSnkyHZl1V9HR8Nln8M03EqB9+SV89JFbJVewBbJ1tq6n8cZVcftjx//I5sKdCJsrAWr16vemvE/IYpH3HS6TtnSpZPLLnRvOnvWY6U5KKaU828CBA5kxY0bcdoUKFQDYsGEDde6slzly5Ajh4eFxn/nwww+5fv06b7zxBlevXuXZZ59l1apVZHSDnBf3OHAAXnsNdu6E/fth0iTZnzOnue1ygNUqA6vJ5euwWGDotyep8/xyShz5i66/TMfLlryja1cYO9bcQHbnTmjfXmbhrVgBKZzirlRaZzGMpH7N06aIiAj8/f0JDw83P4nCg5w7J2V2cuWSUdmnnza7RYnYAtkaO7fQZsWCuP37uo7kheC+90zn6dgRvvtOthP+i7N9TyxYED/dxy7btkH9+hLcb9gAlSun/IdRKo3xqHudUspuqf67HR0t02q//FJmPHXuLKOTuXI5/1qpbONGKQGYHFv5nVKnD9Fj7mR8bkfLGy+9JNN5zZ7pdfSoJNsaORKaNDG3LUqZwN77nfsM8ymJ8k6dkkzFBQrA6tVQqhRkyWJ2yxKxBbJP79mRKJA90PFLKkzve89T0NBQCWTffx/mzr133cqoUQ4GsocPyw0+JkZGZzWQVUoppR7OwYPQoYOMxAYGylpNDw6i7peHwxbIlgw9Sve5U+ID2TZtZJmU2YEsyBrZ/fslmaZSKlm6ZtZdnD8PL7wAVarEr0+pVMltA9nyf+2h47Kf4/bHftSfxiGf3Lf8zrx5stxmwwZJirhhA5w44WAgGxkpmZwvX4Zp06Bhw4f7gZRSSikF2bPD6dOyRvPAAY8OZCH5PBy2QLb4ueN0mzUJ39tR8kbz5tI5MXM5182b0LYt/PmnbGsgq9QD6cisO1i0SNZ+XrwogZqbsgWyTx75i1cW/YTFliShTx82NxrCv98kf6yt/M62bQ9Rfgcga1b44AO4cUPq3CmllFIqZTZvlunE9evLVKljxyBPHrNb5RQ1a96br8MWyBa9cJLuMyaROeamvNG4Mcyfb27waLVCp07SJ8ybF8aNM68tSnkQjxmZHTJkCNWrVydz5szkyJHD7OY4x8WLMqWndWu4dk2Kj69YIVnr3IwtkH3s+BFeWzAjLknC8TqvsbHFSELP2pckIcXld27fBlvw3LOnBLRKKaWUclxEhIzA1q4Nr78eXxHAzQNZq1XWws6dK3/a8jUlxdtbyu+A5OewBbKB/52h+/SJZL19Xd6sV0/SGvv5pXbzk2cY0K+fBLINGsj6K6WUXTxmZDY6Opq2bdtSrVo1pkyZYnZznKNPH6nF+uyzMHWqrI9wA3cXF4+27GTlxu0UO/UPb/wyDa87X3pz6MgrGycSu9HL7u+/FJXfiY2VJBReXjK12Nc3BSdRSimlFCtXymywM2egbFnpf3jAdFZ76sXerXVrSTD51bcnqdZgOYWuhPLG1B/JHn1NPvDss5J7I1Om1P8B7mfkSCmD9NRT0mDt5yhlN48JZgcNGgTA9OnTzW3Iw7p2DbJlk79/843UrenZUwI1N3D3l8XTNXdSo/52AkNP8/bPk/G+dQuAxbSgCzOIRZIkXLp0//OmuPwOwIcfyoLb2rXjR2eVUkopZb8bN+CttyRTb4YMMGgQfPyxRwROtnqxSSWYbNPm/hURSj91krrPLyfHhXP0mjWRbFF3yiVVrQq//mp+bpKgIHjvPekkrVgB/v7mtkcpD+MeEVQqiYqKIiIiItHLNIYh82KKFZMsxQCPPAK9e7tVIJuwuLgtkC14/iyvT5uMz40bAKzmOdrzMzEk/ST37rJstu1Ro1KQIPD772H4cHjySVi8GNyxHp9SSinl7jJmhH/+kUSTe/bAwIEeEcg+qF4sQN++SU85PnTsJFPnLSfbpQu8M+dHskVcljcqVoRVqyTpldnKl5fAesUKKFzY7NYo5XHcI4pKJUOHDsXf3z/uFRgYaE5DwsKgVSupXXbrlqyVdTN3f1nYAtm8ly7w+uRJ+MfIg4BN1KIVi4gm+bUld085DghIQR1ZkOi6b18oVEhu8mllrbRSSinlal5esiZz2zZ5QOwhQkISTy2+my3BZEhI4v22QDbr5Uu8O+dHsv53ZwpZ2bKwZo379CmKF4ft26VdSimHmRrMfvzxx1gslvu+Dh8+nOLz9+/fn/Dw8LjXmTNnnNh6OxgGzJolXxpLlki2wL/+csssvAm/LGyBbM4r//H6xMnkvn0FgB1U4QWWc5PM9z3XyJEPWX4HpDGdOkn24hUrZBRbKaWUUimXJ4/La6g6krQpKfYmjkz4OVsgm/nqZfrOnUi2ixfkjdKlYe1ayJ3bsUY42+XLULcu7N4t23dPaVNK2c3UNbPvvfcer7766n0/U7x48RSf38/PDz8zs9MFBUniomzZpPh49+5ue8OyfQnYAln/iKu89uNU8kfLF8BeytGYVUSS7YHnKlz4IcvvgAznfv89FC0K5co95MmUUkop5WopSdp0N3sTR9o+ZwtkM4Zfpe+8yfifu9PBefRRWLcO8uWz/wdIDVFR0LKljCIsWwaVKpnbHqU8nKnBbN68ecmbN6+ZTXA+w4CYGEmu0LKlLOrv08ftRxYLFowPZLNGXqPL+OkE3AoF4BCleI41XCUnefNKsqek1q48VJInm/BwCf69vCT4V0oppZTHeZikTQklVS82oYR9D1sg63stgnfnTyFn6J0ZecWKwfr1KSyp4ESGAa+9JoFshw6yblkp9VA8Zs3s6dOn2bt3L6dPn8ZqtbJ371727t1LZGSk2U2Ld/o0NGkC/fvLtrc3fPed2weyIOV3atTfTuYb13ll/EyK3TwJwHGK04C1XLLkIzAwvoa3U5M82Vy/LvXVOnSIr3mnlFJKKY/yMEmb7nZ3vdiEEvY9/j4hgaxP5DXe/WUquU6flDcDAyWQNStvSkKffSZrsGrUkFKDbpIAVClP5jG/RQMHDqRChQp89tlnREZGUqFCBSpUqMCuXbvMbprcmSdOhDJlJFPx0aOOLwoxUXCI1JH1i7pFx/FzeOz6UQDOEEAD1hJmkex6o0bFP029O+FeipM82Vit0LEj7Nolael9PKZqlFJKKaUSSGnSpuTY6sUm1/co/ZQEst7Xr/PuwhnkOXFcPlCwoEwtLlo0RT+HU82eDYMHy3Rnrc6glNN4TMQwffp096wxe/IkdOsmN8ucOaV+28svu+3a2LsFh+xkxfrtZIiO5qMVP5Pz2gEAzpOPBqzlJMUIDJBA1haotm4NLVrIl1BYmHxX1Kz5ECOyhiGPaJctg8aNYfx4j/nvp5RSSqnEUpK06UGS63vYRmS9bt6g76KZ5Dt2RA7Il0/6ZiVLOv4DpIZnn4XatWHSpHvLPiilUsxjglm3FB4OFSrA1atyhx0/3vz1GA6wBbLeMTF8uDaInPv2AGDkysXpb9fyeabHkw1Uvb2dkOTJZvRo+OEHSfQ0f76OyiqllFIezNGkTfa6u+9hWyNruXWTd5bMpsCRg/JGrlyStbh0accukBoMQx7QFyki6ZyVUk6lUUNK2G5M/v7wv//JOoz27T1qNNEWyHpZrXy4aRl5dv4mb2TLhmX1ap6uXJanXdGQI0ckSVbhwrB8uSR/UkoppZTHciRpU0rZAlkjKop3ls+j0IE/5Q1/fwgOdo+6rRcvQvPmMr2talWzW6NUmuQxa2bdQmysjCLWqhWfoOiDDyRhkQcGspbYWN7fvoZ8IZvkjcyZpaZr5cqua8zjj8P06fDrr/LNppRSSimPZm/SppQuT0oYyPZZOZ+AfX/IG9mySe6SihVTdmJnunVLqlr89hts2GB2a5RKszSYtdfff0sQ27evjCb+/bfZLUoRWyCLYdDvj00UXLta3vDzgyVLZE2HK1y4EJ8k65VXtJasUkoplYY8KGlTShNG2gLZ2Ohoeq4J4pHdO+UN2wN5dxgBNQzJp7JtG3TuDB99ZHaLlEqzdJrxg1it8vjw00/lKVuHDvD99+CB9XETBrLv/rWdgOVL5A0fH/jlFymL4wpXrkgShMcfl280XSOrlFJKpTnOThhpC2Stt2/Tc/1Siu3cJm9kzChJJF31QP5BhgyR7MXPPivVLjxo9p5SnkajiAfp3FlqguXPL3+2amV2i1IkLpAFOu/exyPL5wNgeHlhmT0bmjVzTUOio+Xb7fBh+YbTQFYppZRKs5yVMDJhIPvmpl8psfXOEilfXyl1U6/ew1/EGYKCYMAAKF4cFi2SmW9KqVSjkcSD9OghRa1HjYLcuc1uTYokDGTLzj1ChSPT497r5z+Vmj7tSGl5WIcYBrz5pmTza9sWvvrKFVdVSimllAezBbIxMTH02LaGxzatkzd8fGSGV6NG5jYwoSpVJHofN05L8CjlArpm9kFq1YJZs9JEIBs48xSvHRkf997bjGX01S60aSMPElPdsGEwbZqsZ5kxQx4SKKWUUkolI2Eg223nBkqtXSVveHvDvHmum1lmr4AASfjkDmWBlEoHNJpIwxIFsgsv0O+fkXHvfcAwxvN2XMr8vn3j8zGliiNHpIxRkSKSaCpTplS8mFJKKaU8XcJAtusfW3hy5VJ5w2KBmTPhxRfNbaDNtWtQvz5s2WJ2S5RKdzSYTaMSBrLNzobTd//QuPc+5zO+44O4bcOAM2ckQUOqefxxmDtXasnmz5+KF1JKKaWUp4sLZK1WOv+1g6eWLYx/c8oUeOkl8xqXkNUKHTvC+vWydlcp5VK6ZjYNShjIdvGJ4qnJX+CFDMF+x3sM4rMkjwsLS4XGXLwIOXPKupZ27VLhAkoppZRKSxIGsp0O7abCwnnxb06YAF27mte4u73/Pvz6Kzz/PHzzjdmtUSrd0ZHZNCZhIPtKFig/6FO8YmX+8Dje4gO+BZJOEV+woJMbY5t207y5ZDFWSimllLqPhIFsx2N/UvnnWfFvjh4tiTndxYQJkiC0bFmZfZbSmkNKqRTTYDYNSRjIdsrpS8UBH8Pt2wD8krkzvfmBpAJZiwUCA6X2m9PExEhN3v37oWRJSZ2vlFJKKZWMhIFsu1OHqfLT1Pg3hw2DPn3Ma9zdNm6EXr0gXz6pcZstm9ktUipd0mDWw1mtcj8d/kN8INsxf1Yq/+8DuHVLPtS2LT7Tp2BYvO6p223bHjXKyQ8U338fVqyQaTcjRjjxxEoppZS5hgwZQvXq1cmcOTM5cuSw65hXX30Vi8WS6NW4cePUbagHSRjIvhh6jGrTJsS/+cUX8MEHyR9shsceg+rVZZ1skSJmt0apdEvXzHqwoCB45x0oWGwnNepLIHtzbW7K7+kN16/Lh55/Hn76iVa+Pizwls//+2/8OQICJJBt7cxCsxMnylQgnXajlFIqDYqOjqZt27ZUq1aNKVOm2H1c48aNmTZtWty2n59fajTP4yQMZFtdOEWNyWPj3/zkExgwwLzGJadQIdi0iXtGCZRSLqXBrIcKCoI2baDys/GBbOiyAozd3QtfrsqH6tWTYuJ3pvi2bg0tWkjW4rAwWSNbs6aTY80zZ6B3b512o5RSKs0aNGgQANOnT3foOD8/PwoUKGD356OiooiKiorbjoiIcOh6niBhINv88llqThiNxVY38L33YPBgcxuYkNUKr78OXbpA3boayCrlBjSY9UBWq4ywJgxkTy4PZOzuXuTlEgC7fKvz//buPC7Kcv//+GsAUVTEDVESF7Dc9y0tzYUUK4/mknlMIT2dTmllan01T6KnzDyaWmZpp5NbatpJtJ/lnnuaiWG5UbiLqJkB4YbC/fvjjpGRRVCYm4H38/GYh8x9XzP3576RmetzX1vT5StxL1HC4bXu7tChQz4GFxBgLmJeqZK63YiIiKSzefNmKlWqRLly5ejUqRNvvvkmFSpUyLL8pEmT7IlzYZQ+kX0s8TwdZr2DLTXV3DlsGEyZUrASxrFjYf58+P13M5kVEctpzKwL2rbNsWtxzFc1mb5nBP6Ya+tE0ozOyV+z7YfSzgvqjz/MSZ8AHn8cHnjAeccWEREp4EJCQliwYAEbN25k8uTJbNmyhW7dupGSkpLla8aMGUNCQoL9cerUKSdGnL/SJ7LdLl+k08wp2NKuxTPPmMOVClIi++mn5tI7derAggVWRyMif1LLrAuK3H8zkT28+j7e/f4lanACgP3UpytrScQnf9aNzcyNG9C3r9lkHBEBpZ2YRIuIiOSB0aNHM/k264QeOnSIOnXq3NH7P/nkk/afGzZsSKNGjQgKCmLz5s107tw509cUL168UI6rTZ/Idr2WwMPvTsb25+oLhIaaS964FaD2lu++g7/9DcqVgy+/BB8fqyMSkT8pmXUx67ft5vRvZiK7f009pn03gvv4BYCfuZdgNvAbFYF8WDc2K6NGwdq15nqyJUs66aAiIiJ5Z+TIkYSFhWVbJjAwMM+OFxgYSMWKFYmJickymS2M0ieyD6dcpuuMt7GljQt+8kn4738LViIbG2v2OLtxA5YtM5cbFJECQ8msC0m/juzRbxsyaderNGQ/ACeoRjAbOEdlbDZzluI8XTc2K//5z82Ziz/9tGB9AYmIiOSQr68vvr6+Tjve6dOn+e2336jitDvP1kufyHa2JdNt2kRsV66YOx9/3Oy+W9BWQPDwgBo1YMwYCA62OhoRuYUyDxeRPpF9rE0TPjnxOi2IBOAMVejEN5yiWv6tG5uZzZvh+efB11czF4uISJFx8uRJoqKiOHnyJCkpKURFRREVFUVSUpK9TJ06dYiIiAAgKSmJV155hV27dnH8+HE2btxIjx49qFWrFl27drXqNJwqfSLbsVgKj77zJrb0ywh+9hkUK2ZtkJnx8zOX4Bk2zOpIRCQTapl1AQ6J7APN6Pz2BIj+FoDf3CoSnLqBowQB+bRubGb++AOeeMJsiY2I0MzFIiJSZIwbN4758+fbnzdt2hSATZs20eHPJQOio6NJSEgAwN3dnR9//JH58+cTHx+Pv78/Xbp04Y033iiUY2JvlT6RfaiEje5vv4EtbZmhLl0clhEsMKZPhwYN4OGHC2aSLSIA2AwjbTGvwi8xMREfHx8SEhIoU6aM1eHkSPpE9tF2LQieNgm++srcWbYsKeu/YVtS0/xbNzY7q1ZBfDw89ZSTDigiOeGKn3Uicnuu+LedPpFtV8qDx98Ox/bbb+bODh3MOk1Bm29jxQqz23OtWnDwoJJZEQvk9PNOLbMFmEMi274lwR/OuJnIlioFq1fj3qIpHZwZVGqqOWtxsWLw2GPOPLKIiIi4kPSJ7IPenjw+efzNRPaBB8whSgUtkT14EAYONOtZy5crkRUp4DRmtoBKn8g+0qE1wfPmwOefmztLlDBbRe+/3/mBvfmm2SXowgXnH1vy3alTp+jQoQP16tWjUaNGfJ72f05ERCQX0ieybcuWpNc7b2I7f97c2aoVfP11wVvKLz4eevaEpCRzMqqGDa2OSHJJ9ZiiRy2zBZBDItvxfh5eMu/mAt3FipljVP8ck+NUK1ZAeDgEBhashcwlz3h4eDBjxgyaNGnC2bNnad68OY888gilSpWyOjQREXER6RPZ+yt402fKBGxnzpg7mzSBNWugoHWTTk01h0398guMHeuEyUckP6geU/QomS1gMiSyX35uLh4O5mDYpUshJMT5gR04cLPbzcqVUKGC82OQfFelShX7MhGVK1emYsWKXLx4UV8CIiKSI+kT2da+PjzxzhvYTp40dzZoAOvXQ7ly1gaZmT/+gMREeOQRmDDB6mjkDqkeU/Som3EB4pDIdmrDw9+shnfeMXfabDB/vjkhgbNdvAg9epjdbhYuNL+MxCU99NBD2Gw2bDYbnp6e1K1bl8WLF2daNjIykpSUFAICAvI8jlmzZlGjRg1KlChB69at2b17d7bl//jjD4YPH0716tXx8vKibdu2fP/99w5lUlJSeP3116lZsyZeXl4EBQXxxhtvcOscd7k9toiI5Ez6RLaVXzn6vTsJ29Gj5s7atWHDBqhY0dogs+LjAxs3wpIlBW+tW7ErzPWY8ePH288t7VGnTh2HMlu3bqV79+74+/tjs9lYsWJFXp+ay1EyW0BkSGR3bXG8MzhnDgwY4PzADMM87pEjZhdjK5JpyROGYfDDDz8wdepU4uLiiI6OJiQkhEGDBnHs2DGHshcvXmTQoEF89NFHeR7H0qVLGTFiBOHh4ezdu5fGjRvTtWtXzqeNpcrE3/72N9avX8/ChQv56aef6NKlC8HBwcTGxtrLTJ48mQ8//JD333+fQ4cOMXnyZP79738zc+bMuzq2iIjcXvpEtqV/RZ6cORnbzz+bO4OCzETRz8/aIDNz8KDZ7RnMoVwFrfuz2BX2egxA/fr1iYuLsz+2b9/usP/SpUs0btyYWbNm5fl5uSyjCElISDAAIyEhwepQHKzb+p0xfPwMY/j4Gca6rd8ZxqxZhmGmkeZj+nRrA1yzxjDCwgwjJcXaOOSuREdHG4Cxf/9++7affvrJAIzVq1fbt129etVo166dsWDBgnyJo1WrVsbQoUPtz1NSUgx/f39j0qRJmZa/fPmy4e7ubqxatcphe7NmzYyxY8fanz/66KPG4MGDHcr06tXLGDBgwB0f21UV1M86Ebk7BfVv++Avx4xRb8w0ho+fYSz8eLGR2rjxzTpM9eqGceKE1SFm7vffDePeew3Dzc0wfv7Z6mjkNgp7PSY8PNxo3LhxjuMAjIiIiFzF7kpy+nmnllmLZWiRPXIQhg69WeDNN2H4cGuCS9O1K8ydC2767+LKIiMjKVeuHPXq1QPg9OnTjB07luLFi9OoUSPAvOsZFhZGp06dGDhwYJbv9dZbb1G6dOlsHyfTxkilk5ycTGRkJMHBwfZtbm5uBAcHs3PnzkyPdePGDVJSUihRooTDdi8vL4c7lm3btmXjxo38/GdLwL59+9i+fTvdunW742OLiEj20rfINqtWmQGzp2Hbt8/cec898M03UK2atUFmJv2ET6NHw733Wh2R3EZhr8cA/PLLL/j7+xMYGMiAAQMyjUEcaQIoC2VIZM8cgyFDbhYYPRpee82a4L7/3jz2okVQqZI1MUie2rt3LwkJCXh7e5OSksLVq1fx8vJi9uzZ+Pv7A7Bjxw6WLl1Ko0aN7OMwFi5cSMNblif4xz/+wRNPPJHt8dLeM70LFy6QkpKC3y1dzfz8/Dh8+HCm7+Pt7U2bNm144403qFu3Ln5+fixZsoSdO3dSq1Yte7nRo0eTmJhInTp1cHd3JyUlhYkTJzLgz+75d3JsERHJWvpEtmn1Kjw1Zzq2PXvMnZUrm4lsYKC1QWYlPBy++gq6dYN//cvqaCQHCns9pnXr1sybN4/atWsTFxfHhAkTaNeuHfv378fb2/u216eoUjJrkQyJbPw58w5haqpZ4IUX4K23rFkC5/x5c0r6M2dg/37o1Mn5MUie27t3L0OHDuXFF18kPj6eUaNG8cADDxAWFmYv8+CDD5Ka9n8wG+XLl6d8+fL5GK2jhQsXMnjwYO655x7c3d1p1qwZ/fv3JzIy0l5m2bJlLFq0iMWLF1O/fn2ioqIYPnw4/v7+hIaGOi1WEZGiIH0i26TmPQz8+D1su3aZOytWNMfI3neftUFmZflys+dbrVqweLEmfHIRhb0ek9aTDKBRo0a0bt2a6tWrs2zZMoakb+wSB+o3aoEMiey1ROjbF27cMAsMHgwzZliTyN64Af36wenT8PbbSmQLkb1799K2bVtq1apFixYt+OCDD5g8eTLHjx/P9XvdafecihUr4u7uzrlz5xy2nzt3jsqVK2d5vKCgILZs2UJSUhKnTp1i9+7dXL9+ncB0d/xfeeUVRo8ezZNPPknDhg0ZOHAgL7/8MpMmTbqrY4uIiCOHRDYwgIELZmPbts3cWa6cOWvxn11BC6T586F0aVixAsqWtToayaHCXo+5VdmyZbnvvvuIiYnJ9fkVJUpmnezWRLZTajIp3XvAtWsApPZ7Ej76yLrxqWPGwObN0KcPjBplTQyS544ePUp8fDwN0i2rVK9ePYKCgrKc0j47//jHP4iKisr2kVn3HE9PT5o3b87GjRvt21JTU9m4cSNt2rS57XFLlSpFlSpV+P3331m7di09evSw77t8+TJut/zduLu72+/Q3u2xRUTEMZFtHFSNgUs+xi3tc7VMGVi3Dho3tjbI2/niC9i2DerXtzoSyaGiUI+5VVJSEkeOHLGvmyuZUzdjJ7o1kXXf6cbl0Y/gbVwGYCV/Yfj2Bbyz0p1evSwIcNkymDrVvJv6ySfWtAxLvoiMjKRYsWLcd0uXr86dOxMREcFruRybfTfdc0aMGEFoaCgtWrSgVatWzJgxg0uXLvH000/by7z//vtERETYvyzWrl2LYRjUrl2bmJgYXnnlFerUqePwmu7duzNx4kSqVatG/fr1+eGHH5g2bRqDBw/O1bFFRCRz6RPZRrWqM2jZXNxWrzZ3li5tLnHTooW1QWYlNRW++w7atAEPD2jSxOqIJBeKQj1m1KhRdO/enerVq3PmzBnCw8Nxd3enf//+9jJJSUkOLbXHjh0jKiqK8uXLU60gTrTmBEpmneTWRNa2x4vG/9cBb/4AYC1deIJlXD9TjD594H//w/kJbfnyULOmOZZEA80Llb1793Lvvffi6enpsD04OJjZs2dz+vRpqlat6pRY+vXrx6+//sq4ceM4e/YsTZo0Yc2aNQ6TKVy4cIEjR47YnyckJDBmzBhOnz5N+fLl6d27NxMnTqRYsWL2MjNnzuT111/n+eef5/z58/j7+/Pss88ybty4XB1bREQyckhk761BaMSnuH35pbnTy8ucTKkg93IZP94cJ7tkiTmcSlxKUajHnD59mv79+/Pbb7/h6+vLgw8+yK5du/D19bWX2bNnDx07drQ/HzFiBAChoaHMmzcvH8+64LIZhmFYHYSzJCYm4uPjQ0JCAmWcuCh2hq7FFctysUE7fFPNxZW30o4Q1nCFkoDZIFq1Khw7ZsGcBNevm4uGi4jLsuqzTkTyl1V/2w6J7H01CP3qc9wWLTJ3Fi8Oq1ZBuqVKCpwvv4QePcwJn77/XuNkRVxATj/vNGY2n2WY7CmgEtcf6mxPZL+jFY+xyp7IgrnK+KlT5nCOfJeaCq+/bs5cDEpkRURExC59ItvwvpqEbvjyZiJbrBhERBTsRPbIERg0CEqWNGNVIitSqKibcT7KkMgG3gPt21Pi19MA7KMR3VjNH2R+tyEuzglBvvWW2e3mxAlYsMAJBxQRERFX4JDI1g4kbOtq3D75xNzp4QGff26u01pQXbliTmiZkACffgrpJg8SkcJByWw+yZDI1q4BDz0ER48CcJjaPMx6fifrwef5PnnZmjUwbhzUqGEuBSQiIiJCJonsro24ffihudPNzVyfNZuZWAuEDz6AqCh47jkYMMDqaEQkHyiZzUZKitnVNy7OTCzbtcvZGNYMiWz9WuZ6rYcPA2DUrMnAqxu5cLYSZDJiOW3MbLt2eXk2tzh6FP76V3Osy/Ll5uRPIiIiUuRlSGSjduD27rvmTpvNXKe1b19rg8yJl14CT0/4+9+tjkRE8onGzGZh+XKzwbJjRzPn69jRfL58efavy5DINqlrdsHZt88sULUqto0bGfP+PUDG1W/Sns+YkY+TP12+bE6V/Pvv5pq2TZvm04FERETElTgksnWCCD0cidvkyTcL/Oc/8NRT1gWYE5fNJQ/x8IAXXjBv3ItIoaRkNhPLl5tDLE6fdtweG2tuzyqhzZDINm8Ajz0Gu3ebBSpVgg0boGZNevUyl9+55x7H96ha1QnL8sTEmCczbBgMHJiPBxIRERFXkSGRPfoT7hMm3CwwaxYMGWJdgDkRH2+uIRsebnUkIuIE6mZ8i5QUs1dKZgsWGYbZcjp8uDlMJH3LaYZEtlVjs9DWrWaBcuVg/XqoXdv+ml69zCJ30pX5rjRqBD/8YCbXIiIiUqjlZNhUhkQ29mfcX3vtZoFp0+D5550beG4ZBjz9NPzyS+YVOREpdJTM3mLbtowtsumlXzanQwdzW4ZE9v6m8MQTsHatWcDb2/y5UaMM7+fufvN98t3+/ebYWH9/swlYRERECrXly82b9OnrNlWrwrvv3uwFliGRvXAC95Ejb75g0iR4+WXnBn4npk6FFSvg4YfVMitSRCiZvUVOl8NJK5chkW3b3Oy6u2KFWcDLC77+Glq2zPtgcyM+3mwGvnwZoqPBiYuti4iIiPOlDZu6tZEybdjU//4HdRvdksj+cRb3YcNuFg4Ph9GjnRv4ndi6FcaMMTP1RYuc0M1NRAoCjZm9RU6Xw6lSJZNE9oEW8OyzsGSJWcjTE1auhAcfzKdoc8gwYPBgcwbjZ59VIisiIlLI3W7YFMCkKbckssm/455+5t/Ro12jhfP8eejXzxwLtmwZ+PpaHZGIOIlaZm/Rrp15Uy82NvMvgLRlc5Jtu1mdPpF9sKU5mPa//zULeniYtzwffth5wWfl3XchIgI6d4bXX7c6GhEREclntxs2VS3oOPcHp0tkuYR7WNjNys/w4fDWWxmXXSiIKlQwb9pXrgxt2lgdjYg4kZLZW7i7m7lfnz7m53f6hDbt83zEa7tZvTldItuuFYwdC++9ZxZwc4NPP4Xu3Z0cfSZ27YJXXjGbktXtRkREpEjIbthU9VrH6f7kKjw8UihXOohQz+u4P/EUpKaaBZ57zpzwyRUSWTDrNhMnWh2FiFhA3Ywzkd2yOdM+2M2Jc7cksm+9ZT7S/Pe/ZncXqxmGufxOaip89hn4+VkdkYiIiEs7fvw4Q4YMoWbNmnh5eREUFER4eDjJycnZvu7q1asMHTqUChUqULp0aXr37s25c+fyLc6shk2lT2RjDgXxl2Rw79cPbtwwCwweDO+/7xqJ7IoVZmNCSorVkYiIRdQym4XMls1JtmXSIjtjhvlBmmbWLAgLsyTmDGw2c8zuli3Qvr3V0YiIiLi8w4cPk5qaypw5c6hVqxb79+/nmWee4dKlS0ydOjXL17388st89dVXfP755/j4+DBs2DB69erFjh078iXOzIZN3ZrIem/ypPGKv8D162aBp56Cjz4ye5gVdDExEBpqJuFPPw21alkdkYhYwGYYRWchrsTERHx8fEhISKBMLidByjDZU7tW8J//QPqJEv79b7NLb0Fw9SqUKGF1FCJigbv5rBOR3JsyZQoffvghR48ezXR/QkICvr6+LF68mD59+gBmUly3bl127tzJ/fffn6Pj5PZvO202YzDHyKZPZJM+L8V6j0fxSL5iFujbFxYvNuf8KOiuXDHHxu7bZw6h+utfrY5IRPJYTj/vXODW25136ckrmSayixaZMwOnGTeu4CSyW7ZAYCBs3mx1JCIiIoVeQkIC5cuXz3J/ZGQk169fJzg42L6tTp06VKtWjZ07d2b5umvXrpGYmOjwyI20YVPNWjsmsu5byrHO8y83E9kePcx6jSsksgBDh5qJ7PPPK5EVKeJcIplN36XnwIEDTJ8+ndmzZ/Paa6/l+7EzTWQjIsyuLWmN2iNHwvjx+R5Ljpw7B/37w4UL5hq3IiIikm9iYmKYOXMmz6a/wX2Ls2fP4unpSdmyZR22+/n5cfbs2SxfN2nSJHx8fOyPgICAXMdXt9FxOj56c7KncR0qEXHlUYpdTTILdOsGS5dCsWK5fm9L/Pe/MHcutGplTlIlIkWaSySzISEhzJ07ly5duhAYGMhf/vIXRo0axfLly7N93d3e0cw0kV2zxpzcKW2ygX/8A6ZMKRgTJaSkwIAB5iDfKVOgdWurIxIREXEJo0ePxmazZfs4fPiww2tiY2MJCQmhb9++PPPMM3ke05gxY0hISLA/Tp06lavXH4pxXEf2tY7+tHwtBFtCglmgc2f44gsoXjzPY883cXFQsaK5nqwrxS0i+cJF+pNkdLsuPWDe0ZwwYcIdvX+miezmzfD44zcnShg0yJzwqSAksgBvvAEbN5r9il580epoREREXMbIkSMJu80EjoGBgfafz5w5Q8eOHWnbti0fffRRtq+rXLkyycnJxMfHO7TOnjt3jsqVK2f5uuLFi1P8DhO2WxPZQfWq49G5M/z+u1mgXTtzkkhX68X1z3+a3YzLlbM6EhEpAFxyAqiYmBiaN2/O1KlTs70Teu3aNa5du2Z/npiYSEBAwG0HEmeayO7aBcHBcOmSWaigTZSwYQN06QI1a0JkJNzSlUlEig5NACWSv2JjY+nYsSPNmzfn008/xf02a7inTQC1ZMkSevfuDUB0dDR16tTJlwmgMiSyjYLw6NQJ0ro0338/rFsH3t45O+GCYOVKePTRglPvEpF85RITQOV3l57ixYtTpkwZh8ftZJrIRkWZY0rSEtlHH4VPPy1YH6h165rdhT7/XImsiIhIPomNjaVDhw5Uq1aNqVOn8uuvv3L27FmHsa+xsbHUqVOH3bt3A+Dj48OQIUMYMWIEmzZtIjIykqeffpo2bdrkOJHNqQyJbJ9ueOzbB+fPmwWaN4fVq10rkV26FHr2hOHDrY5ERAoYS7Ox/OzScycyTWQPHYKHH4b4eLNQp07m1ICennl+/Ltyzz2wfr3VUYiIiBRq69evJyYmhpiYGKpWreqwL62z2/Xr14mOjuby5cv2fdOnT8fNzY3evXtz7do1unbtygcffJCnsWWayLq7w5NPmgWmTIG1a13rpvexY+YyiN7eSmZFJAOX6Wac2y49mcmuuTrTRPbIEXNMSVycWahtW/NLoHTpuz6fPPPee1CvntkFWkQEdTMWKayy+9vOMpFNLyUF7qD+ZJnr1+HBB2H3bq0nK1LE5LQuU4D6yWYtrUtP9erV7V160mQ3cUJOZZrInj5tdttNS2SbNYOvvy5YieyOHfDyy1CjhtmCXNBai0VERCTf5SiRBddKZMGc7Gn3bggLUyIrIplyiWQ2J1167vi9M0tkAXx8zMmUTpyA+vXNFlkfn7s6Vp5KSDCX4bHZzPG7SmRFRESKnBwnsq7myBGYOhVq14aZM62ORkQKKJdYZzYsLAzDMDJ93I0sE1kwx2Z8/TU895w5U3DFind1rDxlGOb6tidOQHg4tGljdUQiIiLiZIU2kQUICoI1a+CzzwpWrzgRKVBcomU2P2SbyKbx8oI8npwhTyxYYH64t2sHr71mdTQiIiLiZIU2kU1NNW/au7ubE3CKiGTDJVpm89o33+65fSJbUBmGmciWLWt2Ly4MX1wiIiKSY9FHThTORBbMrsXp5ywREclGkWyZXbfle4qXKOF6iSyYY2T/3/+Dw4ehWjWroxEREREnW/jFGtyLeRa+RPa772DsWPDz01wgIpIjRbJlFlywRRbg+HHzXw8PaNDA0lBERETEGjdSUgtfIpuQAP37m92MFy2CChWsjkhEXECRaplNmzDqwRb1aN24DomJiRZHlAubNkGvXjBpkjn5k4hIFtI+21xkGXERyaG0v+nAAD96dnmAy5cuWRxRHjEMGDIEjh2D0aOhaVNwpTqaiOS5nNZlbEYRqu2cPn2agIAAq8MQEXGKU6dOZVjOTERcl+oxIlLU3K4uU6SS2dTUVM6cOYO3tzc2m+225RMTEwkICODUqVOUKVPGCREWPEX9Guj8df6ueP6GYfDHH3/g7++Pm1uRHU0iUujkth6Tnqt+nllJ1yx3dL1yR9crezmtyxSpbsZubm531EpRpkyZIv+frKhfA52/zt/Vzt/Hx8fqEEQkj91pPSY9V/w8s5quWe7oeuWOrlfWclKX0S17ERERERERcTlKZkVERERERMTlKJnNRvHixQkPD6d48eJWh2KZon4NdP46/6J8/iJSeOjzLPd0zXJH1yt3dL3yRpGaAEpEREREREQKB7XMioiIiIiIiMtRMisiIiIiIiIuR8msiIiIiIiIuBwlsyIiIiIiIuJylMzm0PHjxxkyZAg1a9bEy8uLoKAgwsPDSU5Otjo0p5k4cSJt27alZMmSlC1b1upw8t2sWbOoUaMGJUqUoHXr1uzevdvqkJxm69atdO/eHX9/f2w2GytWrLA6JKeZNGkSLVu2xNvbm0qVKtGzZ0+io6OtDktEJE+oPnNnilodKLeKcp0pt4pyHSs/KJnNocOHD5OamsqcOXM4cOAA06dPZ/bs2bz22mtWh+Y0ycnJ9O3bl+eee87qUPLd0qVLGTFiBOHh4ezdu5fGjRvTtWtXzp8/b3VoTnHp0iUaN27MrFmzrA7F6bZs2cLQoUPZtWsX69ev5/r163Tp0oVLly5ZHZqIyF1TfebOFKU6UG4V9TpTbhXlOlZ+0NI8d2HKlCl8+OGHHD161OpQnGrevHkMHz6c+Ph4q0PJN61bt6Zly5a8//77AKSmphIQEMALL7zA6NGjLY7OuWw2GxEREfTs2dPqUCzx66+/UqlSJbZs2UL79u2tDkdEJM8V1frMnSgKdaDcUp3pzhX1OlZeUMvsXUhISKB8+fJWhyF5LDk5mcjISIKDg+3b3NzcCA4OZufOnRZGJlZISEgA0N+6iBRaqs/InVKdSaymZPYOxcTEMHPmTJ599lmrQ5E8duHCBVJSUvDz83PY7ufnx9mzZy2KSqyQmprK8OHDeeCBB2jQoIHV4YiI5DnVZ+RuqM4kVivyyezo0aOx2WzZPg4fPuzwmtjYWEJCQujbty/PPPOMRZHnjTs5f5GiYujQoezfv5/PPvvM6lBERLJV1Oszd0J1IBHX52F1AFYbOXIkYWFh2ZYJDAy0/3zmzBk6duxI27Zt+eijj/I5uvyX2/MvCipWrIi7uzvnzp1z2H7u3DkqV65sUVTibMOGDWPVqlVs3bqVqlWrWh2OiEi2inp95k6oDnT3VGcSqxX5ZNbX1xdfX98clY2NjaVjx440b96cuXPn4ubm+g3buTn/osLT05PmzZuzceNG+4D81NRUNm7cyLBhw6wNTvKdYRi88MILREREsHnzZmrWrGl1SCIit1XU6zN3QnWgu6c6k1ityCezORUbG0uHDh2oXr06U6dO5ddff7XvKyp3nk6ePMnFixc5efIkKSkpREVFAVCrVi1Kly5tbXB5bMSIEYSGhtKiRQtatWrFjBkzuHTpEk8//bTVoTlFUlISMTEx9ufHjh0jKiqK8uXLU61aNQsjy39Dhw5l8eLFrFy5Em9vb/uYHx8fH7y8vCyOTkTk7qg+c2eKUh0ot4p6nSm3inIdK18YkiNz5841gEwfRUVoaGim579p0yarQ8sXM2fONKpVq2Z4enoarVq1Mnbt2mV1SE6zadOmTH/XoaGhVoeW77L6O587d67VoYmI3DXVZ+5MUasD5VZRrjPlVlGuY+UHrTMrIiIiIiIiLqdoDpIQERERERERl6ZkVkRERERERFyOklkRERERERFxOUpmRURERERExOUomRURERERERGXo2RWREREREREXI6SWREREREREXE5SmZFRERERETE5SiZFafYvHkzNpuN+Ph4q0PJFZvNxooVK/Ls/WrUqMGMGTPy7P2c7fjx49hsNqKiogDX/b2KiIjkhqt+36ke40j1mMJHyazcNZvNlu1j/PjxVod4W+PHj6dJkyYZtsfFxdGtWzfnB1QAhIWF0bNnT4dtAQEBxMXF0aBBA2uCEhERyWOqxxROqscUDR5WByCuLy4uzv7z0qVLGTduHNHR0fZtpUuXZs+ePVaERnJyMp6ennf8+sqVK+dhNK7P3d1d10RERAoV1WOKDtVjCh+1zMpdq1y5sv3h4+ODzWZz2Fa6dGl72cjISFq0aEHJkiVp27atw5cFwMqVK2nWrBklSpQgMDCQCRMmcOPGDfv+kydP0qNHD0qXLk2ZMmV44oknOHfunH1/2p3Jjz/+mJo1a1KiRAkA4uPj+dvf/oavry9lypShU6dO7Nu3D4B58+YxYcIE9u3bZ78LO2/ePCBj95zTp0/Tv39/ypcvT6lSpWjRogXfffcdAEeOHKFHjx74+flRunRpWrZsyYYNG3J1LVNSUhgxYgRly5alQoUKvPrqq4SGhjrcWcysi0+TJk0c7hxPmzaNhg0bUqpUKQICAnj++edJSkqy7583bx5ly5Zl7dq11K1bl9KlSxMSEmL/Qh8/fjzz589n5cqV9muyefPmDN1zMrN9+3batWuHl5cXAQEBvPjii1y6dMm+/4MPPuDee++lRIkS+Pn50adPn1xdIxERkbykeozqMempHuNalMyKU40dO5Z33nmHPXv24OHhweDBg+37tm3bxqBBg3jppZc4ePAgc+bMYd68eUycOBGA1NRUevTowcWLF9myZQvr16/n6NGj9OvXz+EYMTExfPHFFyxfvtz+YdW3b1/Onz/P6tWriYyMpFmzZnTu3JmLFy/Sr18/Ro4cSf369YmLiyMuLi7DewIkJSXx0EMPERsby5dffsm+fft49dVXSU1Nte9/5JFH2LhxIz/88AMhISF0796dkydP5vj6vPPOO8ybN49PPvmE7du3c/HiRSIiInJ7mXFzc+O9997jwIEDzJ8/n2+++YZXX33Voczly5eZOnUqCxcuZOvWrZw8eZJRo0YBMGrUKJ544gn7F0NcXBxt27a97XGPHDlCSEgIvXv35scff2Tp0qVs376dYcOGAbBnzx5efPFF/vWvfxEdHc2aNWto3759rs9PRETECqrHZE/1GHE6QyQPzZ071/Dx8cmwfdOmTQZgbNiwwb7tq6++MgDjypUrhmEYRufOnY233nrL4XULFy40qlSpYhiGYaxbt85wd3c3Tp48ad9/4MABAzB2795tGIZhhIeHG8WKFTPOnz9vL7Nt2zajTJkyxtWrVx3eOygoyJgzZ479dY0bN84QN2BEREQYhmEYc+bMMby9vY3ffvsth1fDMOrXr2/MnDnT/rx69erG9OnTsyxfpUoV49///rf9+fXr142qVasaPXr0yPY9GjdubISHh2f5vp9//rlRoUIF+/O5c+cagBETE2PfNmvWLMPPz8/+PDQ01OG4hmEYx44dMwDjhx9+MAzj5u/1999/NwzDMIYMGWL8/e9/d3jNtm3bDDc3N+PKlSvGF198YZQpU8ZITEzMMlYRERGrqB7jSPUY1WMKOo2ZFadq1KiR/ecqVaoAcP78eapVq8a+ffvYsWOH/Q4mmN1Vrl69yuXLlzl06BABAQEEBATY99erV4+yZcty6NAhWrZsCUD16tXx9fW1l9m3bx9JSUlUqFDBIZYrV65w5MiRHMceFRVF06ZNKV++fKb7k5KSGD9+PF999RVxcXHcuHGDK1eu5PiOZkJCAnFxcbRu3dq+zcPDgxYtWmAYRo7jBNiwYQOTJk3i8OHDJCYmcuPGDft1LFmyJAAlS5YkKCjI/poqVapw/vz5XB3nVvv27ePHH39k0aJF9m2GYZCamsqxY8d4+OGHqV69OoGBgYSEhBASEsLjjz9uj0lERKQgUz0ma6rHiBWUzIpTFStWzP6zzWYDcOjeMmHCBHr16pXhdWljRnKiVKlSDs+TkpKoUqUKmzdvzlC2bNmyOX5fLy+vbPePGjWK9evXM3XqVGrVqoWXlxd9+vQhOTk5x8fICTc3twxfCtevX7f/fPz4cR577DGee+45Jk6cSPny5dm+fTtDhgwhOTnZ/oGb/ncB5u8jt182t0pKSuLZZ5/lxRdfzLCvWrVqeHp6snfvXjZv3sy6desYN24c48eP5/vvv8/V70JERMQKqsfcPdVjJC8pmZUCo1mzZkRHR1OrVq1M99etW5dTp05x6tQp+13NgwcPEh8fT7169bJ937Nnz+Lh4UGNGjUyLePp6UlKSkq28TVq1IiPP/6YixcvZnpXc8eOHYSFhfH4448D5gfi8ePHs33P9Hx8fKhSpQrfffedffzFjRs37GNj0vj6+jrMvJiYmMixY8fszyMjI0lNTeWdd97Bzc0cFr9s2bIcx5EmJ9fkVs2aNePgwYNZ/g7BvEsbHBxMcHAw4eHhlC1blm+++SbTL38RERFXoXqM6jHifJoASgqMcePGsWDBAiZMmMCBAwc4dOgQn332Gf/85z8BCA4OpmHDhgwYMIC9e/eye/duBg0axEMPPUSLFi2yfN/g4GDatGlDz549WbduHcePH+fbb79l7Nix9qn2a9SowbFjx4iKiuLChQtcu3Ytw/v079+fypUr07NnT3bs2MHRo0f54osv2LlzJwD33nuvfbKGffv28de//tV+tzanXnrpJd5++21WrFjB4cOHef755zMs5N2pUycWLlzItm3b+OmnnwgNDcXd3d2+v1atWly/fp2ZM2dy9OhRFi5cyOzZs3MVR9o1+fHHH4mOjubChQsOd02z8n//9398++23DBs2jKioKH755RdWrlxpnzhh1apVvPfee0RFRXHixAkWLFhAamoqtWvXznV8IiIiBYnqMarHiPMpmZUCo2vXrqxatYp169bRsmVL7r//fqZPn0716tUBs/vIypUrKVeuHO3btyc4OJjAwECWLl2a7fvabDa+/vpr2rdvz9NPP819993Hk08+yYkTJ/Dz8wOgd+/ehISE0LFjR3x9fVmyZEmG9/H09GTdunVUqlSJRx55hIYNG/L222/bP4CnTZtGuXLlaNu2Ld27d6dr164OdyJzYuTIkQwcOJDQ0FDatGmDt7e3/Q5pmjFjxvDQQw/x2GOP8eijj9KzZ0+HMSONGzdm2rRpTJ48mQYNGrBo0SImTZqUqzgAnnnmGWrXrk2LFi3w9fVlx44dt31No0aN2LJlCz///DPt2rWjadOmjBs3Dn9/f8DsDrV8+XI6depE3bp1mT17NkuWLKF+/fq5jk9ERKQgUT1G9RhxPptxt53LRSRfhYWFER8f77BOnIiIiIgrUD1G8pNaZkVERERERMTlKJkVERERERERl6NuxiIiIiIiIuJy1DIrIiIiIiIiLkfJrIiIiIiIiLgcJbMiIiIiIiLicpTMioiIiIiIiMtRMisiIiIiIiIuR8msiIiIiIiIuBwlsyIiIiIiIuJylMyKiIiIiIiIy/n/MnrJFl1as4oAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1330x410 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
+    "\n",
+    "pg.qqplot(x_normal, ax=axes[0])\n",
+    "pg.qqplot(x_not_normal, ax=axes[1]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0f983f0-6143-44b0-9eee-f6d54eb5cf51",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "SciPy also provides similar plots (probability plots) with [probplot](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.probplot.html):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "6bcaa795-3a85-4b8c-aad9-d554e244a2ec",
+   "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": [
+    "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
+    "\n",
+    "stats.probplot(x_normal, plot=axes[0])\n",
+    "stats.probplot(x_not_normal, plot=axes[1]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f46e74c-ee1f-454c-a4a1-3e551899e7e7",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "#### Normality tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da10f0a4-eb46-4a63-90de-117e428b01d4",
+   "metadata": {},
+   "source": [
+    "With Pingouin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "920395a5-7770-45ed-9bd3-80ff71bda307",
+   "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>W</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>normal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.940223</td>\n",
+       "      <td>0.092226</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          W      pval  normal\n",
+       "0  0.940223  0.092226    True"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pg.normality(x_not_normal)#, method='jarque_bera')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35c08d95-c21f-4202-8bbf-eed8b50fcdf7",
+   "metadata": {},
+   "source": [
+    "A major issue with tests of normality is they depend too much on the sample size. They are not powerful enough on small samples, and tend to see departures of normality everywhere in large samples."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81a1572d-1639-42d9-834c-2ba86c7c7dd3",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "\n",
+    "Similar options can be found in Scipy:\n",
+    "* D'Agostino's test: [normaltest](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html), preferably for large samples ($n>20$),\n",
+    "    * Similar test for skewness only: [skewtest](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skewtest.html) ($n\\ge8$),"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "e964be07-0696-4ff8-844a-3e7d318a7f3a",
+   "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": [
+    "scipy_material.illustration_skewness_kurtosis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eed065d2-dd35-4fdb-ad91-8d387a06c7ea",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "* Shapiro-Wilk's test: [shapiro](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.shapiro.html),\n",
+    "* Generic goodness-of-fit tests: [kstest](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kstest.html) and [anderson](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.anderson.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c7a08d6-3c74-48ce-ad32-f164089b6ec7",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "### Equal variance (homoscedasticity)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e5a893e-6475-401b-9335-b117629b8d0a",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "#### Graphical approaches\n",
+    "\n",
+    "Simple per-group box plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "8a31b653-d891-4f07-ab8b-73562b2ed86d",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(df, y='response', x='group');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ecc864d-c32b-4465-b562-6b23c5f1ace2",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "#### Equality-of-variance tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "3b2c0d0d-0ab0-4bf7-8128-bcfa4d1a3cec",
+   "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>W</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>equal_var</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>levene</th>\n",
+       "      <td>2.080216</td>\n",
+       "      <td>0.144465</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               W      pval  equal_var\n",
+       "levene  2.080216  0.144465       True"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pg.homoscedasticity(df, dv='response', group='group')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "348a2ae9-6644-412a-b41c-d494e4620b90",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "In SciPy, we also find:\n",
+    "* Bartlett's test: [bartlett](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bartlett.html), most basic and common test,\n",
+    "* Levene's test: [levene](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.levene.html), better for skewed or heavy-tailed distributions,\n",
+    "* ...and others: Fligner-Killeen's test ([fligner](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fligner.html)), Ansari-Bradley's test ([ansari](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ansari.html)), etc\n",
+    "\n",
