diff --git a/notebooks/scipy_TP_solutions.ipynb b/notebooks/scipy_TP_solutions.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..213e271d30d7e03abfb28ea95c7e161c41dc8a99
--- /dev/null
+++ b/notebooks/scipy_TP_solutions.ipynb
@@ -0,0 +1,1293 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "a5a5210d",
+ "metadata": {},
+ "source": [
+ "Import `numpy`, `pandas`, the `pyplot` module from `matplotlib`, `seaborn`, and the `stats` module from `scipy`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "529c5f56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from scipy import stats\n",
+ "from matplotlib import pyplot as plt\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bb564f37",
+ "metadata": {},
+ "source": [
+ "# Comparison of two group means"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "08c1dd12",
+ "metadata": {},
+ "source": [
+ "Load the `mi.csv` data file located in the `data` directory of the course repository:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "00130518",
+ "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>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": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_csv('../data/mi.csv', index_col=0)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9cc036b2",
+ "metadata": {},
+ "source": [
+ "Question: anything missing?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "8a648a9b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(816, 43)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "2f0a8116",
+ "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>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>FluIgG</th>\n",
+ " <th>MetabolicScore</th>\n",
+ " <th>LowAppetite</th>\n",
+ " <th>TroubleConcentrating</th>\n",
+ " <th>TroubleSleeping</th>\n",
+ " <th>HoursOfSleep</th>\n",
+ " <th>Listless</th>\n",
+ " <th>UsesCannabis</th>\n",
+ " <th>RecentPersonalCrisis</th>\n",
+ " <th>Smoking</th>\n",
+ " <th>Employed</th>\n",
+ " <th>Education</th>\n",
+ " <th>DustExposure</th>\n",
+ " <th>Income</th>\n",
+ " <th>HadMeasles</th>\n",
+ " <th>HadRubella</th>\n",
+ " <th>HadChickenPox</th>\n",
+ " <th>HadMumps</th>\n",
+ " <th>HadTonsillectomy</th>\n",
+ " <th>HadAppendicectomy</th>\n",
+ " <th>VaccineHepA</th>\n",
+ " <th>VaccineMMR</th>\n",
+ " <th>VaccineTyphoid</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>0.464319</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>9.00</td>\n",
+ " <td>3</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Never</td>\n",
+ " <td>No</td>\n",
+ " <td>PhD</td>\n",
+ " <td>No</td>\n",
+ " <td>(1000-2000]</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>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>-0.049817</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.05</td>\n",
+ " <td>3</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Active</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Baccalaureat</td>\n",
+ " <td>No</td>\n",
+ " <td>(2000-3000]</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>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>0.332944</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>6.50</td>\n",
+ " <td>3</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>Active</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Baccalaureat</td>\n",
+ " <td>Current</td>\n",
+ " <td>(2000-3000]</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>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>0.404886</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>10.00</td>\n",
+ " <td>3</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Never</td>\n",
+ " <td>No</td>\n",
+ " <td>PhD</td>\n",
+ " <td>No</td>\n",
+ " <td>(3000-inf]</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>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>-0.303782</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>9.00</td>\n",
+ " <td>0</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Never</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Baccalaureat</td>\n",
+ " <td>No</td>\n",
+ " <td>[0-1000]</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>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",
+ "</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 FluIgG MetabolicScore \\\n",
+ "1 Yes 94.9627 20.13 No 0.464319 0 \n",
+ "2 Yes 79.1024 21.33 Yes -0.049817 1 \n",
+ "3 Yes 117.2540 22.18 No 0.332944 2 \n",
+ "4 No 94.1796 18.68 No 0.404886 0 \n",
+ "5 Yes 105.1250 29.01 No -0.303782 1 \n",
+ "\n",
+ " LowAppetite TroubleConcentrating TroubleSleeping HoursOfSleep Listless \\\n",
+ "1 0 0 1.0 9.00 3 \n",
+ "2 0 0 1.0 7.05 3 \n",
+ "3 0 0 1.0 6.50 3 \n",
+ "4 0 0 2.0 10.00 3 \n",
+ "5 0 0 1.0 9.00 0 \n",
+ "\n",
+ " UsesCannabis RecentPersonalCrisis Smoking Employed Education \\\n",
+ "1 No No Never No PhD \n",
+ "2 No No Active Yes Baccalaureat \n",
+ "3 Yes No Active Yes Baccalaureat \n",
+ "4 No No Never No PhD \n",
+ "5 No No Never Yes Baccalaureat \n",
+ "\n",
+ " DustExposure Income HadMeasles HadRubella HadChickenPox HadMumps \\\n",
+ "1 No (1000-2000] No No Yes No \n",
+ "2 No (2000-3000] No No Yes No \n",
+ "3 Current (2000-3000] No No Yes No \n",
+ "4 No (3000-inf] No No Yes No \n",
+ "5 No [0-1000] No No No No \n",
+ "\n",
+ " HadTonsillectomy HadAppendicectomy VaccineHepA VaccineMMR VaccineTyphoid \\\n",
+ "1 No No No No No \n",
+ "2 No No No No No \n",
+ "3 No No No No No \n",
+ "4 No No No No No \n",
+ "5 No No No No 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 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# in Jupyter-lab, pandas is set to display dataframes with a limited number of columns\n",
+ "pd.options.display.max_columns = None\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3512f950",
+ "metadata": {},
+ "source": [
+ "Show a summary table for these data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a7a7d087",
+ "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>Age</th>\n",
+ " <th>PhysicalActivity</th>\n",
+ " <th>Inbreeding</th>\n",
+ " <th>BMI</th>\n",
+ " <th>FluIgG</th>\n",
+ " <th>MetabolicScore</th>\n",
+ " <th>LowAppetite</th>\n",
+ " <th>TroubleConcentrating</th>\n",
+ " <th>TroubleSleeping</th>\n",
+ " <th>HoursOfSleep</th>\n",
+ " <th>Listless</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>count</th>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " <td>816.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>46.485711</td>\n",
+ " <td>2.751804</td>\n",
+ " <td>91.904255</td>\n",
+ " <td>24.208958</td>\n",
+ " <td>0.203601</td>\n",
+ " <td>0.932598</td>\n",
+ " <td>0.512255</td>\n",
+ " <td>0.355392</td>\n",
+ " <td>1.119771</td>\n",
+ " <td>7.499246</td>\n",
+ " <td>1.290441</td>\n",
+ " <td>576.877451</td>\n",
+ " <td>0.544526</td>\n",
+ " <td>59.209559</td>\n",
+ " <td>36.431985</td>\n",
+ " <td>9.214806</td>\n",
+ " <td>185.485294</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>13.854402</td>\n",
+ " <td>3.565008</td>\n",
+ " <td>12.936172</td>\n",
+ " <td>3.181184</td>\n",
+ " <td>0.232411</td>\n",
+ " <td>0.893942</td>\n",
+ " <td>1.674008</td>\n",
+ " <td>1.408535</td>\n",
+ " <td>0.931400</td>\n",
+ " <td>1.017186</td>\n",
+ " <td>2.055716</td>\n",
+ " <td>518.489935</td>\n",
+ " <td>1.333918</td>\n",
+ " <td>9.206104</td>\n",
+ " <td>0.318461</td>\n",
+ " <td>0.378376</td>\n",
+ " <td>84.971737</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>20.170000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>43.727000</td>\n",
+ " <td>18.500000</td>\n",
+ " <td>-0.430491</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>37.000000</td>\n",
+ " <td>35.700000</td>\n",
+ " <td>8.433000</td>\n",
+ " <td>17.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>35.830000</td>\n",
+ " <td>0.500000</td>\n",
+ " <td>84.077225</td>\n",
+ " <td>21.770000</td>\n",
+ " <td>0.065082</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>7.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>300.750000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>54.000000</td>\n",
+ " <td>36.200000</td>\n",
+ " <td>8.917000</td>\n",
+ " <td>136.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>47.710000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>91.862800</td>\n",
+ " <td>23.850000</td>\n",
+ " <td>0.227855</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>7.500000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>556.500000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>58.000000</td>\n",
+ " <td>36.400000</td>\n",
+ " <td>9.233000</td>\n",
+ " <td>187.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>58.352500</td>\n",
+ " <td>4.000000</td>\n",
+ " <td>100.008000</td>\n",
+ " <td>26.210000</td>\n",
+ " <td>0.363819</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>8.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>779.250000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>65.000000</td>\n",
+ " <td>36.600000</td>\n",
+ " <td>9.550000</td>\n",
+ " <td>263.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>69.750000</td>\n",
+ " <td>49.000000</td>\n",
+ " <td>150.107000</td>\n",
+ " <td>32.000000</td>\n",
+ " <td>0.769841</td>\n",
+ " <td>4.000000</td>\n",
+ " <td>14.000000</td>\n",
+ " <td>14.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>12.000000</td>\n",
+ " <td>14.000000</td>\n",
+ " <td>5701.000000</td>\n",
+ " <td>14.000000</td>\n",
+ " <td>100.000000</td>\n",
+ " <td>37.700000</td>\n",
+ " <td>11.217000</td>\n",
+ " <td>335.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Age PhysicalActivity Inbreeding BMI FluIgG \\\n",
+ "count 816.000000 816.000000 816.000000 816.000000 816.000000 \n",
+ "mean 46.485711 2.751804 91.904255 24.208958 0.203601 \n",
+ "std 13.854402 3.565008 12.936172 3.181184 0.232411 \n",
+ "min 20.170000 0.000000 43.727000 18.500000 -0.430491 \n",
+ "25% 35.830000 0.500000 84.077225 21.770000 0.065082 \n",
+ "50% 47.710000 2.000000 91.862800 23.850000 0.227855 \n",
+ "75% 58.352500 4.000000 100.008000 26.210000 0.363819 \n",
+ "max 69.750000 49.000000 150.107000 32.000000 0.769841 \n",
+ "\n",
+ " MetabolicScore LowAppetite TroubleConcentrating TroubleSleeping \\\n",
+ "count 816.000000 816.000000 816.000000 816.000000 \n",
+ "mean 0.932598 0.512255 0.355392 1.119771 \n",
+ "std 0.893942 1.674008 1.408535 0.931400 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 0.000000 0.000000 0.000000 \n",
+ "50% 1.000000 0.000000 0.000000 1.000000 \n",
+ "75% 1.000000 0.000000 0.000000 2.000000 \n",
+ "max 4.000000 14.000000 14.000000 3.000000 \n",
+ "\n",
+ " HoursOfSleep Listless SUBJID DepressionScore HeartRate \\\n",
+ "count 816.000000 816.000000 816.000000 816.000000 816.000000 \n",
+ "mean 7.499246 1.290441 576.877451 0.544526 59.209559 \n",
+ "std 1.017186 2.055716 518.489935 1.333918 9.206104 \n",
+ "min 3.000000 0.000000 2.000000 0.000000 37.000000 \n",
+ "25% 7.000000 0.000000 300.750000 0.000000 54.000000 \n",
+ "50% 7.500000 0.000000 556.500000 0.000000 58.000000 \n",
+ "75% 8.000000 3.000000 779.250000 1.000000 65.000000 \n",
+ "max 12.000000 14.000000 5701.000000 14.000000 100.000000 \n",
+ "\n",
+ " Temperature HourOfSampling DayOfSampling \n",
+ "count 816.000000 816.000000 816.000000 \n",
+ "mean 36.431985 9.214806 185.485294 \n",
+ "std 0.318461 0.378376 84.971737 \n",
+ "min 35.700000 8.433000 17.000000 \n",
+ "25% 36.200000 8.917000 136.000000 \n",
+ "50% 36.400000 9.233000 187.000000 \n",
+ "75% 36.600000 9.550000 263.000000 \n",
+ "max 37.700000 11.217000 335.000000 "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ec2a049f",
+ "metadata": {},
+ "source": [
+ "Inspect the distribution of variables `Age` and `OwnsHouse`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "14572a36",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# categorical vs continuous => boxplot, violinplot\n",
+ "sns.boxplot(x='OwnsHouse', y='Age', data=df);\n",
+ "# optional\n",
+ "sns.swarmplot(x='OwnsHouse', y='Age', data=df, linewidth=1, size=3);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "9c625646",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " _________________________________________\r\n",
+ "/ Where on Earth ALL younger people own a \\\r\n",
+ "\\ house while elder people do not? /\r\n",
+ " -----------------------------------------\r\n",
+ " \\ ^__^\r\n",
+ " \\ (oo)\\_______\r\n",
+ " (__)\\ )\\/\\\r\n",
+ " ||----w |\r\n",
+ " || ||\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cowsay \"Where on Earth ALL younger people own a house while elder people do not?\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "af5660e7",
+ "metadata": {},
+ "source": [
+ "Isolate the house-owners group from the others, draw their respective age distributions and check they are normally distributed:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "86252da3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "group = df.groupby('OwnsHouse').groups\n",
+ "house_owners = group['Yes']\n",
+ "others = group['No']\n",
+ "house_owners_age = df.loc[house_owners, 'Age']\n",
+ "others_age = df.loc[others, 'Age']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "1f4abc62",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.histplot(hue='OwnsHouse', y='Age', data=df, kde=True);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "b67a8ea8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "(theoretical_quantiles, observed_quantiles), (slope, intercept, _) = stats.probplot(house_owners_age, fit=True)\n",
+ "plt.scatter(theoretical_quantiles, observed_quantiles, marker='+', color='b')\n",
+ "plt.axline((0, intercept), slope=slope, color='r');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1d0bb402",
+ "metadata": {},
+ "source": [
+ "\\[CORR\\]\n",
+ "\n",
+ "The red line is fitted to the blue points and does not align well on the linear part. To better illustrate what is the linear part, we reimplement the regression (the exact implementation is out of the scope of this session):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "8f971da3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import statsmodels.api as sm # anticipating the next class...\n",
+ "central = (-1<theoretical_quantiles) & (theoretical_quantiles<1)\n",
+ "model = sm.OLS(observed_quantiles[central], sm.add_constant(theoretical_quantiles[central])).fit()\n",
+ "a, b = model.params\n",
+ "plt.scatter(theoretical_quantiles, observed_quantiles, marker='+', color='b')\n",
+ "plt.axline((0, a), slope=b, color='r');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b8ecfebf",
+ "metadata": {},
+ "source": [
+ "\\[CORR\\]\n",
+ "\n",
+ "The misalignment of the default regression line on the central part of the distribution is indicative of some asymmetry, while the diverging tails also hint at some departure from normality (kurtosis).\n",
+ "\n",
+ "Here, we have comfortable sample sizes and these departures from normality may not affect the power of the statistical test."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6de8e66",
+ "metadata": {},
+ "source": [
+ "Are the sample size and variance of the two groups similar enough for running a standard $t$ test?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "d5ac4dc5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(288, 528, 219.94736689332564, 138.81976459481174)"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(house_owners_age), len(others_age), np.var(house_owners_age), np.var(others_age)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e177b52b",
+ "metadata": {},
+ "source": [
+ "\\[CORR\\] `ttest_ind` allows variance ratios up to $2$. The groups can have different sample sizes."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cd273f22",
+ "metadata": {},
+ "source": [
+ "Test the group mean ages equal:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "ed503837",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Ttest_indResult(statistic=-10.858676761684935, pvalue=9.562420864768222e-26)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# define your significance level first!\n",
+ "significance_level = 0.05\n",
+ "\n",
+ "# run a t-test for independent samples\n",
+ "stats.ttest_ind(house_owners_age, others_age)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "967b1f9f",
+ "metadata": {},
+ "source": [
+ "How would you report the result of this test?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "de90c6e8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(814, -10.305953282828284, -0.7954424784394866)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# we need:\n",
+ "# * the number of degrees of freedom, to give a complete report of the outcome of the t-test,\n",
+ "n1, n2 = len(house_owners_age), len(others_age)\n",
+ "degrees_of_freedom = n1 + n2 - 2\n",
+ "\n",
+ "# * the mean difference (this is almost an effect size in itself, an intuitive one),\n",
+ "mean_difference = np.mean(house_owners_age) - np.mean(others_age)\n",
+ "\n",
+ "# * and the effect size.\n",
+ "f, _ = stats.ttest_ind(house_owners_age, others_age)\n",
+ "cohen_d = f * np.sqrt(1/n1 + 1/n2)\n",
+ "\n",
+ "degrees_of_freedom, mean_difference, cohen_d"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21e69d05",
+ "metadata": {},
+ "source": [
+ "\\[CORR\\]\n",
+ "\n",
+ "«**In our study**, house owners ($n=288$) were found to be significantly younger than the other surveyed people ($n=528$; $10.3$ years younger on average, $t(814)=-10.9$, $p<0.05$). This effect was found to be large (Cohen's $d \\approx 0.8$).»\n",
+ "\n",
+ "Note: as we report the sample size for each group, we may omit the (still nice-to-have) information of the number of degrees of freedom."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "54db26df",
+ "metadata": {},
+ "source": [
+ "# Asymmetry and power"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b3e0f9d2",
+ "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": "3.8.10"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "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/scipy_cours.ipynb b/notebooks/scipy_cours.ipynb
index 78f76bf5346b1c836453a57f9016872536b960ff..8237eb9b1ecd82e3ac028df04753c3722556afd4 100644
--- a/notebooks/scipy_cours.ipynb
+++ b/notebooks/scipy_cours.ipynb
@@ -192,7 +192,7 @@
"Always good to get a reminder about [general considerations](https://www.coursera.org/learn/stanford-statistics/home/welcome), __prior to data collection__ and analysis.\n",
"\n",
"* Sampling from the population,\n",
- "* identifying the sources of variability..."