+    "Example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "280180fa-4f20-44a6-a463-2ce9b0ad75a4",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BartlettResult(statistic=3.3024375753550594, pvalue=0.19181598314035977)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.bartlett(A, B, C)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e906820a-af04-4a9d-992f-f2e07a7aed91",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "In the above example, as there is not enough evidence to reject $H_0$ ($p>0.05$), we can proceed to perform a standard one-way ANOVA, for example with Pingouin's `anova`. Otherwise, we would use Pingouin's [`welch_anova`](https://pingouin-stats.org/generated/pingouin.welch_anova.html) or SciPy's [`alexandergovern`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.alexandergovern.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c09c6222-4167-4e32-b6da-4abe3a8960de",
+   "metadata": {
+    "heading_collapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## χ² tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b60daa6-99f2-4b43-9b95-1bcda7114ec2",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "When the sum of the observations is known, *e.g.* observations are frequencies -- proportions that sum to $1$, we use a $\\chi^2$ test instead of an ANOVA.\n",
+    "\n",
+    "These tests are named after the $\\chi^2$ distribution. Tests based on this distribution derive a one-sided *p*-value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "f4a1bcc6-c691-44ff-9932-f34ac5fd9a03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1330x410 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scipy_material.illustration_chi2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac68602f-a5ee-4b75-befc-5fc198a7b5b1",
+   "metadata": {},
+   "source": [
+    "We will use:\n",
+    "* SciPy's [`contingency.crosstab`](), [`chisquare`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html) and [`chi2_contingency`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2_contingency.html),\n",
+    "* Pingouin's [`chi2_independence`](https://pingouin-stats.org/build/html/generated/pingouin.chi2_independence.html#pingouin.chi2_independence).\n",
+    "\n",
+    "Scipy's functions take counts as input data, while Pingouin's only function takes a dataframe and the column names of categorical variables.\n",
+    "\n",
+    "If we have data in long format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "141d0591-b63d-49e1-98e7-2491a6fa5d11",
+   "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>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>day</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>29.03</td>\n",
+       "      <td>5.92</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>27.18</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>22.67</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>17.82</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>18.78</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>244 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     total_bill   tip     sex smoker   day    time  size\n",
+       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
+       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
+       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
+       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
+       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
+       "..          ...   ...     ...    ...   ...     ...   ...\n",
+       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
+       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
+       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
+       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
+       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
+       "\n",
+       "[244 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tips = pg.read_dataset('tips')\n",
+    "tips"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f04ebc2-0fa8-43f8-a1d9-65069f18f53c",
+   "metadata": {},
+   "source": [
+    "We can summarize the crossed counts of the categorical variables in a contingency table, using SciPy's `contingency.crosstab`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "d90e3c9a-e4f5-4f07-b3c2-7bbaf1d018b8",
+   "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>Fri</th>\n",
+       "      <th>Sat</th>\n",
+       "      <th>Sun</th>\n",
+       "      <th>Thur</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>9</td>\n",
+       "      <td>28</td>\n",
+       "      <td>18</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>10</td>\n",
+       "      <td>59</td>\n",
+       "      <td>58</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Fri  Sat  Sun  Thur\n",
+       "Female    9   28   18    32\n",
+       "Male     10   59   58    30"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(row_labels, col_labels), counts = stats.contingency.crosstab(tips['sex'], tips['day'])\n",
+    "observed_counts = pd.DataFrame(counts, index=row_labels, columns=col_labels)\n",
+    "observed_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1feb29a6-eeac-4096-8e90-c663fdeea8a8",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "### Homogeneity and independence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ef858d5-ec8f-405d-924e-62f5286a94c5",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "$H_0$: men and women give tips following a similar weekly pattern.\n",
+    "\n",
+    "If we consider the proportion of each day in the total collected tips:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "fe4ce304-25ac-4734-9a0a-e20db59b742f",
+   "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>Fri</th>\n",
+       "      <th>Sat</th>\n",
+       "      <th>Sun</th>\n",
+       "      <th>Thur</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Altogether</th>\n",
+       "      <td>0.077869</td>\n",
+       "      <td>0.356557</td>\n",
+       "      <td>0.311475</td>\n",
+       "      <td>0.254098</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Fri       Sat       Sun      Thur\n",
+       "Altogether  0.077869  0.356557  0.311475  0.254098"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expected_props = observed_counts.sum(axis=0) / observed_counts.values.sum()\n",
+    "\n",