+ "* identifying the sources of variability, etc."
]
},
{
@@ -1587,7 +1587,7 @@
"\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 rejecting $H_0$ by chance.\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 $H_0$."
]
},
diff --git a/notebooks/statsmodels_cours.ipynb b/notebooks/statsmodels_cours.ipynb
index 8bf5f2a7aefdf169f491956149501d5ee899abc5..56915f5a7d3031ad9cc6f0c59785ceb3d08e6041 100644
--- a/notebooks/statsmodels_cours.ipynb
+++ b/notebooks/statsmodels_cours.ipynb
@@ -15,14 +15,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Requirement already satisfied: statsmodels in /home/flaurent/.local/lib/python3.8/site-packages (0.12.2)\n",
- "Requirement already satisfied: pandas>=0.21 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.3.1)\n",
- "Requirement already satisfied: numpy>=1.15 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.21.1)\n",
- "Requirement already satisfied: scipy>=1.1 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.7.1)\n",
- "Requirement already satisfied: patsy>=0.5 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (0.5.1)\n",
- "Requirement already satisfied: pytz>=2017.3 in /usr/lib/python3/dist-packages (from pandas>=0.21->statsmodels) (2019.3)\n",
- "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/lib/python3/dist-packages (from pandas>=0.21->statsmodels) (2.7.3)\n",
- "Requirement already satisfied: six in /usr/lib/python3/dist-packages (from patsy>=0.5->statsmodels) (1.14.0)\n"
+ "Requirement already satisfied: statsmodels in /home/flaurent/.local/lib/python3.8/site-packages (0.12.2)\r\n",
+ "Requirement already satisfied: numpy>=1.15 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.21.1)\r\n",
+ "Requirement already satisfied: pandas>=0.21 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.3.1)\r\n",
+ "Requirement already satisfied: patsy>=0.5 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (0.5.1)\r\n",
+ "Requirement already satisfied: scipy>=1.1 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.7.1)\r\n",
+ "Requirement already satisfied: pytz>=2017.3 in /usr/lib/python3/dist-packages (from pandas>=0.21->statsmodels) (2019.3)\r\n",
+ "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/lib/python3/dist-packages (from pandas>=0.21->statsmodels) (2.7.3)\r\n",
+ "Requirement already satisfied: six in /usr/lib/python3/dist-packages (from patsy>=0.5->statsmodels) (1.14.0)\r\n"
]
}
],
@@ -71,7 +71,7 @@
"id": "4b642a0a-0046-4a61-87d6-d303d41d3f06",
"metadata": {},
"source": [
- "## Data formatting"
+ "## Data format"
]
},
{
@@ -410,6 +410,14 @@
"fitted_model = smf.ols('Y ~ Group', data=dataframe).fit()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "a8110314",
+ "metadata": {},
+ "source": [
+ "OLS stands for *ordinary least squares*."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 9,
@@ -425,8 +433,8 @@
"Dep. Variable: Y R-squared: 0.149\n",
"Model: OLS Adj. R-squared: 0.086\n",
"Method: Least Squares F-statistic: 2.358\n",
- "Date: Thu, 23 Sep 2021 Prob (F-statistic): 0.114\n",
- "Time: 14:56:51 Log-Likelihood: -96.604\n",
+ "Date: Fri, 24 Sep 2021 Prob (F-statistic): 0.114\n",
+ "Time: 10:19:09 Log-Likelihood: -96.604\n",
"No. Observations: 30 AIC: 199.2\n",
"Df Residuals: 27 BIC: 203.4\n",
"Df Model: 2 \n",
@@ -651,7 +659,7 @@
"id": "f7a66725-297c-45c1-b262-1a62b3ebd49f",
"metadata": {},
"source": [
- "## Linear model"
+ "## Linear models"
]
},
{
@@ -985,12 +993,12 @@
"output_type": "stream",
"text": [
"Requirement already satisfied: formulaic in /home/flaurent/.local/lib/python3.8/site-packages (0.2.4)\n",
- "Requirement already satisfied: numpy in /home/flaurent/.local/lib/python3.8/site-packages (from formulaic) (1.21.1)\n",
"Requirement already satisfied: astor in /home/flaurent/.local/lib/python3.8/site-packages (from formulaic) (0.8.1)\n",
+ "Requirement already satisfied: interface-meta>=1.2 in /home/flaurent/.local/lib/python3.8/site-packages (from formulaic) (1.2.4)\n",
+ "Requirement already satisfied: pandas in /home/flaurent/.local/lib/python3.8/site-packages (from formulaic) (1.3.1)\n",
+ "Requirement already satisfied: numpy in /home/flaurent/.local/lib/python3.8/site-packages (from formulaic) (1.21.1)\n",
"Requirement already satisfied: wrapt in /home/flaurent/.local/lib/python3.8/site-packages (from formulaic) (1.12.1)\n",
"Requirement already satisfied: scipy in /home/flaurent/.local/lib/python3.8/site-packages (from formulaic) (1.7.1)\n",
- "Requirement already satisfied: pandas in /home/flaurent/.local/lib/python3.8/site-packages (from formulaic) (1.3.1)\n",
- "Requirement already satisfied: interface-meta>=1.2 in /home/flaurent/.local/lib/python3.8/site-packages (from formulaic) (1.2.4)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/lib/python3/dist-packages (from pandas->formulaic) (2019.3)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/lib/python3/dist-packages (from pandas->formulaic) (2.7.3)\n"
]
@@ -1322,10 +1330,10 @@
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 2.358</td>\n",
"</tr>\n",
"<tr>\n",
- " <th>Date:</th> <td>Thu, 23 Sep 2021</td> <th> Prob (F-statistic):</th> <td> 0.114</td> \n",
+ " <th>Date:</th> <td>Fri, 24 Sep 2021</td> <th> Prob (F-statistic):</th> <td> 0.114</td> \n",
"</tr>\n",
"<tr>\n",
- " <th>Time:</th> <td>14:56:54</td> <th> Log-Likelihood: </th> <td> -96.604</td>\n",
+ " <th>Time:</th> <td>10:19:12</td> <th> Log-Likelihood: </th> <td> -96.604</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 30</td> <th> AIC: </th> <td> 199.2</td>\n",
@@ -1380,8 +1388,8 @@
"Dep. Variable: Y R-squared: 0.149\n",
"Model: OLS Adj. R-squared: 0.086\n",
"Method: Least Squares F-statistic: 2.358\n",
- "Date: Thu, 23 Sep 2021 Prob (F-statistic): 0.114\n",
- "Time: 14:56:54 Log-Likelihood: -96.604\n",
+ "Date: Fri, 24 Sep 2021 Prob (F-statistic): 0.114\n",
+ "Time: 10:19:12 Log-Likelihood: -96.604\n",
"No. Observations: 30 AIC: 199.2\n",
"Df Residuals: 27 BIC: 203.4\n",
"Df Model: 2 \n",
@@ -1478,8 +1486,8 @@
"Dep. Variable: Y R-squared: 0.149\n",
"Model: OLS Adj. R-squared: 0.086\n",
"Method: Least Squares F-statistic: 2.358\n",
- "Date: Thu, 23 Sep 2021 Prob (F-statistic): 0.114\n",
- "Time: 14:56:54 Log-Likelihood: -96.604\n",
+ "Date: Fri, 24 Sep 2021 Prob (F-statistic): 0.114\n",
+ "Time: 10:19:13 Log-Likelihood: -96.604\n",
"No. Observations: 30 AIC: 199.2\n",
"Df Residuals: 27 BIC: 203.4\n",
"Df Model: 2 \n",
@@ -1779,6 +1787,14 @@
"plant_model.params"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "12fc1dcd",
+ "metadata": {},
+ "source": [
+ "If we plot them together with the data...:"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 35,
@@ -1852,6 +1868,18 @@
"ax.plot([x[2]-dx, x[2]+dx], [y_high_daily, y_high_weekly], 'k-d', markerfacecolor='w');"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "ee8754fe",
+ "metadata": {},
+ "source": [
+ "...we can appreciate the equal daily-weekly differences do not quite match the variability across the levels of the `sun` factor.\n",
+ "\n",
+ "This inter-factor dependence is called an *interaction*.\n",
+ "\n",
+ "To model this interaction, we need an extra term in the model:"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 37,
@@ -1872,6 +1900,7 @@
],
"source": [
"model_with_interaction = ols('height ~ water * sun', data=plant_data).fit()\n",
+ "# remember `water * sun` is equivalent to `water + sun + water:sun`\n",
"print(sm.stats.anova_lm(model_with_interaction))"
]
},
@@ -1941,6 +1970,16 @@
"ax.plot([x[2]-dx, x[2]+dx], [y_high_daily, y_high_weekly], 'k-d', markerfacecolor='w');"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "101b2d2f",
+ "metadata": {},
+ "source": [
+ "`statsmodels` features a `interaction_plot` helper function.\n",
+ "\n",
+ "We still need quite some boilerplate to make it play nicely with `swarmplot`, but the code below is easier to generalize and wrap into a function:"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 40,
@@ -1998,7 +2037,7 @@
"source": [
"If main effects are found to be significant, we can proceed to performing pairwise comparisons between the levels of each factor.\n",
"\n",
- "If the interaction effect was not significant, we could proceed as follows:"
+ "If the interaction effect were not significant, we could proceed as follows:"
]
},
{
@@ -2094,8 +2133,8 @@
}
],
"source": [
- "post_hoc_tests = plant_model.t_test_pairwise('sun')\n",
- "post_hoc_tests.result_frame"
+ "posthoc_tests_sun = plant_model.t_test_pairwise('sun')\n",
+ "posthoc_tests_sun.result_frame"
]
},
{
@@ -2165,19 +2204,46 @@
}
],
"source": [
- "post_hoc_tests = plant_model.t_test_pairwise('water')\n",
- "post_hoc_tests.result_frame"
+ "posthoc_tests_water = plant_model.t_test_pairwise('water')\n",
+ "posthoc_tests_water.result_frame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "62d4382a",
+ "metadata": {},
+ "source": [
+ "### The multiple comparisons problem"
]
},
{
"cell_type": "code",
"execution_count": 43,
+ "id": "7f395588",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "power = 0.8\n",
+ "type1_error_rate = 0.05"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "167951da",
+ "metadata": {},
+ "source": [
+ "Red pixels represent the tests (comparisons) for which $H_0$ is false (right figure) or rejected (left figure):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
"id": "8642f0e6",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 957.6x295.2 with 2 Axes>"
]
@@ -2189,17 +2255,14 @@
}
],
"source": [
- "power = 0.8\n",
- "type1_error = 0.05\n",
- "\n",
"true_grid = np.zeros((20, 60), dtype=bool)\n",
"true_grid[:10,-10:] = True\n",
"\n",
- "rejection_grid = np.array([[ np.random.rand() <= type1_error for _ in range(60) ] for _ in range(20)])\n",
+ "rejection_grid = np.array([[ np.random.rand() <= type1_error_rate for _ in range(60) ] for _ in range(20)])\n",
"rejection_grid[:10,-10:] = [[ np.random.rand() <= power for _ in range(10)] for _ in range(10)]\n",
"\n",
"_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
- "for ax, title, grid in zip(axes[::-1], ('true', 'observed'), (true_grid, rejection_grid)):\n",
+ "for ax, title, grid in zip(axes[::-1], ('true', 'observed (actual test results)'), (true_grid, rejection_grid)):\n",
" ax.imshow(grid, cmap='seismic')\n",
" ax.set_title(title)\n",
" ax.axis(\"off\");"
@@ -2207,20 +2270,159 @@
},
{
"cell_type": "markdown",
- "id": "3fe8ac1f",
+ "id": "6bdba34b",
"metadata": {},
"source": [
- "Problem: Corrections are per-factor. We perform a total of 4 comparisons, therefore we should correct considering this number.\n",
+ "The top right group of tests with false $H_0$ is affected by type-2 errors, or equivalently by the power of the test (`power = 1 - type2_error_rate`).\n",
+ "\n",
+ "The original $5%$ significance level of the test translates into $5%$ type-1 errors that become visible when the test is applied many times as we did above.\n",
"\n",
- "Anyway, as we found significant interaction, we should rerun the ANOVA in the shape of one-way ANOVA, with one factor, for each level of the other factor, and vice-versa.\n",
+ "We want the significance level to apply to the \"whole picture\", and to control the *family-wise error rate*. Basically, we want our $5%$ level to upper-bound the risk of erroneously rejecting any single $H_0$ (or more).\n",
"\n",
+ "This can be done with a procedure called *correction for multiple comparisons*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48d0f081",
+ "metadata": {},
+ "source": [
+ "### multipletests"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "d89d9c34",
+ "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>coef</th>\n",
+ " <th>std err</th>\n",
+ " <th>t</th>\n",
+ " <th>P>|t|</th>\n",
+ " <th>Conf. Int. Low</th>\n",
+ " <th>Conf. Int. Upp.</th>\n",
+ " <th>pvalue-hs</th>\n",
+ " <th>reject-hs</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>low-high</th>\n",
+ " <td>-1.92</td>\n",
+ " <td>0.440437</td>\n",
+ " <td>-4.359308</td>\n",
+ " <td>0.000182</td>\n",
+ " <td>-2.825331</td>\n",
+ " <td>-1.014669</td>\n",
+ " <td>0.000547</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>med-high</th>\n",
+ " <td>-1.63</td>\n",
+ " <td>0.440437</td>\n",
+ " <td>-3.700871</td>\n",
+ " <td>0.001015</td>\n",
+ " <td>-2.535331</td>\n",
+ " <td>-0.724669</td>\n",
+ " <td>0.002029</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>med-low</th>\n",
+ " <td>0.29</td>\n",
+ " <td>0.440437</td>\n",
+ " <td>0.658437</td>\n",
+ " <td>0.516046</td>\n",
+ " <td>-0.615331</td>\n",
+ " <td>1.195331</td>\n",
+ " <td>0.516046</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>weekly-daily</th>\n",
+ " <td>-1.44</td>\n",
+ " <td>0.359615</td>\n",
+ " <td>-4.004280</td>\n",
+ " <td>0.000462</td>\n",
+ " <td>-2.179200</td>\n",
+ " <td>-0.700800</td>\n",
+ " <td>0.000462</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " coef std err t P>|t| Conf. Int. Low \\\n",
+ "low-high -1.92 0.440437 -4.359308 0.000182 -2.825331 \n",
+ "med-high -1.63 0.440437 -3.700871 0.001015 -2.535331 \n",
+ "med-low 0.29 0.440437 0.658437 0.516046 -0.615331 \n",
+ "weekly-daily -1.44 0.359615 -4.004280 0.000462 -2.179200 \n",
+ "\n",
+ " Conf. Int. Upp. pvalue-hs reject-hs \n",
+ "low-high -1.014669 0.000547 True \n",
+ "med-high -0.724669 0.002029 True \n",
+ "med-low 1.195331 0.516046 False \n",
+ "weekly-daily -0.700800 0.000462 True "
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.concat((posthoc_tests_sun.result_frame, posthoc_tests_water.result_frame))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2a6451f",
+ "metadata": {},
+ "source": [
+ "Problem: with `t_test_pairwise`, the correction operates per factor. We performed a total of 4 comparisons, therefore we should correct considering this number.\n",
+ "\n",
+ "Note: there is no need to perform all possible comparisons, but be honest! If you did proceed to compare, then this comparison should count, whatever its outcome is.\n",
+ "\n",
+ "Anyway, as we found significant interaction, we should rerun the ANOVA in the shape of one-way ANOVA, with one factor, for each level of the other factor, and vice-versa."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3fe8ac1f",
+ "metadata": {},
+ "source": [
"<table><tr><td><img src=\"img/two-way-anova-interaction-significant-flowchart.png\" /></td></tr>\n",
"<tr><td><a href=\"https://www.spss-tutorials.com/spss-two-way-anova-interaction-significant/\">SPSS recommendation for two-way ANOVA interaction</a></td></tr></table>"
]
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 46,
"id": "c380d42c",
"metadata": {},
"outputs": [],
@@ -2237,14 +2439,14 @@
"id": "b7ea24c3",
"metadata": {},
"source": [
- "This would eventually lead to up to 9 comparisons and again `t_test_pairwise` would not properly take this into account.\n",
+ "This would eventually lead to up to 9 comparisons, but again `t_test_pairwise` would not properly take this into account.\n",
"\n",
"We should use [multipletests](https://www.statsmodels.org/stable/generated/statsmodels.stats.multitest.multipletests.html) instead, for the purpose of correcting the $p$-values:"
]
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 47,
"id": "1b9e153d",
"metadata": {},
"outputs": [
@@ -2254,7 +2456,7 @@
"7"
]
},
- "execution_count": 45,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@@ -2280,7 +2482,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 48,
"id": "2fe99f26",
"metadata": {},
"outputs": [
@@ -2417,7 +2619,7 @@
"weekly-daily[sun=low] -1.636487 0.002279 True "
]
},
- "execution_count": 46,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -2429,7 +2631,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 49,
"id": "25b21f28-f70d-466e-bbb6-8a6fc86b3696",
"metadata": {
"jupyter": {
@@ -2462,13 +2664,27 @@
"plt.yticks([]);"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "748acdc4",
+ "metadata": {},
+ "source": [
+ "Beware: the confidence intervals from [t_test_pairwise](https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.OLSResults.t_test_pairwise.html)'s table are not corrected for multiple comparisons."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f436837b",
+ "metadata": {},
+ "source": [
+ "### Bonferroni, Šidák and Holm"
+ ]
+ },
{
"cell_type": "markdown",
"id": "aecb5d85-20d7-4261-a572-b6c891cb8aff",
"metadata": {},
"source": [
- "Beware: the confidence intervals from [t_test_pairwise](https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.OLSResults.t_test_pairwise.html)'s table are not corrected for multiple comparisons.\n",
- "\n",
"[multipletests](https://www.statsmodels.org/stable/generated/statsmodels.stats.multitest.multipletests.html) implements several correction procedures. The Holm correction with Šidák adjustments is the default method (`holm-sidak`).\n",
"\n",
"If we perform $n$ tests, the $p$-value for each test can be adjusted as follows:\n",
@@ -2514,7 +2730,7 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 50,
"id": "b350dec1",
"metadata": {},
"outputs": [
@@ -2713,7 +2929,7 @@
"[5 rows x 31 columns]"
]
},
- "execution_count": 48,
+ "execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
@@ -2725,7 +2941,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 51,
"id": "67e86aec",
"metadata": {},
"outputs": [
@@ -2748,7 +2964,7 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 52,
"id": "8bd68586",
"metadata": {},
"outputs": [
@@ -2761,8 +2977,8 @@
"Dep. Variable: Response R-squared: 0.642\n",
"Model: OLS Adj. R-squared: 0.640\n",
"Method: Least Squares F-statistic: 354.9\n",
- "Date: Thu, 23 Sep 2021 Prob (F-statistic): 4.97e-46\n",
- "Time: 14:56:58 Log-Likelihood: -103.52\n",
+ "Date: Fri, 24 Sep 2021 Prob (F-statistic): 4.97e-46\n",
+ "Time: 10:19:16 Log-Likelihood: -103.52\n",
"No. Observations: 200 AIC: 211.0\n",
"Df Residuals: 198 BIC: 217.6\n",
"Df Model: 1 \n",
@@ -2801,7 +3017,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 53,
"id": "50155198",
"metadata": {},
"outputs": [
@@ -2863,7 +3079,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 54,
"id": "bc471543",
"metadata": {},
"outputs": [
@@ -2897,9 +3113,10 @@
"source": [
"The most distant points may be outliers.\n",
"\n",
- "The dispersion of the residuals should not greatly vary with the explanatory variable(s) (homoscedasticity).\n",
+ "We expect the residuals not to exhibit any structure:\n",
"\n",
- "In addition, structured residuals, *e.g.* systematic positive-only or negative-only errors on subdomains of the explanatory variable, are indicative of the model not being flexible enough.\n",
+ "* systematic (positive-only or negative-only) errors on subdomains of the explanatory variable are indicative of the model not being flexible enough,\n",
+ "* the dispersion of the residuals should not vary as a function of the explanatory variable (homoscedasticity).\n",
"\n",
"Key criterion: the residuals should be normally distributed."
]
@@ -2916,7 +3133,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 55,
"id": "5492f4e8-2ac8-4ba7-9a6c-241f33994998",
"metadata": {},
"outputs": [
@@ -2929,8 +3146,8 @@
"Dep. Variable: Response R-squared: 0.642\n",
"Model: OLS Adj. R-squared: 0.640\n",
"Method: Least Squares F-statistic: 354.9\n",
- "Date: Thu, 23 Sep 2021 Prob (F-statistic): 4.97e-46\n",
- "Time: 14:56:58 Log-Likelihood: -103.52\n",
+ "Date: Fri, 24 Sep 2021 Prob (F-statistic): 4.97e-46\n",
+ "Time: 10:19:17 Log-Likelihood: -103.52\n",
"No. Observations: 200 AIC: 211.0\n",
"Df Residuals: 198 BIC: 217.6\n",
"Df Model: 1 \n",
@@ -2966,7 +3183,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 56,
"id": "0d7780a9",
"metadata": {},
"outputs": [
@@ -2993,11 +3210,11 @@
"metadata": {},
"source": [
"Note: `statsmodels`'s `qqplot` compares with R's qqplot as long as `fit=True` to standardize the residuals.\n",
+ "`fit=True` makes `qqplot` differs from `scipy`'s `probplot`.\n",
"\n",
- "Deviations at the distribution tails are common and not as informative (in terms of normality) as smaller but consistent deviations from the midline at or near the mode.\n",
- "Here, the deviation around the theoretical median ($x=0$) is indicative of a departure from normality.\n",
+ "The small but consistent deviations from the regression line near the distribution mode may result from asymmetries, while deviations at the distribution tails may reflect departures from normality in terms of kurtosis.\n",
"\n",
- "However, the points at the tails are of interest, because they may be outliers."
+ "The points at the tails are of interest, also because they may be outliers."