+    "pd.DataFrame(dict(Altogether=expected_props)).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f4b4727-3a9b-474f-b6ad-b632be97b04e",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "$H_0$ can be expressed in terms of proportions as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1473b92c-5f36-47a9-a17b-e6be81d9db52",
+   "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>Fri</th>\n",
+       "      <th>Sat</th>\n",
+       "      <th>Sun</th>\n",
+       "      <th>Thur</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>0.077869</td>\n",
+       "      <td>0.356557</td>\n",
+       "      <td>0.311475</td>\n",
+       "      <td>0.254098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>0.077869</td>\n",
+       "      <td>0.356557</td>\n",
+       "      <td>0.311475</td>\n",
+       "      <td>0.254098</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Fri       Sat       Sun      Thur\n",
+       "Female  0.077869  0.356557  0.311475  0.254098\n",
+       "Male    0.077869  0.356557  0.311475  0.254098"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(dict(Female=expected_props, Male=expected_props)).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6fa1de7-3da6-4853-9bac-bbd226b31014",
+   "metadata": {},
+   "source": [
+    "Or equivalently in terms of counts:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "087ea780-304c-459d-a7c0-066817f3d82f",
+   "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>Fri</th>\n",
+       "      <th>Sat</th>\n",
+       "      <th>Sun</th>\n",
+       "      <th>Thur</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>6.77459</td>\n",
+       "      <td>31.020492</td>\n",
+       "      <td>27.098361</td>\n",
+       "      <td>22.106557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>12.22541</td>\n",
+       "      <td>55.979508</td>\n",
+       "      <td>48.901639</td>\n",
+       "      <td>39.893443</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Fri        Sat        Sun       Thur\n",
+       "Female   6.77459  31.020492  27.098361  22.106557\n",
+       "Male    12.22541  55.979508  48.901639  39.893443"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expected_counts = np.outer(observed_counts.sum(axis=1), expected_props)\n",
+    "\n",
+    "pd.DataFrame(expected_counts, index=row_labels, columns=col_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0f49b1a-3a7a-4ad8-8d2a-9fd3513ad2a6",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "These counts are not integers. They are theoretical counts. We can contrast them with the observed counts, and derive the following statistic:\n",
+    "\n",
+    "$$\n",
+    "\\chi^2 = \\sum_{i=1}^{k}\\sum_{j=1}^{l} \\frac{(O_{ij} - E_{ij})^2}{E_{ij}} \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\sim \\chi^2_{(k-1)(l-1)} \\mbox{ } \\textrm{under} \\mbox{ } H_0\n",
+    "$$\n",
+    "\n",
+    "where $O_{ij}$ are the observed counts and $E_{ij}$ the expected or theoretical counts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "d9585b87-f5dd-4cf2-ad91-0a79dcc95c86",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.222001372406606"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "j, k = expected_counts.shape\n",
+    "dof = (j - 1) * (k - 1)\n",
+    "chi2 = np.sum((observed_counts.values - expected_counts) ** 2 / expected_counts)\n",
+    "chi2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12881bb7-405f-42c0-a892-eea600084170",
+   "metadata": {},
+   "source": [
+    "The $p$-value can calculated as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "a7cd985e-eb2b-4a72-a221-30e3e0fdf8ec",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.004180302092822262"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.chi2(dof).sf(chi2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13d7ef58-e62d-48de-a3a4-2ccc2ac588cb",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "SciPy's $\\chi^2$ test for homogeneity/independence is [chi2_contingency](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2_contingency.html):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "4cd7ea08-110a-4ff7-9521-0f12e4169d35",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13.22200137240661, 0.004180302092822257)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chi2, pvalue, dof, expected_props = stats.chi2_contingency(observed_counts)\n",
+    "(chi2, pvalue)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45da92b9-cd4e-4d1b-8996-451285ef1896",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "Due to the design of the test, it doesn't matter what factor whose effect is hypothesized to be null under $H_0$. We get the exact same result after transposing the contingency table:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "d7e7fd75-068c-4f37-b981-498890c4a0d7",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13.22200137240661, 0.004180302092822257)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chi2, pvalue, dof, expected_props_T = stats.chi2_contingency(observed_counts.T)\n",
+    "(chi2, pvalue)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb3833b5-fc3b-4433-9a91-608d9eeb68cd",
+   "metadata": {},
+   "source": [
+    "As already mentioned, Pingouin's `chi2_independence` is suitable for data in long format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "08c1de88-2f70-4bdb-9291-634c872b730d",
+   "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>test</th>\n",
+       "      <th>lambda</th>\n",
+       "      <th>chi2</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>cramer</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearson</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>13.222001</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.004180</td>\n",
+       "      <td>0.232784</td>\n",
+       "      <td>0.876800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cressie-read</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>13.186226</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.004251</td>\n",
+       "      <td>0.232469</td>\n",
+       "      <td>0.875841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>log-likelihood</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>13.194401</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.004235</td>\n",
+       "      <td>0.232541</td>\n",
+       "      <td>0.876061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>freeman-tukey</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>13.270507</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.004087</td>\n",
+       "      <td>0.233211</td>\n",