]
},
{
@@ -3018,7 +3235,7 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 57,
"id": "e79b930e",
"metadata": {},
"outputs": [],
@@ -3029,7 +3246,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 58,
"id": "3cab691b",
"metadata": {},
"outputs": [
@@ -3221,7 +3438,7 @@
"[200 rows x 8 columns]"
]
},
- "execution_count": 56,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
@@ -3232,7 +3449,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 59,
"id": "7f143230",
"metadata": {},
"outputs": [
@@ -3277,7 +3494,7 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 60,
"id": "bc655fc0",
"metadata": {},
"outputs": [],
@@ -3289,7 +3506,7 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 61,
"id": "b5b46df6",
"metadata": {},
"outputs": [
@@ -3314,7 +3531,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 62,
"id": "a0b5ffc9",
"metadata": {},
"outputs": [],
@@ -3324,7 +3541,7 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 63,
"id": "7bb8d264",
"metadata": {},
"outputs": [],
@@ -3336,7 +3553,7 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 64,
"id": "1ddf4e63",
"metadata": {},
"outputs": [
@@ -3364,7 +3581,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 65,
"id": "7a441682",
"metadata": {},
"outputs": [],
@@ -3375,13 +3592,13 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 66,
"id": "0d8019bc",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwsAAAD+CAYAAAB1EZmNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAB3h0lEQVR4nO3deXxc5X3o/89zZtdq2Za12LJlG4ONd1m2AdsEQk3IcuESsjRJ06yF0PRHcnN7m9y2vyxd7iW/JjRrA26TS+jNTUKzJ01zCYUUDBjjDdvYGOMFJEuWbEnWOmdmzjnP748zM9JIo302Sd/3C71s65yZeTQ255nveZ7v96u01gghhBBCCCHEcEa+ByCEEEIIIYQoTBIsCCGEEEIIIdKSYEEIIYQQQgiRlgQLQgghhBBCiLQkWBBCCCGEEEKkJcGCEEIIIYQQIi1vvl544cKFur6+Pl8vL4QQM97Bgwcva60r8z2OQiBzihBCTN1Y80negoX6+noOHDiQr5cXQogZTyn1Wr7HUChkThFCiKkbaz6RbUhCCCGEEEKItCRYEEIIIYQQQqQlwYIQQgghhBAirbzlLAghZoZYLEZzczOmaeZ7KHNWMBhkyZIl+Hy+fA9FCCGyQuaa3JjKfCLBghBiTM3NzZSWllJfX49SKt/DmXO01nR0dNDc3Mzy5cvzPRwhhMgKmWuyb6rziWxDEkKMyTRNFixYIBfvPFFKsWDBArnbJoSY1WSuyb6pzicSLAghxiUX7/yS918IMRfItS77pvIeS7AghChoTU1N3HzzzVx77bWsXbuWr371q8ljnZ2d7N69m1WrVrF79266urpGPP7AgQPcd999U3rtz3/+83zpS1+a1GNuuummSdX7/93vfsfb3va2yQ5NCCFEhtXX17N+/Xo2bdpEY2Nj2nMefPBBHnnkkSk9/2TnB4CSkpJJnT+VeWs8krMghChoXq+XL3/5yzQ0NNDb28uWLVvYvXs31157Lffffz+33HILn/nMZ7j//vu5//77+eIXv5jy+MbGxlEv+jOR7Wh6wjEMuQMnhBAZ9+STT7Jw4cJRj3/sYx/L4WhywI6B2T3mKbKyIISYEMt2aOocyMqXZTujvm5NTQ0NDQ0AlJaWsmbNGi5cuADAz3/+cz7wgQ8A8IEPfICf/exnIx4/9M795z//eT784Q9z0003sWLFCr72ta8lz3vkkUfYsGEDGzdu5P3vf/+I5xl6R+jy5cvU19cDEA6H+f3f/33WrFnDnXfeSTgcTj7mscce4/rrr6ehoYF3vvOd9PX1AfCb3/yG1atX09DQwE9+8pMJvf9aay71Rni9c4CugSi21hN6nBBCzCT5mmsmauid+5tuuolPf/rTbNu2jauvvpqnn34aANu2+dM//VPWrVvHhg0b+PrXvz7ieYauGPzoRz/igx/8IADnzp3j+uuvZ/369fzlX/5lymP+7u/+jq1bt7JhwwY+97nPJb//t3/7t1x99dXs3LmTU6dOTewHsSLQexGuvA7hK2OeKisLQogJae022fX/PZmV5376z26mbn7RuOedP3+ew4cPs337dgDa2tqoqakBoLq6mra2tnGf4+WXX+bJJ5+kt7eXa665hnvvvZdXXnmFv/mbv+HZZ59l4cKFdHZ2Tnjs3/rWtygqKuLkyZMcPXo0GdhcvnyZv/mbv+Hxxx+nuLiYL37xizzwwAP82Z/9GX/0R3/EE088wVVXXcW73/3uMZ/fcTR2/KvXjE14XEIIMRPlc65RSnHrrbeilOKee+7h7rvvHvc5Lcti//79/PrXv+YLX/gCjz/+OHv27OH8+fMcOXIEr9c7qTnlE5/4BPfeey9/+Id/yDe/+c3k9x977DFOnz7N/v370Vpz++2389RTT1FcXMwPfvADjhw5gmVZNDQ0sGXLltFfQDvg2HClacJjkmBBCDEj9PX1cdddd/GVr3yFsrKyEceVUhNK3HrrW99KIBAgEAiwaNEi2traeOKJJ3jnO9+ZXHqeP3/+hMf11FNPJXMiNmzYwIYNGwDYt28fJ06cYMeOHQBEo1Guv/56Xn75ZZYvX86qVasA+IM/+AP27NmT8pxaaxztBgiygCCEELmxd+9eFi9eTHt7O7t372b16tXceOONYz7m7W9/OwBbtmzh/PnzADz++ON87GMfw+t1P2ZPZk555pln+PGPfwzA+9//fj796U8DbrDw2GOPsXnzZsCdE0+fPk1vby933nknRUVuEHT77benf2LtgGOB47i/nwQJFoQQE1JTHuTpP7s5a889llgsxl133cX73ve+5IUZoKqqitbWVmpqamhtbWXRokXjvlYgEEj+3uPxYFnWhMbo9XpxHPcCO5Gyc1prdu/ezfe///2U7x85cmTMx9iOdrcYSZAghJiD8jnXLF68GIBFixZx5513sn///nGDhcScMpn5BFKrEg2fU9Ld+NJa89//+3/nnnvuSfn+V77ylbFfaGiQMEWSsyCEmBCvx6BuflFWvrye0S9FWms+8pGPsGbNGj71qU+lHLv99tv57ne/C8B3v/td7rjjjin9bG984xv5l3/5Fzo6OgDSLhnX19dz8OBBwN1fmnDjjTfyf/7P/wHg+PHjHD16FIDrrruOZ555hldffRWA/v5+XnnlFVavXs358+c5c+YMAN///vfRQMx2iFoOtjO7AwWlVJ1S6kml1Aml1EtKqU+Mce5WpZSllHpHLscohMiffM01/f399Pb2Jn//2GOPsW7duin9DLt37+ahhx5KBg/p5pSqqipOnjyJ4zj89Kc/TX5/x44d/OAHPwDge9/7XvL7b3rTm/jOd76TzH27cOEC7e3t3HjjjfzsZz8jHA7T29vLL3/5S/cB2gE7ClZ0WoECSLAghChwzzzzDP/8z//ME088waZNm9i0aRO//vWvAfjMZz7Db3/7W1atWsXjjz/OZz7zmSm9xtq1a/mLv/gL3vCGN7Bx48YRQQnAn/7pn/Ktb32LzZs3c/ny5eT37733Xvr6+lizZg2f/exnk3tFKysrefjhh3nPe97Dhg0bkluQgsEge/bs4a1vfSsNDQ0sXFjpbjtyZnGEkMoC/qvW+lrgOuDjSqlrh5+klPIAXwQey/H4hBBzUFtbGzt37mTjxo1s27aNt771rdx2221Teq6PfvSjLF26NFk0I3FDaaj777+ft73tbdxwww3J3DuAr371q3zzm99k/fr1yWIeALfeeivvfe97k8nP73jHO+jt7aWhoYF3v/vdbNy4kTe/+c1s3droriRkIEhIUDpPG2IbGxv1ZGvNCiFy7+TJk6xZsybfw5g1ppqP8MqplymuWpb887wiPwtKAge11jO6LqxS6ufAN7TWvx32/U8CMWAr8Cut9Y/SPDxJ5hQhZjaZa6ZpEtuNTp56hTXVqYneqnLVqPOJrCwIIUQOaK2xbIeo7WDZkrgMoJSqBzYDzw/7/mLgTuBb4zz+bqXUAaXUgUuXLmVtnEIIUbAyuN1oNBIsCCFEFjlaz5l8hMlQSpUAPwY+qbXuGXb4K8CntR67ZIfWeo/WulFr3VhZWZmlkQohRAHKQZCQINWQhBAiCxL9ERxZQhhBKeXDDRS+p7VO15WuEfhBvCLIQuAtSilLa/2z3I1SCCEKUAaqG02WBAtCCJEh0h9hfMqNAL4NnNRaP5DuHK318iHnP4ybs/CznAxQCCEKUR6ChIRxtyFNpMydUup9SqmjSqljSqlnlVIbszNcIYQoPJKPMCk7gPcDb1RKHYl/vUUp9TGl1MfyPTghhCgoOdxuNJqJrCwkytwdUkqVAgeVUr/VWp8Ycs454A1a6y6l1JuBPcD2LIxXCCEKRmIVYQ6VPZ02rfVeYPxW24PnfzB7oxFCiAKVx5WE4cZdWdBat2qtD8V/3wucBBYPO+dZrXVX/I/7gCWZHqgQYm5qamri5ptv5tprr2Xt2rV89atfTR7r7Oxk9+7drFq1it27d9PV1TXGM01PSUlJ8veO4yYtxyxn1EDhP/7jd9z5n2+f1Gvs/r03cvCglP8UQoh8qK+vZ/369WzatInGxuxVpf7d737H2972tvQHR1lJ+Pxf/Q1feuDvJ/U6JRULpzPMpElVQxqtzN0wHwH+bRpjEkKIJK/Xy5e//GVOnDjBvn37+OY3v8mJE+7C5v33388tt9zC6dOnueWWW7j//vuzOhbb0UQth5g9epAghBBi5nryySc5cuQIOe/bUgDbjUYz4WBhnDJ3iXNuxg0WPj3KcamJLcRMZVvQ9Vp2vmxr1JetqamhoaEBgNLSUtasWZPsavnzn/+cD3zgAwB84AMf4Gc/+9nIYds2/+2//Te2bt3Khg0beOihhwD4+7//ez784Q8DcOzYMdatW8fAwABtbW3ceeedbNy4kY0bN/LMM89g2e6F27Idfve7J1NWDD75ift45JHvAvDY//0NG9at5bptW/n5kLH09/dz9x99lJ03XMf2rY388he/ACAcDvP+972XjevX8a533IUZDk/lb0YIIWaPPM01E3Xp0iXuuusutm7dytatW3nmmWcAuOOOO3jkkUcAeOihh3jf+94HwAsvvMANN9yQ7Azd29ub8nyf/9zn+NL/d38ySFi3aQvnz78GwN/+zy9y9bXr2XnTGzn1yivJx5w5c5bb3nY7W7bfwK6bb+Hll08BcO7cea7f9QbWb27kLz/7+Qn9PANRi/Y+c8xzJlQNaQJl7lBKbQD+CXiz1roj3Tla6z24+Qw0NjbKbTkhZpKeC/DVDdl57k8chYpl4552/vx5Dh8+zPbtbkpUW1sbNTU1AFRXV9PW1jbiMd/+9rcpLy/nhRdeIBKJsGPHDm699VY+8YlPcNNNN/HTn/6Uv/3bv+Whhx6iqKiID33oQ7zhDW/gxz/5CdGYRU9Pr9sfYRymaXLvvR/j//7f37Lyqqv4g/e+J3ns/v/5P7jpppvZ84//xJUrV9h5w/W88ZZb+Kd/3EOoqIgXjx3n2NGjXLd960TfMSGEmJ3yONcopbj11ltRSnHPPfdw9913j3yKT3yC//Jf/gs7d+7k9ddf501vehMnT55kz5497Nixg+XLl/PlL3+Zffv2EY1Gefe7380Pf/hDtm7dSk9PD6FQKP5M2l1JcGzSVcU4eOgQP3j0Xzhy4Hksy6Jh+/VsadgMwN1//HEe/MbXWbXqKp7fv58/vu8TPPHYb/jEp/6Ue+++mz98//v45rceHPXn1ECPGaPPtLDHbmcDTCBYmEiZO6XUUuAnwPu11q+kO0cIIaajr6+Pu+66i6985SuUlZWNOK6UIl6XP8Vjjz3G0aNH+dGPfgRAd3c3p0+fZvny5Tz88MNs2LCBe+65hx07dgDwxBNP8O3/9TAxy0Epg/Ly8gmN79TLL1NfX89Vq1YB8J73vo9vf/sfAfj3xx/nX3/1K77y9+4l1IyYNL3+Onuffpo//pM/AWD9hg2sX5+lCVIIIcS49u7dy+LFi2lvb2f37t2sXr2aG2+8MeWcxx9/PLkVFqCnp4e+vj6qqqr4q7/6K26++WZ++tOfMn/+fI4dO0ZNTQ1bt7o3gsrKyuLbjWJugDDGdqOn9z7DnXfcTlFREQC3v+2tgDsXPvvcPt75nvclz41EIgA889xz/PjR7wPw/ve9l0//+V+mPKfDYA+g7nB0wu/LRFYWEmXujimljsS/9+fAUgCt9YPAZ4EFwD/EJ2tLa529zBAhRO6VLXbvymTruccQi8W46667eN/73sfb3/725PerqqpobW2lpqaG1tZWFi1aNOKxWmu+/vWv86Y3vWnEsdOnT1NSUkJLSwuWM5iHMFY+gtfrxRlygTfNsZdvE2P4wQ8f5eprrhn3XCGEmNPyONcsXuweX7RoEXfeeSf79+8fESw4jsO+ffsIBoMjHn/s2DEWLFhAS0vLyCcfWt1oyErCiDklMvac4jgO8+bN48iB9OnDw2+aadzKfY5256KpmEg1pL1aa6W13qC13hT/+rXW+sF4oIDW+qNa64ohxyVQEGK28Xjd5dtsfHlGv2+hteYjH/kIa9as4VOf+lTKsdtvv53vftfNF/jud7/LHXfcMeLxb3rTm/jWt75FLBYD4JVXXqG/v5/u7m7uu+8+/v3J33Hp8mX+5dEfoTXcfPMb2fOQu3xr2zbd3d0pz7d06TJOnjxJJBLhypUrPPnkEwBcs3o1r732GmfOnAHghz/8QfIxv7d7N//wD99MXqiPHD4MwM5du/jhD9zzXjp+nGPHsjRBCiHETJGnuaa/vz+ZT9Df389jjz3GunXrRpx366238vWvfz355yNHjgCwf/9+/u3f/o3Dhw/zpS99iXPnznHNNdfQ2trKC/ueBStKb3c3lpWaN1G/bBmHDrvPcejwYc6dOw/Ajbt28rNf/JJwOExvby+//NdfA+7qxPL6ZfzLj34MuHPkiy+6c8eO66/nBz/8FwD++fvu3BKznXij0Knv/p9UNSQhhMi1Z555hn/+53/miSeeYNOmTWzatIlf/9q9aH7mM5/ht7/9LatWreLxxx/nM5/5zIjHf/SjH+Xaa6+loaGBdevWcc899xCJxvjEJz7JPR+7l6uuWsWDD/0jf/mXf057eztfeuDv+Y/f/Y4tmzdx/fZtnDx5IuX56urqeMdd76Bh80be997fZ9PGTQAEg0H+4R++xZ133M5127amrHL8+V/8JbFYjMaGzWzeuIEvfOFzANx9z8fo7+tj4/p1/NUXPp9M5BZCCJFbbW1t7Ny5M5mI/Na3vpXbbrttxHlf+9rXOHDgABs2bODaa6/lwQcfJBKJ8Ed/9Ed85zvfoba2li9/+ct8+MMfxmdofvi9R/h/PvFJNm7Zxu43v3XEavRdb//PdHZ1sXZjA9/4hwe5Or6VtWHzZt79znewccs23vyf7mDrli3Jx3zvuw/z7Ye/y8Yt21i7sYGf//JXAHz1gS/xzQcfZN3mRpqbL2TsvVHTiTSmo7GxUee8LJUQYtJOnjzJmjVr8j2MadPxBmq21u667AzzyqmXKa4aTMybV+RnQUngoKzkumROEWJmmy1zDY4dT1rOXfnTxFajiRTjADj1ymnmhVK/t/TqTaPOJxOqhiSEEDOVEw8QpC+CEEKIrMlDkOBosPX0thhNhAQLQohZR+vBACFPi6dCCCHmAsd2E5dzNNlMdhUhEyRYEELMGokLqKwiCCGEyBqtQduj9kjIhkTZUycPd8AkWBBCjEtrnbaHQaGw43Wj85WDlW3uz1W4778QQmRCoc81uQ4SBsueZm6VfCrzpFRDEkKMKRgM0tHRUXAfxLXWWLZDxLKxbKfgxpcpWmu6ujoxfP58D0UIIbKmUOcawA0MbMvtuGxnf8uRA1iOHlL2NDPP684nV/BM8tO/rCwIIca0ZMkSmpubuXTpUr6HArgXu+k0l5l5FIbPT6B8YbqDPqXUk0AV7k2oPVrrryql5gM/BOqB88C7tNZdORqwEEJMWqHNNUB8JcHJWdKygzu3ZXN68xhQ7JvcarUEC0KIMfl8PpYvX57XMWit6Y1Y9IRjRK3cVZqYIf6r1vqQUqoUOKiU+i3wQeDftdb3K6U+A3wG+HQ+BymEEGMphLkmybYg3AWRnqyvIkRth76IxUDEQuesrvfktnpJsCCEKFhRy6HXjNFrWnlJ6poBYlrrQwBa616l1ElgMXAHcFP8nO8Cv0OCBSGEGJsdiwcJvVkNEjSa/qhFf8QmatlZe51MkWBBCFFwBqIWPWGLgaiV76HMGEqpemAz8DxQpbVujR+6iLtNSQghRDpWJB4k9GX1ZWKOQ5/pzm0z6QaYBAtCiIJgOzq5ihCzZavRWFQ4jP9XP2EJ1AAopUqAHwOf1Fr3DK0morXWSqmZMysJIUSuxEwId0J0IGsvodGEozZ9UYtIrPBXEdKRYEEIkVdmzKbHjNEfsedQ0vLUBQ4fpPpdd4BlUQW1SikfbqDwPa31T+KntSmlarTWrUqpGqA9fyNOpZSqAx5hWFL2sHPeh7ttSgG9wL1a6xdzPVYhxCwVHXBXEmLhrL2E5Tj0R2z6IxZ2Drs6Z4MEC0LMRjETPD4wPPkeSVpaa/oiFj3mzL3Tkg8qHKb6XXfg6e4GSKTCfRs4qbV+YMipvwA+ANwf//XnOR3o2CzSJGVrrU8MOecc8AatdZdS6s3AHmB7PgYrhJhFIn1ukGBFsvcSlk1vxCI8i7bRSrAgxGzi2DDQAWYPzFtacMFCzHboNS16zVhOW9XPFkW//iU6NjgB9bu/vB84ppQ6Ev/2n+MGCY8qpT4CvAa8K5fjHEs8l6I1/vuhSdknhpzz7JCH7AOW5HSQQojZJdIbDxKiWXn6mZawPFkSLAgxG2gN5hUY6MxZ6/nJkITlzPCdP4cnPLi3tgTQWo9WA++WnAxqGoYlZY/mI8C/5WRAQojZQ2u39Gm4yy2FmgW21vSZ1qzYajQWCRaEmOmiA9B/yS35VkBsx72I9pgxSVjOkFj9cuxQEd6B/nwPZdqGJ2WPcs7NuMHCzlGO3w3cDbB06dIsjVQIMaMkbp6Fr7ir7VkQtW16TYuBqA05642QP5Ns+CyEKBh2DHpaoaeloAKFiGVzqTfC650DdPRHJFDIoIG33o7yzfx7PKMkZQ8/ZwPwT8AdWuuOdOdorfdorRu11o2VlZXZG7AQovA5tru63nUO+juyEigMRC3aek3aesz4SvnsDxRAVhaEmHm0dpdVw10Fs+VIEpZzQweDXHz05/FqSDb0Z7cmeDYot65ruqTsoecsBX4CvF9r/UouxyeEmGHsmLuKkKVuy47W9Ecs+iIWljM3b35JsCDETBLpg4HLWdt/OVmSsJx7kc1beP3oaSr//d9o+/AHWvI9ninYQfqk7KUAWusHgc8CC4B/iPeMsLTWjbkfqhCiYMXCbpAQzc62zEQDtf6IhZ4jKwijkWBBiJnAirp5CVmsCT0ZkrCcXzoUou/t76L5wx9oHf/swqK13ovbP2Gscz4KfDQ3IxJCzCiRXjdIyFL5UzPmNlCbTaVPp0uCBSEKmeO43SXN7rxvOXIcTa8kLOdVW4/J/nOd7DvbyeGmrnwPRwghcsNxINLtzoVZWFlPlD7tMy2Z39KQYEGIQmX2uFuO8rxHMmo5dIdj9EcsnALJkZgrLNvhpZYenj/XyfPnOjl3eeZXQRJCiAmzLTdAMK9k5YZZostyXyQ2t+c3e+z+E+MGC0qpOuARoAo37XuP1vqrw85RwFeBtwADwAe11oemOGQh5raY6W45ymKHyYkIR226wzHZapRjnf3ReHDQwcHzXfRHUxPGS4NettbP5+ZrFvGHX8zTIIUQIpusSDwfoS8rQULEsumLzJ3Sp+moSC+hlucINT9DsGXfmOdOZGXBAv6r1vqQUqoUOKiU+q3W+sSQc94MrIp/bQe+Ff9VTIHjaM539NPWY1JVFqR+QTGGMeYWXzEbODb0X3b3Y+ZRf8TiSjgmVY1yxHY0py728vy5Dp4/18krbSMrHF21qITty+ezffl81tSU4TEU84r8eRitEEJkUbTfDRKykJ+n0QxE3SBhNnZZnghPfxuh5r2EmvcSaDuC0hN7H8YNFrTWrUBr/Pe9SqmTwGJgaLBwB/CI1loD+5RS85RSNfHHiklwHM1vXrrIpx49ghlzCPoMHnjXJm5bWy0Bw2xVAN2XHUfTG7HoCUs+Qi50h2McON/F8+c62H+ukx4zdfWmyO9hy7IKti+fz7bl81lYEsjTSIUQIsscxy17anZnpWeQnSh9as7uLstpaY3vyhk3QGh6Gn/X6ZTDjsdPpLqR8JKdwF+O+jSTyllQStUDm4Hnhx1aDDQN+XNz/HsSLEzS+Y7+ZKAAYMYcPvXoEVbft4sVlSV5Hp3IuDx3X7YdTXc4JqVPs0xrzZlL/Tx/roN9Zzs52drD8Ld72fwiti2fz/YV81m/uByfR3pmCiFmMSs6GCRk4UZZzHFLew/MtdKnjkWg/WhyBcHbfzHlsO0vw1xyA+ElOzFrtqK9ofiRDAQLSqkS3I6bn9Ra90xh+Cil7gbuBli6dOlUnmLWa+sxk4FCghlzaO81JViYTeyYu+UoS/Whx5NIWu6LWOi5nNSVRQNRiwOvdbH/bCfPn++koy81gczvNdhcNy+5elA7LzTKMwkhxCzhOBDtdQt4ZCkvbzAfYe7k2ykrTLBlP6HmvQQvPIcnmvox3SqpIbxkJ+ElO4lUrgdjcvWNJnS2UsqHGyh8T2v9kzSnXADqhvx5Sfx7KbTWe4A9AI2NjfIJJY2qsiBBn5ESMAR9BotKg3kclciYPHdfNmM2VwYkaTkbtNa83jmQrFx0rLkba9jyQU15MBkcbK6bR8DnydNohRAih2Kmu4oQ6c3a3BeOWfSYcycfwQh3ErrwrBsgtB5AOak3pKLzVxNesoPwkp3E5q0ANfWt7BOphqSAbwMntdYPjHLaL4A/UUr9ADexuVvyFaamfkExD7xr04ichfoFxfkempiuPHZf7otYdEvScsaZMZsjTVd4/qwbIFzsMVOOew3FhiXl8eTkBdTND6GmccEWQogZQ2s3ODCvuFuOsvES8f4IvWELK89lxnPB2/M6oSZ3e5H/8kuoIdurtPIQqdrsriDU7cQuWpS5153AOTuA9wPHlFJH4t/7c2ApgNb6QeDXuGVTX8UtnfqhjI1wjjEMxW1rq1l93y7ae00WlUo1pBkvT92XNZqecEySljOs5Uo4uXpwpOkKUSv1vV1Q4k8GB1uWzaPIL+1shBBziOO4AYLZ7Vb5ywLLceiL2PTP9v4I2sHfcZJQ09OEmvfi63k95bDjLcKs3e4GCIuvQ/tLszKMiVRD2guM+Uk1XgXp45ka1FxnGIoVlSWSozDT5an7sq01fabFFWcAW/ly9rqzVdRyOHahO945uYOmrtSgz1CwtraM7csXsG35fFZWFsvqgRBi7klU9gt3Za2ZqBmz6YtahGfzVlo7QvDioXiC8jN4zM6Uw1ZoIWZ8e5FZtRk80yuj7Who6zZZXL9y1WjnyC0vIbLB7IGBjqzdVUknatv0mTb9UQvQ2D4NsiV+Si71RpKN0Q69doXwsO1b80I+t3LR8vlsWVZBWUiCMiHEHKW1m48w0JmVOc/RmoGYW/p0tq6SDzZI20uw5XkMK/WmVKx8eXJ7UXT+NaAyUy3P0XDgfBcPPXWGS6YqG+08CRaEyCQrEt9yZI5/bgZoNOGoTe8cbjKTCbajOdHSw754Y7Szl0ZWqbqmujTZGO2a6lIMWT0QQsx1Zo+7gp6FXLyY49BnWvTP0tKnnr6Lgw3S2l9MaZCmlUFk4TrMOreCkVW6JCtjaOs2eeipM8Tssd9fCRaEyATHdu+qmN05eTnLceiP2PRH5mCTmQy5MhBlfzz34IXzXfRFUie74oCHrcvcvgdb6+czv1g6JgshRDJxOdyVlR5BZsy9AWbGZtlWI63xdZ1Obi8a2SAtgFmzFXPJTsKLb8AJzsv6kK4MRMcNFECCBSGmJwd7NIcarB9twyy805JNjtacbutj31l39eDUxd4R7+CKhcXJ7UVra8vwSmM0IYRwOQ5EuiF8JePbjRJVjWbdViPHItD+YryD8l68A20ph+1AOebiGxhYspNIzVa0N7dl8ucV+fF5lKwsCJEVyYtm9oMER7sX0f7ILLuI5kCvGePga108f66T/ec66RpIvQsW9BlsWVrB9hXz2VY/n0Vl0s9ECCFSZPGmWGKr0UDUmjVVjVRsgGDrfkJNewm2PIcn2pty3CqpHdIgbd2kG6RlUlV5kHtuXMlDT50Z8zwJFoSYDMd2txqZV7IeJERtx11FmKX7NbNBa83Zy/3JvgcvtXQzrC8aSypCydyDDUvm4ffK6oEQQoygtTvfhbsyvpIQjln0RexZs9XICHcQan7GTVC+eChtg7SBup2YS3YSK18+rQZpmWQoaKyvoK5iHXd9T/eMdp4EC0JMhGO7F8wclEGdtfs1syQctTn0urt68PzZTi71RVKO+zyKTXXz2L58AduXz2dxRShPIxVCiBkgS30SbK3pj7ir5LOhgZrbIO3peIO0E6kN0gwvZtVmwkt2YS7ZgV1UmceRjk1rTY9l0Hz+zOnRzpFgQYix5DAnYSBqSVWjCdBa09w12BjtaPOVEfstF5UGuG6FGxxsWjqPkE9qyAohxJgSQUK4K6M3xWZNrp128F9+KZmgPKJBmq+YcO11bv+D2u1of2H2ytJac/6KxaHWCIdaTA63RuiNSs6CEFNjRaGvzS2HmiVjtapPNEq5MhBlXpGfqvIgc7WRdyRm82Jzd7L3QcuV1NK0HkOxfnG5u71oxXyWzS+SxmgFSilVBzwCVOF+ctijtf7qsHMU8FXgLcAA8EGt9aFcj1WIOSELN8USc1t/xJ7ZN8ASDdKaniZ04dk0DdIqCS/ZQbhuF5FFm8BTeD13tNa09NocjAcGh1ojdIYn9/cswYIQw2Xp7krKS2hNX8Si10zfqn5oo5SYrfF5FPfcuJLG+oo5EzBc7DHjuQcdHH79ChEr9eI2v9ifzD1oWFZBSUAuZzOEBfxXrfUhpVQpcFAp9Vut9Ykh57wZWBX/2g58K/6rmCTH0Zzv6Ketx6SqLEj9gmKMuXIREWOzY+5Wo0hPxoKE2VDW24j0ELzwHKHmpwm2vjCiQVq0fDnhul2El+wkNv+agsk/GOpSvxscuKsHEdr6RwZs80MGDTUBttQGaagNcN0XR38+mV2FSEgmc3VmbcuRrTV9phskjJW0PLxRSszWPPTUGeoq1lEzb3ZW7LFsh2MXupPbi17rGEg5roA1NWVsX+EGCFctKpHGaDOQ1roVaI3/vlcpdRJYDAwNFu4AHtFaa2CfUmqeUqom/lgxQY6j+c1LF/nUo0cwYw5Bn8ED79rEbWurJWCYy6L97lwXHRj/3AkyYzb90Zm71cjT1zqkQdrRkQ3SKtcTXuIGCHZpbR5Hmt4V0+Zwa4SDLe7KQVP3yJzHUr9i85DgYFm5d8Ir8BIsCAFug5mBjqx0oQSI2ja95sQvpOkapcRszZVwdFYFCx19kWRjtAOvdcXfn0FlQW+y70HjsvmUFxXeEq+YOqVUPbAZeH7YocVA05A/N8e/J8HCJJzv6E8GCgBmzOFTjx5h9X27WFFZmPupRZZo7a4ghK9krJFazHEYiNgMRGdgwnJKg7S9+LteTTnsNkjb5uYfLL4+Jw3SJqM/6nDkortqcKg1wqudI/9OQz7FxqoAW2oDNNQEuGqBb8o32CRYEHNbtN8NEqzo+OdOkq01A/E7LZPds5muUYrPo5gXmtldhG1H8/LFnmTlotPtfSPOWbWoJLl6sLq6DI/cAZ2VlFIlwI+BT2o9esm+cZ7jbuBugKVLl2ZwdLNDW4+ZDBQSzJhDe68pwcJcYnbDQGdGKhvZWhOOzdBchAk0SAsv3kG4bieR6sacN0gbi2k5HG+LcjC+rejU5SjD+6j5PbC+KhDfWhTgmoV+vBmaPyVYyBHZN1pgYqYbJMTC4587CY7WDMTcACESm/qFdGijlKE5C1XlhXPxmqjugRgvvOYGBy+c76THTF29KfJ7aFxWwfYVC9hWX8GCkkCeRipyRSnlww0Uvqe1/kmaUy4AdUP+vCT+vRRa6z3AHoDGxsaZt/chy6rKggR9RkrAEPQZLCqdedcRMUlaQ7TPDRKmuZKg0YSjNgMxm/AM22akYgMEW553A4SW5zCiqTeoYiWLCde5DdKiC9eBURiV82K25uSlRHBg8lJ7lGFxPx4Fayr9yZWDtYsCBLzZ+VwpwUIOyL7RAmJF3SAh2p/Rp02UhgtH7Yw0UBvaKOVKOMq80MyphuRozavtffHVgw5OtvaOeEeWLSjiuuXz2b5iAetqy/B6pDHaXBGvdPRt4KTW+oFRTvsF8CdKqR/gJjZ3S77C5NUvKOaBd20aMffULyjO99BEtiQbh06/R0LEsumP2IRjM6u7shG+PKxBWmqwFFmwJtlB2SqvL4gEZdvRnO6IJYODo21RTCv1PVfAqgW+5MrBhqoARf7czJ0SLOSA7BstALblJi6bU9rtkFa292saCmrmBWdEjkJfxOLQa13sO9vJ/vOddPanbusKeA02Lx1sjFY9A1dIRMbsAN4PHFNKHYl/78+BpQBa6weBX+OWTX0Vt3Tqh3I/zJnPMBS3ra1m9X27aO81WVQqq9qzlhVx8xGifdOq4heLVzMKz6Q8BK3x9rxGqCmeoNxxIvWw4cWs2hIPEHbgFC3M00CHjElrznVZHGo1OdgS4cjFCH1peh3Uz/PSUBtgS02QTTUBygL5ubEmwUIOZHPfqGxvGodjuxdQ80pGyqAmakdPd5sRzOw+ClprXuscSJY2PXahB9tJfX9ryoPJxmgbl5QTkMZoAtBa78W9STbWORr4eG5GNLsZhmJFZYncmJqNEluNzG53a+1Un2Ym9kRwbPyXTyQTlH29TamHfSXxBmk7MBdfh/bldzVNa01zj5WsWHS4NUKXOTIYqy31uNWKagJsrgmwoKgw5k0JFnIgW/tGZXvTGLR2+yRkqFdC1Lbpi9gMRKyMbDOaiX0UwjGbI69fSTZGa+tJbVbn8yg2LJmX7H1QN78oTyMVQohZzLbcykbT3GqUKOXdF0nf76fQKCtC4OKBZAdlT+RKynGrqDJZ3jSyaGPeG6S19cWDg3hScnuaXgcLiww21wTZUhtgS02A6tLcfizXRgDtDeCMk8wtwUIOZGvfqGxvSiNRHi4DlR8SycrZuNsyU/ooXOgK8/y5Dp4/18mRpisjyrlWlgSSlYsallYQ8hfGXRAhhJhVtHZz7SI9bmGOaXy4j9p2snFaJm5+ZZMR6R5skNbyAoaduoISnbfC3V5Ut4tYxdV5zT/oCtvJDskHWyI094wsxV4eMNhcM1ixqG4SvQ6mSxs+tMeP9gTQniDaGwA1sW1NEizkQLb2jUpZvCG0dnslhDun3SshHK9mlM3mMoXaRyFqORxtvpJsjNbclVotylCwtrbMzT1YMZ8VC4tzdqETQog5x7bcbbTT7LKczZtfmebpa0muHqRvkLYhmaCczwZpvRGHFy8OBgdnu0ZWnSryKTZVu1uKGmsDrJg/9V4HE6Ux0B4/ePzx4MANECYaGKQjwUKOZGPfqJTFw714RrrdvIRpLsf2Ryz6I7lJ6iqkPgrtPWYyODj0eteIALSiyJdsjLZlWQWlQWmMJoQQWWVF40FC77RWEWZEZ2Wt8XW9kkxQ9l85k3LY8QQxa7fGG6TdgBMoz8swwzGHY21RDsUrFp3qiDEsVQ+/BzZUBWiIlzPNZK+DdDQKDD+O110p0J4AeDL/OUKChRlsTpfFSyQuR7qndbdlsp2VMyWffRQs2+Gl1p54cnIn5y6nlpFVwDXVpVy3Yj7bly9gVVVJ1u+ECCHEnJdMWO6ZVg+gGdFZ2bEItB1JJih7B9pTDtuBeYSX3EB4ya54g7Tc99+J2poT7VEOtZocaonw0qUoVppeB2sX+ZPBwdpFAfyeLAYHypsMCrQ3iPYEc7L1SoKFGWxOlsWzY/EgoWdad1vCMYveiDXtikZTles+Cp39UV4438m+s50ceK2T/kjqz10S8LK13m2MtrW+goqimd0pWgghZgzbcpOVIz1TXiHPVEPQbFKxfrdBWtNeQi37MGLDGqSVLolvL9pFdOG1OW+QZjmaVzpiHGpxg4OjbVEiw7YLK+DqhT621ARoqA2yvspPkS875Uw1akhQ4P6KkZ+P7eO+qlLqO8DbgHat9bo0x8uB/41bI9sLfElr/b8yPVCR3pwpi2dF3MpGkb7xzx1F1HbcXISIha3zf7clm30UbEdzsrWX51/r5flznZxq6x1xzorKYrcx2vIFXFtbhmc2B5lCCFFoov1ukBAdmNLDJ9tZOR/luo2BIQ3S2tI1SLs2nqC8E6tsWU4TlB2tOdsV41CLm3dwpDVCf2zke7h8ntctZ1obYFN1gNIs9TpwE5ADKQFCITSMg4mtLDwMfAN4ZJTjHwdOaK3/k1KqEjillPqe1jo6yvlCTFx0wA0SprEkOxB1VxEKPalrunoiDvubTZ5rMnn+gkm3eSHleNBnsGVZBdctX8C25fOpLM39sq4QQsxpjjNY9tQemRA7EVa8cVr/JG585axct9Z4u88ntxcFOk6mHjZ8mNUNboCwOLcN0rTWNPVYyeDgUGuE7jS9DhYneh3UuonJ80OZXeFI5Bloz9DqRIGcr6RMxrjBgtb6KaVU/VinAKXKLYlSAnQC0ytHI0Sk191uZEXGPTUdJ56w3JejhOV80FpzpjPGs00m+5pNXmqPjki2qqsIsX3FfK5bvoB1i8vxe/PT/VEIIea0aebZJbYZhaMOZmzyH7GyWq7bsfFffikeIDyNrzf1RpXjKyG8+DrCS3Zh1m5H+3LXg6etz4onJLvBQbpeB5VFHrdLcjzvoKokc1t9UrcSuZWJMPwFs2IwUZl4R74B/AJoAUqBd2tdAHs8xMyTgUYzMcdxE5ZnQP3oqRiIOhxoifBck8m+5jCXB1L/V/N7YHNNgBuWBNhVYzNv+ZYZd1ESQohZI1H61OyeUp5dxLLpi0y/CEemy3Ury3QbpDXtJXTh2TQN0hYRrhvSIC1He+07w/bgykGLyYXekZ8lyoNGss9BQ02AJWWZ63WglQftDaG9QbfR2QwMDNLJxN/em4AjwBuBlcBvlVJPa617hp+olLobuBtg6dKlGXhpMSvYVjwfYepJy2bMpjdiTemOSyHTWvN6txUPDkxevBgZUY2hpsTD9XVBblrUzzZ9lNL2gwRfPoDnSBdN7/0PYhVX5WfwQoiscRzN+Y5+2npMqsrmQHGLmSY6EM9H6B//3GE0mv6oRZ9pEbMzc+81E+W6DfNKvEHaXoKt+zHs1JX/aMVVyf4HsYpVOfmQ3Btxko3QDrWYnLsy8jNAsU8lG6Ftrg2woiJzvQ40BtoXQntDON5QVsqWFoJMBAsfAu7XWmvgVaXUOWA1sH/4iVrrPcAegMbGxtl321dMjm25TdSmWEc6GxfUQhCxNIdb3dyD55pNWofdGfEo2Fgd4MbFsDt0irqew4QuHsB37tyI5wq27JNgQYhZxnE0v3np4oiy2betrZaAIZ+0dm96ha9MKR/Bchz6Ijb9kRjONKr9pTPVct2e3pbk9qLApWOoIRtHtPIQWTSkQVpJTUbHnM5AvNfBwRaTQ60RXrkcG7HeEvAoNlT72VITYHNtkKsX+DLW60AbAbQ3gONxf8UzN3L/MhEsvA7cAjytlKoCrgHOZuB5xWzl2DDQOeWVhERyV18WLqj50tJr8XyTybNNJodaTaLDVk4XFBlcv9jPW+a30Gi/SNmlAwROHB9RWcLxFhGp3oxZ3UjfVbcTm39NDn8KIUQunO/oTwYKAGbM4VOPHmH1fbtmf2W8QuTY7iqCeWVK+QhR26HXjGW138+Ey3Vrja/zVDJB2X8l9eOc4w1h1mx18w8WX5f1BmkRS3Piktsh+VBrhBPtUYbtpsJruL0OttS4SclrKv0Z6XWQWp0okLOeBoVoIqVTvw/cBCxUSjUDnwN8AFrrB4G/Bh5WSh3DLUH7aa315ayNWMxcWrvbjcJdUwoS8tVALRtituZoW4R98dWD14YtnRoKrq3086aqHn4v8BJLew4TbDuIp6k75TytDKLzV2PWbMWsaSS6cG1yb2istG7OXtiEmM3aeswRndbNmEN7rynBQi5ZEXcVIdo3zTktN9tnRy3XbccItB+Od1B+Bm/4UurhYAXhxTsI1+3CrG7I6t10y9GcuhxNBgfH2iIjbp4ZCq5Z6KOhJsiW2gDrq/wEp1m8QytPSlBQ6NWJcm0i1ZDeM87xFuDWjI1IzE5mt7uaMIXE5XSlT/NRL3q6LvfbPNdssq8pzAstEcLD6jmXBw121Wj+c9kpNlsvUnbpIL7Tr494nljJYiI1jZjVjZhVDehAaa5+BCFEAagqCxL0GSkBQ9BnsKh0+j1bJBdiAqL9bpAwxZLeEcumx8x8jt1k5kUV7XMbpDUnGqSl5lbEypYSXrKD8JKdRBdkr0GaE6/qlwgOXrwYYSBNr4MVFb5kQvKmmgAl/qkHByOanXkC4PFN58eY9aSDs8gex4m3rr8C1uTaboxV+jRn9aKnyXY0L12Ksq/JZF+TyenOkXtY1ywwePvCJm7yHqeu5xCBiydQrakBleMvwaxqcFcPqrdil9bm6kcQQhSg+gXFPPCuTSNyFuoXFE/reSUXYgzT7I+QyLHrj9hZ6fkzkXnRM3CJYPMzhJqfJth2GOWkBiuRhWuT+QdW+bKMjxHivQ66LQ7Gy5kebo3QHRm5dauu3JusWLSpOkDFNHodaOUdXDEosGZnM4UEC7NAQd0JSgQI0T73rssklmYdrQnHbPdrjK1GWa0XPU1dYZv9F9zgYP+FCD3DLoIlfsVbqq7wtqITbIgdoezyYYzXUrtSa+UhsnAtkcTWovnX5K3FuxCi8BiG4ra11ay+bxftvSaLSjNz3ZdciDTsWDwfYWqlT6O2Q1/EIhy1sppjl35efJWrvSXUdb8Qb5D2cspjtOEfbJC25AacUHYapF3sHQwODrWaI0p+Aywqjvc6qAnQUBtgUfHU5zxtBHC8wcE8A1k1mDb5BDLDFcydoCnWkp5su3rIfL3o6XC05nRHvDFak8nJS9ERP8G68gjvmX+KHcZxFncfxnfpwojniZUtdbcV1TQSqWrIadMaIcTMYxiKFZUlGf0QL7kQQ1iReEnvvvHPTSPX5bwT86KBwzp1jh3GS+zwHGfxUx0p59n+Usza6wnX7cSs2ZaVuaZjwOZwayIp2aQlTa+DefFeB24ztCCLSz1T6nXgrhoEh+QbBEBJ89FMk2Bhhsv7nSAr6l5QJ5HgpdGYMZuBqBsgTLZ5WibqRU9Hb8ThQItb2vT5ZpPO8LDVA6/Nuxa+zm3Bl7g2coTiKy+jWlPPsf1lRKq3xLcWNWKXVOdk7EIIMZps5kLMGLFwfE4bmPRD81XOW1km9T0H+LTv11ynXmKeSs0/sIqqCNftzFqDtJ6Iw5HWCAdbTQ61RDifptdBiX+w10FDTZDlFVNrhJYsXeoNyqpBDkmwMMPl7U7QFC6oEcumP2pPezl2qvWip0przbkui+eaTZ5rCnO8bXjpNs31pZd4Z9lJruMo