+       "      <td>0.878089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>mod-log-likelihood</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>13.407679</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.003833</td>\n",
+       "      <td>0.234413</td>\n",
+       "      <td>0.881673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>neyman</td>\n",
+       "      <td>-2.000000</td>\n",
+       "      <td>13.873557</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.003082</td>\n",
+       "      <td>0.238451</td>\n",
+       "      <td>0.893170</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 test    lambda       chi2  dof      pval    cramer     power\n",
+       "0             pearson  1.000000  13.222001  3.0  0.004180  0.232784  0.876800\n",
+       "1        cressie-read  0.666667  13.186226  3.0  0.004251  0.232469  0.875841\n",
+       "2      log-likelihood  0.000000  13.194401  3.0  0.004235  0.232541  0.876061\n",
+       "3       freeman-tukey -0.500000  13.270507  3.0  0.004087  0.233211  0.878089\n",
+       "4  mod-log-likelihood -1.000000  13.407679  3.0  0.003833  0.234413  0.881673\n",
+       "5              neyman -2.000000  13.873557  3.0  0.003082  0.238451  0.893170"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expected_counts, observed_counts, test_results = pg.chi2_independence(tips, 'sex', 'day')\n",
+    "test_results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d2998ef-7712-40a6-b79d-22b855aa12eb",
+   "metadata": {
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "### Goodness-of-fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d855b1f2-3105-445a-b1a6-b99bceeb66b7",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "Instead of inferring theoretical counts from the observed counts, we may want to test *vs* predefined (theoretical) counts. This is essentially the same test.\n",
+    "\n",
+    "A random example: [Color proportion of M&Ms [Coursera]](https://www.coursera.org/learn/stanford-statistics/lecture/rAwbR/the-color-proportions-of-m-ms):\n",
+    "| blue | orange | green | yellow | red | brown |\n",
+    "| :-:  | :-:    | :-:   | :-:    | :-: | :-:   |\n",
+    "| 24%  | 20%    | 16%   | 14%    | 13% | 13%   |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "84f4e9b3-a653-422c-8fc5-83b29869ba87",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "410"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expected_props  = np.array([ .24, .2, .16, .14, .13, .13 ])\n",
+    "observed_counts = np.array([ 85, 79, 56, 64, 58, 68 ])\n",
+    "np.sum(observed_counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "8bb2916a-e9a4-4bbe-b04f-14818bb68723",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([98.4, 82. , 65.6, 57.4, 53.3, 53.3])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expected_counts = expected_props * np.sum(observed_counts)\n",
+    "expected_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a75bd8d7-f61e-49bc-80b4-cb1f05ccd7c1",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "|          | blue | orange | green | yellow | red  | brown |\n",
+    "| --:      | :-:  | :-:    | :-:   | :-:    | :-:  | :-:   |\n",
+    "| Expected | 98.4 | 82     | 65.6  | 57.4   | 53.3 | 53.3  |\n",
+    "| Observed | 85   | 79     | 56    | 64     | 58   | 58    |\n",
+    "\n",
+    "The statistic is again:\n",
+    "\n",
+    "$$\n",
+    "\\chi^2 = \\sum_{i=1}^{k} \\frac{(O_i - E_i)^2}{E_i} \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\sim \\chi^2_{k-1} \\mbox{ } \\textrm{under} \\mbox{ } H_0\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "a8898631-a5b5-49e8-a158-3e149345391a",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.566983829178941"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k = len(expected_counts)\n",
+    "chi2 = np.sum((observed_counts - expected_counts) ** 2 / expected_counts)\n",
+    "chi2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "7a8af457-13dc-483a-90e3-50d3949c8edf",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1276329790529603"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pvalue = stats.chi2(k-1).sf(chi2)\n",
+    "pvalue"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92886c91-f6ef-419d-bfda-e28d9dfcc7bc",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "SciPy's implementation of the test is [chisquare](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "1e246915-e9be-4826-9677-d9f30348d0df",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Power_divergenceResult(statistic=8.566983829178941, pvalue=0.1276329790529603)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.chisquare(observed_counts, expected_counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81cbe581-b511-4e29-89cb-62c234c45fbb",
+   "metadata": {},
+   "source": [
+    "Pingouin's `chi2_independence` is not suitable for performing this test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca862d48",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true,
+    "tags": []
+   },
+   "source": [
+    "### Two-sample goodness-of-fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b1a4872",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "The $\\chi^2$ test can also be used for comparing the distributions of a continuous variable for two samples (two groups) in a more general way than a $t$-test for independent samples.\n",
+    "\n",
+    "The procedure consists in binning the continuous variable so that the problem can be formulated as a homogeneity test, with bins as the levels of one factor, and the grouping criterion as another factor.\n",
+    "\n",
+    "As a consequence, we will use the same functions as before.\n",
+    "\n",
+    "A similar test is the two-sample Kolmogorov-Smirnov test implemented in SciPy as [ks_2samp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html). This test does not require binning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e38724a8-405f-45d6-b622-c6f3caaa2025",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Aucun(e)",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/scipy_material.py b/notebooks/scipy_material.py
new file mode 100644
index 0000000000000000000000000000000000000000..bca6ecab07eb4601ce26a127f884916625e45c93
--- /dev/null
+++ b/notebooks/scipy_material.py
@@ -0,0 +1,198 @@
+import numpy as np
+from scipy import stats
+from matplotlib import pyplot as plt
+import ipywidgets as widgets
+from ipywidgets import interact
+
+"""
+Plot a normal distribution with shaded area to illustrate probability mass and
+confidence interval.
+
+Parameters
+----------
+m : real
+    sample mean
+s : real
+    standard error of the mean
+alpha : real
+    1 - probability mass
+
+"""
+def illustration_confidence_interval(m=46, s=1, alpha=0.05):
+    b = 3
+    grid = np.linspace(m-b*s, m+b*s, 200) # possible population mean values
+    pdf = stats.norm(m, s).pdf
+    prob = pdf(grid) # probability for the population mean
+
+    plt.plot(grid, prob, 'r-', zorder=3)
+    plt.axhline(0, color='k', linestyle=':', linewidth=1)
+    plt.xlabel('population mean')
+    plt.ylabel('probability density')
+    plt.axvline(m, color='k', linestyle=':', linewidth=1, label='sample mean')
+
+    u = stats.norm().isf(alpha / 2)
+    ci_low = m - u * s
+    ci_high = m + u * s
+
+    plt.fill_between(grid, np.zeros_like(prob), prob, where=(ci_low<=grid)&(grid<=ci_high), alpha=.1)
+    plt.plot([ci_low]*2, [0, pdf(ci_low)], color='b')#, label='confidence lower bound')
+    plt.plot([ci_high]*2, [0, pdf(ci_high)], color='b')#, label='confidence upper bound')
+
+    ml = (grid[0]+4*ci_low)/5
+    pl = (2*pdf(ci_low)+pdf(m))/3
+    plt.annotate(f'${alpha/2*100}\%$',
+        [ml, .1*pdf(ml)], [ml, pl],
+        arrowprops=dict(arrowstyle="->"),
+        horizontalalignment='center')
+
+    ml1 = (4*m+ci_high)/5
+    ml2 = (m+ci_high)/2
+    plt.annotate(f'${(1-alpha)*100:.0f}\%$ prob. mass',
+        [ml1, pl], [ml2, (pdf(ml2)+pdf(m))/2],
+        arrowprops=dict(arrowstyle="->"))
+
+    line_width, head_length, height = pdf(m)/30, b*s/10, .5*pdf(ci_low)
+    t = plt.arrow(ci_low+head_length, height, ci_high-ci_low-2*head_length, 0,
+        width=line_width, head_length=head_length, linestyle='none')
+    t = plt.arrow(ci_high-head_length, height, ci_low-ci_high+2*head_length, 0,
+        width=line_width, head_length=head_length, linestyle='none')
+    plt.text(m, height+line_width, f'${(1-alpha)*100:.0f}\%$ confidence interval',
+        ha='center')
+
+    plt.legend(loc='upper left')
+
+    plt.xlim([grid[0], grid[-1]])
+
+def illustration_onesided_probabilitymass(z, N = stats.norm(0, 1), onesided_pvalue=None):
+    if onesided_pvalue is None:
+        onesided_pvalue = N.sf(z)
+    grid = np.linspace(N.ppf(.001), N.ppf(.999), 100)
+    pdf_curve, = plt.plot(grid, N.pdf(grid), 'r-')
+    z_line, = plt.plot([z, z], [0, N.pdf(z)], '-', zorder=1)
+    tail = grid[z<=grid]
+    plt.fill_between(tail, np.zeros_like(tail), N.pdf(tail), alpha=.2)
+    plt.axvline(0, linestyle='--', color='grey', linewidth=1)
+    plt.axhline(0, linestyle='--', color='grey', linewidth=1)
+    plt.xlim(grid[[0,-1]])
+    plt.xlabel('$X$')
+    plt.ylabel('probability density')
+    plt.legend([pdf_curve, z_line], ['$\mathcal{N}(0,1)$', '$z$'])
+    plt.annotate(f'$\\approx {onesided_pvalue:.2f}$', (1.8, .03), xytext=(2, .13), arrowprops=dict(arrowstyle="->"))
+
+def illustration_t_pdfs():
+    grid = np.linspace(-3.1, 3.1, 100)
+
+    dfs = [1, 2, 5, 20]
+
+    for df, color in zip(
+        dfs,
+        ['blue', 'green', 'orange', 'red'],
+    ):
+        t = stats.t(df)
+        plt.plot(grid, t.pdf(grid), '-', color=color)
+
+    plt.axvline(0, linestyle='--', color='grey', linewidth=1)
+    plt.axhline(0, linestyle='--', color='grey', linewidth=1)
+    plt.xlim(grid[[0,-1]])
+    plt.xlabel('$t$')
+    plt.ylabel('probability density')
+    plt.legend([ f'$df={df}$' for df in dfs ]);
+
+def illustration_cohen_d():
+    def plot_pdfs(cohen_d):
+        grid = np.linspace(-3, 3+cohen_d, 100)
+        x1 = stats.norm(0, 1).pdf(grid)
+        x2 = stats.norm(cohen_d, 1).pdf(grid)
+        plt.fill_between(grid, x1, alpha=.5)
+        plt.fill_between(grid, x2, alpha=.5)
+        plt.show()
+    slider = widgets.FloatSlider(.5, min=0, max=4, step=.1)
+    return interact(plot_pdfs, cohen_d=slider)
+
+def illustration_skewness_kurtosis():
+    skewed_dist = lambda sigma, x: np.exp( -.5*(np.log(x)/sigma)**2 ) / ( x*sigma*np.sqrt(2*np.pi) )
+    heavy_tailed_dist = lambda scale, x: stats.cauchy.pdf(x, 0, scale)
+
+    colors = ['blue', 'green', 'orange', 'red']
+    _, axes = plt.subplots(1, 2, figsize=(13.3,4.1))
+
+    grid = np.linspace(0, 3, 100)
+    grid = grid[1:]
+
+    ax = axes[0]
+    for sigma, color in zip((.25, .5, 1), colors):
+        ax.plot(grid, skewed_dist(sigma, grid), '-', color=color, label=f'$\sigma={sigma:.2f}$')
+
+    ax.axhline(0, linestyle='--', color='grey', linewidth=1)
+    ax.set_xlim(grid[[0,-1]])
+    ax.set_title('skewness')
+
+    grid = np.linspace(-4, 4, 100)
+
+    ax = axes[1]
+    for s, color in zip((.5, 1, 2), colors):
+        ax.plot(grid, heavy_tailed_dist(s, grid), '-', color=color, label=f'$scale={s:.2f}$')
+
+    ax.axvline(0, linestyle='--', color='grey', linewidth=1)
+    ax.axhline(0, linestyle='--', color='grey', linewidth=1)
+    ax.set_xlim(grid[[0,-1]])
+    ax.set_title('kurtosis')
+
+    for ax in axes:
+        ax.set_ylabel('probability density')
+        ax.legend();
+
+def illustration_chi2():
+    grid = np.linspace(0, 20, 200)
+
+    dfs = [2, 3, 5, 9]
+
+    _, axes = plt.subplots(1, 2, figsize=(13.3,4.1))
+
+    ax = axes[0]
+    for df, color in zip(
+        dfs,
+        ['blue', 'green', 'orange', 'red'],
+    ):
+        chi2 = stats.chi2.pdf(grid, df)
+        ax.plot(grid, chi2, '-', color=color)
+
+    ax.axhline(0, linestyle='--', color='grey', linewidth=1)
+    ax.set_xlim(grid[0],grid[-1])
+    ax.set_xlabel('$\chi^2$')
+    ax.set_ylabel('probability density')
+    ax.legend([ f'$df={df}$' for df in dfs ])
+
+    ax = axes[1]
+    df, color = 2, 'blue'
+    chi2 = stats.chi2.pdf(grid, df)
+    ax.plot(grid, chi2, '-', color=color)
+    ax.axhline(0, linestyle='--', color='grey', linewidth=1)
+    ax.set_xlim(grid[0],grid[-1])
+    ax.set_xlabel('$\chi^2$')
+    ax.set_ylabel('probability density');
+
+    A = [85, 86, 88, 75, 78, 94, 98, 79, 71, 80]
+    B = [91, 92, 93, 85, 87, 84, 82, 88, 95, 96]
+    C = [79, 78, 88, 94, 92, 85, 83, 85, 82, 81]
+    bartlett_statistic, bartlett_pvalue = stats.bartlett(A, B, C)
+    bartlett_statistic_line, = ax.plot([bartlett_statistic]*2, [0, stats.chi2.pdf(bartlett_statistic, df)], '-', zorder=1)
+
+    tail = grid[bartlett_statistic<=grid]
+    ax.fill_between(tail, np.zeros_like(tail), stats.chi2.pdf(tail, df), alpha=.2)
+
+    ax.annotate(f'$\\approx {bartlett_pvalue:.2f}$', (4, .02), xytext=(8, .1), arrowprops=dict(arrowstyle="->"));
+
+def illustration_falsealarms(type1_error_rate, power):
+    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")
+