1d0vYlxJfV+04SVSuR6z2t1aFKtYJZUWhBAFJVu5EDNCpNdNWrYi4546XNR2GIhmpz/CaAzzCqELzxJs3kuw9QUMO8LVQ26ov6oXE63bxbz1u7ErrsroHv2BqMOLbe62ooOtEV7tGNnrIOhVbKz2s7kmSGNtgKvm+/BMcreDxnBXDbwBtCfk9jWQVYO8kGBhhsv5naBYGAY6IGZO6PSobTMQdRiIWNg6M3daJlwvehoGYg6HWiLsa3ZXENr7U5dRF3l6eU/FKX7Pf5yrw0cIhNshdbWXWHl9fGvRViJVm9DeUOYGKMQMpZT6DvA2oF1rvS7N8XLgfwNLceeoL2mt/1duRzk3ZSsXomA5DkTi+Qj25LYL2VozEHVLeWcjYTkdb2+z2z25aS+By8dHNEgzF22kfcF2XitvJDi/jqryIHYG/uoiluZ4eyLnIMLJSyN7HfgSvQ5q470OFro7ACZDG360ZzDfYK40PJsJJFiY4XJ2J8i2oP/ShFrXR23HzUOIjqxklCmj1ouehqbuGM/Fcw+OXIwwdMHGh8UtRa/y9tITbLGPMr/vNKov9WppBysw41uLItWN2EWVGRubEB5D4TEUXsPAYyiCvhl7h+1h4BvAI6Mc/zhwQmv9n5RSlcAppdT3tNaTK6kmpiQbuRAFZ4o5do7WDMQswlEHM5aDfj/awd95imDTXoqa9+LrPpc6Hm8Is2Yb4SU7MRdfjxMowwCWT/NlLUfz8qUoh+J5B8fbR/Y68Ci4ZqHfLWdaG2D9ogAB78SDg9TypW6VIlltL1wSLMxwWb8T5NhuubiBzjEvqtkuC5cNEUtz5KLbGG1fs0lzz9A7S5prjAu8q+wEb/AeZ3n4OB7bhCE90bThJ7JoQzLvIFaxUpZIxaQopfAohccT/9VQeA33z15DYaj4nw01pf29hUhr/ZRSqn6sU4BS5f7AJUAnkJssUTG7xUw3SIj2TypICMfcFYScNAS1YwTbDrsrCM178YZTe9zawfnJ/geZapDmaM2rHbFkcPBi28g+QACr5vvYHC9nurE6QPEkeh0Mli8dLGEq5UtnjrwFC/0Ri7OX+mb3EmeOZOVOULTf3cM5zkU1Ytn0R2zCseyWhcuUtj7LXT1oNjnYEsG0Bse8gG7eFHyJtxWdYKN1lOLoZRi22ypacVWyalG0cqO7VCpEGl7DSH7oTwQBhjH0z8ak9/DOEd8AfgG0AKXAu7VOv4dRKXU3cDfA0qVLczZAMcNEB9wcuwk2UdNoIjEnWcY7U1toR6OifYRa9rkBwoV9GFZqzpvbIG0n4bpdRBesmfZNKa01r3VbyW1Fh1pMeqMj5+9l5d5kcLCpJsC84MTu/I9cNQhI+e8ZLm9/e2cv9/OWrz0tDV8KieMMLs2O0Wk5kcwVzuI2o0yxHM2xtijPNYXZ12RybkiVhgBRdhmnuKP4BDs8x6gxz7oHhlyn7dCCZN6BWd2IE5qf459AFDKfx8DrUfg8Bj7DwOd1gwCfZ/asBOTBm4AjwBuBlcBvlVJPa617hp+otd4D7AFobGws/LsVIreiAxDunFCOneU4mJbtBglTqNI3WZ6BdkLNzxBqeppA+5GUBmkaRTTRIK1uJ1bZ9APhll4r2efgYEtkRBU/gKpiD1vipUwbagIsLJ5gcJDS9Cze20Cuf7NKXkM9afhSILR277qEu0ZdRYjaDpGYTX80tyXhpqJjwOb5eGLyCxdM+pPLqZo16nVu8R3nzaHjXBM9iVdHIYb7BTieIJGqjZjVW4nUNBIrXy4XvTnOOyQI8A8NDiQgyJYPAfdrrTXwqlLqHLAa2J/fYYkZwXEg2uve9LLGTnNxdGL7bA7mNa3xXTlLqHkvoeZn8HemaZBW0+gGCItvmPaNqcv9trtqEC9n2to38gbg/JDb62BzTYDG2iA1E+h1kLpqEIjnGsiqwWyX97/hOdvwpRA4drxcXNeIlYREs7RwzL3Tku1l2OmwHc3Ll6PJ7UWnLseSxxbRxZuMY7wl9BLbOUapfcU9EK+Op1HE5l+dXDmIVK4DT276NYjCYCRyBYasEEhAkFevA7cATyulqoBrgLP5HZIoeLGwO59FesfNR7Ach75IDkqdOhaBS8fjAcLTePtaUw7b/lLMxTcQXrJj2g3Sus14cBDfWvR698g0n9JEr4PaIFtqAywrH7/XQWrTs2C86ZlcE+eavAcLc67hSyFIdKZMk4+QqV4I2dZt2uy/EOG5JpP9zSbdETeYCWFyk/EyN3mPc4v/OHXW6+4DhsRCVtEizJpGzJptRKoacILzcv8DiKwbGgR4DDeBOF0egQQDuaWU+j5wE7BQKdUMfA7wAWitHwT+GnhYKXUMUMCntdaXR3k6MZfZsXiA0DNu6dPEDbD+qJ22kpGjoa3b5MpAlHlFUy/HrawwwdYX4vkHz+GJdKcct4qr3dWDJTuJLNow5bvy/VGHFy9GkgHC6c7YiHNCXsXGardaUUPNxHodaOVBe0M43pBb7luangnyHCzMqYYvhcCKuj0ShpU/zelS7BRprTndEUv2PThxKYqjQeGwVp3nfZ5j/F7gOBucU3gThVPivzjeEJGqzcnVA6tsqdwZmcEkCJjZtNbvGed4C3BrjoYjZprENqNI77i5CLbWhGMWkZhb6nS0G2COhgPnu0Y0+mysr5hQwOA2SHuGUPNeAq0vYNip25+i869OBgixeSunNP9ELM2xRK+DlggvXx7Z68DvgXWL4l2SawOsqfTjHS84wHDzDLwhHF+R9DYQaeUtWFixsJh/vW/XiGpIjqM539FPW49JVdksbwiTC44DsQGI9kGkL+WQGc9ByEk5uCnojzocaIm4ycnNJh0DbiBTQwd3eY5xk+cYuzwvUZbIe4zHOVoZROevjq8ebCW6cK3sqZwBUoIANaRi0JAgwKOUXA+EyLKCnIetiJuHMM42o0SAMJky3m3dZjJQAIjZmoeeOkNdxbpRe/l4e5ri24v24r90HDVkDtXKQ6RqUzz/YAd2SfUkflCSYzgZ73VwqDXC8bbU3j/g9jpYU+lPBgfrJtDrYLArcki2FYkJy9snqOKAd0SeguNofvPSxRENxqRa0iRpHS992uNWgxjCchJt6QuvkpHWmvNXLPY1u43RXrwYwdZQTJjrjBPs9B7nZu8x6mkZ8iD3F6ukBrN6q7t6UNWADpTm54cQY/J5DPxeY7CKUDw/QFYChCgMBTUPJ5OVe9xgYQzTufl1ZSCaDBQSYrbmSjg6GCxoB3/Hy8kAwdd9PnWo3hBm7XVuD4Ta6yc9B9mO5tXOGAda3FKmR9uiKaW9wd2Tt2qBj4Z4OdMNVQGKJtDrwPHEgwNfSCoViSkpqNut5zv6kxcokGpJk6a1e+dlWMKy5ThELIf+qEUkVlgN00zL4VBLJBkgtPbZGDhsUGf5Y+MouzzHaTBO4yV13I6vBLN6M2b1NsyaRuzSxXn6CUQ6XiMRFCj8Xvf3fo8hAYEQBa4g5uEJJivHHLfM6XRvfs0r8uPzqJSAwedRVPgh2PI8oaanCV14Bk+4I+VxboO0nUMapE28OEbi5tjBeDnTw60R+tL0Oqif543nHATZXBOgLDB+cKANfzLnQPtC0ixUTFtBBQttPWbyApUg1ZImYFh/hMQFNGI5xKzCq2TU0mPxXDw4ONRqErVhiWrnZuMYO33H2OF5iXJS8yq08hBdeK3b86B2G9H518jWogLgMVRypSAREPg9hqwECjFD5W0ejsa3y0b73DlttNNsm3DUIRzLXI5dVXmQe25cyUNPncFvD7DDe5I/WHSexY8fTtMgbdmQBmmrJ/xBXGtNS6/NwRYzmZTcZY4cf22ph4aaIA21bknThUXj9zqQpGSRbQX1aauqLEjQZ6RcqOZ6taQx9446NoSvoM0rRGMWEcthoAD7IERtzdGLkWRp09e7LUoZ4AbjJf5f4xi7AseoV20jHhcrrUvmHUSqNqN9kgifD0q524QGy4nGgwKvdCAWYrbJ6TzsOO52WbPbrWw0iohlMxCvYpSN7bO+gTZuij7D71X/B6UdRzG0DfFFhGSDtLpdhJfsxCqrm/DztvcP7ZIcoa1/5Mr+giKDhhq3lOnmmgC1pWN/LEv2OfAE4g3QAlLuW2RdQQUL9QuKeeBdm0bslZyr1ZLS7R39yl1ruHVVOXa0D7O/n4FojKbOMJ39kWmVe8u09n6LfU1u5aKDLRFiVoxN6lXu8hxnp/8Ym4xX8QzbV2r7y4hUb0lWLZpKUpiYPG8ib8CjBn8/JKHY65ElbFH4+iMWZy/1FUYy7gyW9XnYcSDWP7iSkGabkUYTiTkMxGzMqJ351fFkg7Sn3QTlzldSh+jxE6mefIO0rrDNkYuR+NaiCE1peh2UBwy310E8KXnpGL0ORjRA8/glMBATolSiIAjJuVypoYVC3O8bSiWLi4z5fDpPtfQbGxv1gQMHRnw/cSe9vddkUWmBVGHIk7OX+njL157CExugCJMiFSHogf951wYqS/zTLveWSZajeal9sDHamc4oy9VFdhrH2GUc43rjBKUqnPIYbXiJVK6PJyY3EqtYBcbE2suL8cVK65ITi8dQI5KKE6sEsjowcymlDmqtG/M9jkIQqFmll3/0a1IUIwMyPg9rHa/I1+vmI4wSICQagY5V5nTKHIvApWNu/kHzXrz9F1MO2/4yzCU3uPkHNVvd7Tzj6Iv3OkgEB2fS9TrwKTZVuwnJDTUBVs73YYyRu6UNP46vCO0tcrsjS56XGMZQqTf3fBmqGjjWfFJQKwsAhqFYUVkiOQoxk/aW16myWjHU4EUzasPlXpPKEv+Uyr1lUlfY5vl434P9F0w80V52GMf5aHxr0RI1so9SrLzezTuo2UqkatOELshi4jzKvWj4DANPSQC/P4jPI6sDYm6QohiZkbF5OBZ2S3ZHe9PmIURt2+2BYDlEYjY6wyW8lRUm2LJ/sEFatCfluFVSM9ggrXL9uHlwpuVwrC2aTEo+dTmGM0qvgy217tc1C8fudaANH9oTxElUKpJ8gznPLSFu4Ev0DfIYg1uBjfzkBBZcsDCegqz/nAla40QHGBjoxezvJmpGMaIDBDwwtICRz6OYF3LvFk+o3FsGOVpz6nLM3V7UHObMpTCb1Wl2eY7yCeMY6wPnUgIbADtYgVm9BbN6K5GaRuyiyoyPa+6I3znwDG1EBl41eFdBMeT/haAPvLJSI+YWKYqRZ1bUXUGI9qbtqhy1bQaiDuFodsp3G+FOQheedRukXTwwSoO0XfEGaSvGvHMfszUnLkU51BLhYKvJS+1RrDS9Dq5d5E+WM722cvReB4M9DgJoT9DdViSFOuYUr2Hg88ZXBYzBFQLDINlfqBCrBo77r1Qp9R3gbUC71nrdKOfcBHwF8AGXtdZvyNwQBxVU/ec0Y5t0EGPHMAd6iQz0Yob7icYshtaHHlqhYeg2o6pyNxAYrdxbIpjIhN6Iw/4LbuWifc1hFkaa2GUc478Zx9geOEmxSq19rQ0/kUUbknkHsYqVUrZtQhQ+j8JjGBgqvsxouEuJg12J5X0UYjxzvShG3sRMt2x3tH/EoYhlE45lL0Dw9jQltxf5L7+UpkHa5vgKwg7s4qpRn8d2NK90xDjUYnKwNcKxUXodXL3Qx+aaAI21QdZX+SnyjX5tdrcVFSeboMm2otltaEEQr2EkV/Z9HjWjy4dPJKR9GPgG8Ei6g0qpecA/ALdprV9XSi3K2OiGKYj6z2lMJIjRWhO1HaLmAJbZTyzcSywSHnNfpqGgsb6Cuop1XAlHmRdKTWAeL5iYCq01Z7piyeTki+2XuE4d5xbjGJ/zHKcm0DniMdF5Kwe7JVduRHulXXw6hhpcThzcc5hYbpRAQIjpmutFMXLOjrnBQbTPDRaGMGN2FhOU4w3Smp4m1Pw0vp7XUw4PNkjbSXjxdWh/+gZpjtac67I4FC9neuRi+l4Hy+d5aagN0lDjViwqHafXgTYCOP5iHF+xJCTPEmrIDbzECsBgEZDEDb3Zu+V33GBBa/2UUqp+jFPeC/xEa/16/Pz2DI1thELtw5AuiPkvPzzM0ntvoKbURzTcR8zsh9gASk+uKZqhoGZeMO22ovGCiYkaiDociDdGO/R6D8siJ7nROMb/MI6xLnB+xPl2aEEy78Cs3oITWjC5F5zFhgYEPs+wZcYZekdBiJlgxcJi/vW+XSmrurN222o+xczBAGFIudOhFYzCUSvzCcp2hODFQ24H5QvPjmiQZoUWYi7Z4SYoV21O+yFda01zj5UsZXqoNcKVNL0OlpR5k9WKNtcEmB+aQK+DZIBQInkHM0SiGlDig34yEPCMDAjm+nUjE5vlrgZ8SqnfAaXAV7XWaVchpqtQ+zCkBjGaIFFCVoSmc69QXuXeZc/WP7OxgonRaK1p6nYboz33ehiz/VWu5xjvNI7yd8bLBP2pFR0cT4DIok1uUnJNI7Hy5XN6KXVoQDC01KjPIwGBEPlSHPCm3DQq5G2rM44VGeyo7Aze8HK0xozZmDG3SVqmAwQV6SXU8pyboNzyPIaVWlEvVr483iBtp9uoM82W17a+1OCgPU2vg8oiT7xLsvtVPU6vgwTHE0T7EisIEiDkS6L8Z2L6TWzfTQQCQwOARG7AbF0ByJZMBAteYAtwCxACnlNK7dNavzL8RKXU3cDdAEuXLp30C02n/nMm7zDZjiZiuRfIiGVjR01qvN147Ag+3AuRz6NYEMhPWdp0IpbmSLwx2iuvt7Bq4Ag7Pcf5gHGMSl93yrkaRaxilbtyULOVSOW6Wb+Umqgi5DEUBor4f/G6xIOJR16PgUcCAiEKXqFuW50xEluMIj1u0jLu6kHUcohYDqblVjIiwxWMPP1t7upB814CbUdSVuM1imjlesJLdhCu24VVumTE47vCdkpw0NyTptdB0EgGBg21AerKRu91MJQ2fEM6JQclOTnLRvQKMFLLhCaShCX4z75M/EtvBjq01v1Av1LqKWAjMCJY0FrvAfaA22dhsi9kGIrb1laz+r5dk6r/PN07TNEhF0YzZhOLRVF2FGWZKCtMncfkT2+sjecOkJHcgUxo7XUbox1s6sZ38QjbOcYfGcdYbTTBsM/+sdAiIrWNbtWi6i04wXl5GXP2uAnEie7DiT2GiTsPKmtrP0KIfCjUbasFS2uIDbjN0mIDyS1GjtYMxCzCUXf+y3Rw4DZIO+MGCE1P4+86nXI42SCtbpfbIC1YkXK8N+Jw5GIiODA52zUyOCj2KTYmeh3UBlhRMXavg+TQEtWLfEU43iJZPZiG5HYfz+CH/MSH/sTfhbtC4AYJiV9FYchEsPBz4BtKKS/uR9DtwN9n4HnTmkr958neYTJjbmAQiVmYkQi2FUXZETc4sCP4hucdZCh3YLpituZYW4R9TQNcev1lVvUfYadxjHuMUwS8qRdQyxMiWr2ZSLxqkVW2dNZsLUpsE/J5DPxetwKBz2NIQCDEHFKo21bzacQKe0UQww7HcxD6k83S7OT2IpuBaBYCBMci0H403kH5mZEN0gLlmIuvJ7xkF2ZNY0o/nnDM4WhbNJmU/ErHyF4HAY9iQ5WfzbUBttQGuXqBb8xeB0NpI+D2PJCmaBOS7u5/orS315Pf3gAicyZSOvX7wE3AQqVUM/A53BKpaK0f1FqfVEr9BjgKOMA/aa2PZ2/IkzfqHaaeAerLvZjRCJGoRSQWJRqNoe0Yyo6BE0WhJxRRTSV3IBMu99vsazZ55bVmitteYJs+xp8ax1mgeuN/Sy4HA3P+NcRqtxKpbnS3Fs3oJdTBlYJEvwGfYUiJ0cTElqfO7EIUiulsW52NEivsn370AEZsgApflC+8eSW7VlViKEXEsumP2Jzv7Odyj8m8osze9FKxAYKt+wk1P0PwwrN4or0px62S2iEN0gbnp6iteanVXTU41BLhxKWRvQ68BqytjAcHNUGuXeTH75lgcIARXzkIoX1FM3xezJzEB39vfF5N+VKpfxaz30SqIb1nAuf8HfB3GRnRdGkN2kl+ObaNNnuo9vWhY1F8ysKDQ8ADvu7zXHBSy3wqspeM7Gho6za5MhCd8oXYduJNYl7rxHz9MCv7D7PbOMa9RgsM+4w8EKrGXryNSE0jZtUWdCB9+bjCp+I1ij34fXNopUApMDxg+NxfBw/Ej3ndXBKPL37OHA6ShBhmqttWZx3bAitM08XLPPDof7DQstxJzoL/8euTfOmdfhYUB4g5DgfOd40oxd1YXzHlgMEId7gN0pr2Erx4EOUMb5C2Opl/kCicYTmaU5ejHGod4FCL2+sgMqz5qKHgmgU+GmqDbK4JsKHKT2iMXgfDacOH4y1C+4rn7OpBohSo3+vOp4kVgETBDtkCJIaaOSG01u4eShX/OJ8ICOyoW6nBjrjH45UabK2JWjbnLvfztV+e4AObKvnp4QvJi+BHdy5nYUnu+gE4milfiK+YNi80DXDx3HFK2w+yTR/l0+o0PmWn/A1GPMWEFzXgLHGrFqVL/ip0CoXPayQvYn6Pgd87iwMDpQY/7Ht8qb83vHNyEhMiU8bbtjorS6taUbDCEAuDZYLtNkJrbevEtlK3o0ZtTWt3mHlFPtq6zeT8BO621oeeOkNdxbpJrZh7e14f0iDtxIgGaWZ1A2aiQVrRIhytOdsV49BLfRxscXsdDMRGroyuqPCxJV7KdFP1+L0OhtKoeOfkIhxf0awv2JGOoRQBn0HI5yHo8xDwztwGYSL3Ci9YSAQFiQ//dswNCOzoqFsrorZD1LaJWZqY42BZOtkEprlzgLbeCI+faOO2ddXJxywo8ec0p2AyF2JHa053xDh55jw0vcDKgcO83XiJeao/ZenDxuBK+RqMum1Ea7cSXbB6xiyhGkrh93ridzRm6BYipQaD16G/KmPIl8ddFVBGfJXAO+zYDPp5hcgApdR3gLcB7VrrdaOccxPwFdzNlJe11m/I9DgKvbTqhAMZx4FYfzIx2bEtYrZDzHGIxByiloPlOBjKXaGNDblL7/Mo5oXcD85XBqIpx8Cdp66Eo2MHC9rBf/lEMv9gRIM0XzHheIM0s3Y7jq+Yph6LQ69FONTawaHWCN2j9DrYEi9nurkmQMUEeh2kDEt5ksGB9hWlLas6Gw02/Ix3DfYaBLwe/N658fOL7MjfJ0vHgu4LoG13NUA7E9pnbTkOMdshasU7Ilv2mLWd5xX58XkUnQMxfvliK+BeIHeuXJixH2UixrsQ90UdjrzWQc+ZF6joOMg25yi/Z7S5Jw65RnYFFhOtacRYuo1IdQPaV/j7bz3KwOt1L15uwrEHfyHXOB76gT/xAd8bcO9GGd4hAUD+P1AIMQM9DHwDSNuPRyk1D/gH4Dat9etKqUXZGEQhl1YdN5CJmW7FolgYOxomHIthxhyiMWfUbslV5UHuuXHliNXtRNW+xFw5WjCRYmiDtOZn8JidKYetUGV8e9FOIos2c3FAcbA1wuHnIhxq6eXSwMheB4uK3V4HW+LBQVXJ5D+eaMOH4ytG+4pSkqJnm+S2IY8xuHXIM5hfIESm5S9Y0I67TDqGmOMQsxyitnbvlFijXwhHM94FMleGX4i1BsNjcO7UMTqfOcTV4SO8U53Bq+I/X/yzdL9RSueCzQSWbcdavBW7pHqUVygMCkXA54lvH3JXD/LSlyBx1z/5wX7Ir0OPJQKClN/LxVaIbNFaP6WUqh/jlPcCP9Favx4/vz0b4yjk0qqpgYxGx0w+/+he1nx4E7XFipgVIzbkhtlEGONU7RtvrpxIg7SBJTsx63bSGryKwxdjHHw1wqGnL9PSO3KM8+K9DtxypkEWl3qmtC1GG/545+Ri8ORua3G2uavviap+7ipBYrVAtg+JXCuIPSu21li2Q9R2iA0JDHQGyrWNd4HMlaryIB+8YTk/23uEDeo0242X2W6cpOxC/IIbDw4svLQWr8FZspXg8u1EK64Gw0Mkt8OdMI8yCPiM+FKngd8zuaXiCUnc3U9s50l+sDeGfcgfdkwIMRNdDfiUUr8DSoGvaq3TrkJMR8GWVrWitF1qpzjWxXxi+FXM3Xlqwemmi3hryqb81GNV7Us3V9Z6r1B86keEmp8m0H40tUGaMogsXIdZt5NLi25gf28lB1tNDv0uwvkrbSOev8Sv2JTodVATZHnFxBqhpeN2Ti6KBwgzN/8g0ejTO6ThmC9e7ls6DItCkrdgwXLcxCrL0WS8hvMw+SprCnDxcidtp/YRbD3AjeYR3uu/PPIc3xKuVDZSsmI71G4GXxEGEB35dHnhJkZ53FrJ8R06Bu5djynnGKS7u2944xV+EtV9vHKnX4i5xwtsAW4BQsBzSql9WusRjT6VUncDdwMsXbp0Ui8y3dKqGUuOdmx0tI/wQB/mQB+RSBRP/wALvOGJbQnKIAPNUud1rrnkbi8a2SAtgFmzle6aHez3NPBcR4hDpyKcfjaGpiPl3KDX7XXgrh4EWbXAN+UtMlp53c7JvpC7vWgG5OYlmpAZaUqNJgp3yJYhMVPk7f84rTWWM7ktRTNBNBrlwukjRM/vp7b7IFucsxgqfsGPXxeuqDIulG3Gs3QbpSu34RRXUUy2Q6aJc3ML4isGU80vMDzgCw2W9kzZBiR3/oUQo2oGOrTW/UC/UuopYCMwIljQWu8B9gA0NjZO6hI6ndKqU02O1loTsRwipknU7MM2+3CiYWwndZtOTrfPOhaB9heT+QfpGqT1197AyZLr+G3kWvZdVJw8E8XWNtCXPM9nwNpFfhpqgzTUBLi20t16O1UaA+0vwfGXFHT+QWKbkN9j4ItvF/J7pNKQmF0KPzwvdFrTffEsXa88R0n7Qa6OHGelGrJpSEEEH2cD19Jf1UjF1dfhX7SKsnhlhkIIl9yLnVtKLTCZ1QLDSE34TQQG3hB45J+WEGJKfg58QynlBfzAduDvs/FC45VWHc25y6MnRy9bUEzMdrAcjW3HK/RFI1iRAZx4xaKh23nSjivL22eTDdKanibU8hxGtC/leKxkMU3zr2ev0cgvrqzg6CsWURsgljLG1Qv9NMQrFq2v8hOcZsWdZIM0X7FbvCOPH7gTfQi8npErA0NXCCQoEHPBrPlEl4mGZxN+rYEuLp/aB837WdpzmDqGVIKIv+ZZYxkXK7YQWLaNyqsaKPEFyW/KnMvnccuoJfZFjtvcLLFdyON3k8c8vvjv/RIQCCEmTSn1feAmYKFSqhn4HPF+81rrB7XWJ5VSvwGO4t5P+Set9fF8jXc4x9GcbO1Jmxx97MKV+EkWKjaAYYVRVjgZHExmSprI9tnJzHtG+DKh5mfdBOWLB1FOLOV4T/k1vBjcxs8jDfxrRxXh5I7Zwd4MK+f72FIToKE2wMbqACX+6a8QayOQ3F6kvaGcBAhDP+x7PYMdig0VLz0azyUQQrhmxae96TQ8mxA7QrT5RXpf3UfF5UMss86ybNgpl/Q8ThdtIlq7lUXXXEdpRSV1GXjp6VOE/B6CPoOQz5u+MlEyGPAP5gwk/1wYF8xZ2TxJiDlIa/2eCZzzd8Df5WA4E2bFC3CcbuvlSFMXAa8iYg3ufCr2Wiw0+vD29I7oVJwNE5n3vN2vJfsfBC6/lPp45aWpZCO/U438c/dGXm2bN+I16sq9yeBgU/Xkex2k4zZIC8W3FxUN604/fYkVAY/hlhZ1twcNlhWVPAEhJm9WBAuZ6jyZpDVG5xl6z+zD2/ICdf3HCZB6Fyas/RzzXEvHgi0UrdzO0vqrqZvinYjMroq4jXd8HoOgz0PI58FIFyB4fOAvBn+J20OggJdSC715khBi9ojZTrwiX7w0abw6X6Kfz6vtvfz8SAvv376MHz5/Go9lMs8b4Z4ddSwJRlA52luabt7b89Rp1mgPS7r3E2rai6+3KeUxEU8RR/xb+Km5mV+FN9AXLko5XlXsYXO818GW2iCVxZn5IJ8SIPiKM9IgLREUBIY0HZOkYSGyY1YEC1PuPDmEEb6MbjqAeW4flZ2HKXe6Uo47WnGSes4Vb8Jasp1l12ympixEzTTHnolVEb/XQ9Drrh74vWm2FRke8AbjKwY+8BW5v84Qhdw8SQgxczhOPIfA1m5OgeMW2rAdnfyeHq3Jp3ZQVoQqXz9B8yKHjrTyoXULAB+GKmVVZXFOS3In5j0/MRqM0+wwjnODcYL5z/WmnNflWcATegs/NbfwvLOGWP/gtF8RNNgc73WwpTZI7RR7HaST6RUEj6EI+TwE/Z54mW7JFxAiV2ZFsDCpzpNxyjLxt72Ief55Aq0HqIqcH3FOi57PEe8GrixspPyqbaypq2LjNKo7pDPVVZGAz0OR35N+a5FSbnDgL3a/ZlBgkE4hN08SQhSOmO1+8He0TiYYW/GAIGZNsgKfY6EsE2WZGJaJctzCFUuC8Mkb63joqTP88sXW5A2eyrLcleY2It2s7HyKv/L9hkZ1ipBK3fZ0Ri3lX2MNPGY3clwvJ5EtUeJXXFcTSJYzrZ839V4H6WRyBcFQ8S20Xg9Bv7t6IITIj1kRLEyozJx28HWdxtP8Atbr+1nYfRzfkMQtgD4dZL9ew+ulDRhLt3HNyqtYX57dD9qTWRXxexMBgmdkxSLDgECZu2rgy02SWK4UbPMkIUTBiFgOTZ0DU3uw1ig74n5ZpvvrsATghHw1+vT0tcbLm+5NNkhbGZ8GbK04oK/hMXsLjzmNNOkqAEJexXXVgeTqwVXzp97rYDQaFa9gVIL2FU06QPDFC234PG5ScaJTsX+alZWEEJkzK4KF0S7evnA7gZb92E0vUNJ2kCK7J+VxtlYc1Ss54tlAX3UjNSs3sGlxKau8ufugPd6qiN/rBgchv9sULSmxeuANgi/oBgmzKEAYarrNk4QQIoVjx4MCczA4mESnm5w0+tQaX9crhJrcAMF/5UzK4QEd4GlnPY/ZjTzhbKKLMvweWFcd4E01ATbXur0OvFmKYhxvCMdfOuEVBKXcfDp/PMcgUapbthIJUfhmRbAA7sW7tthhed9xvK+8gKd5P2XhphHnNTmV7NXraS5vILC0kc3LK7mlPLNLsZORblXkT954FatryijyD1lB8AbiwYE//msgL+PNh+k0TxJCCDc4CI/YUlRwHItA25HkCoJ3oD3l8GVdxr/bDTzmbGGvsx5L+VlT6ec/xSsWrVsUIJClm13JLUaJHgjj5CAo5eYYFAXcxp4SGAgxc83sYMGx8XeeInDxAEbzfoo7T+DRqVuLenSI55y1HPZuwKzexqrl9WxZHGRnBupDZ0JiVWR55QbMmEVteRFL5xdheLzudiJfCHzFc76nwVSbJwkh5iDHQllhNzCwzEmXMs1l3x4V6yfY8jzBpr0ELuzDZ6U2SDvnVPGY08hjdiNH9CpWzHe3FP11bYCNVQGKsjiXpTZJG3+LkcdQBOP5dMV+r9zQEWKWmHGfQD19rQQvHsDfsh9/6yH8VmrlB0sbHNZX8Yyzngvlm6lYtp7rlxbz3vm+grur4TUMQn4vRQEPy6oWuE3PvAE3QJjhSclCCJFT2sbT3+5uLRol32Aist63h3iDtKZn4PxTlF0+jHfYTa4jzkoesxv5v04jsdKlbFkc5M7aIF+o9lMezG6ir5uDUDwkB2H0H1opRdBnUOTzzrgkZOndI8TEFXywoKJ9BNsOE2h9AV/LCwT7L4w455xTxdPOBg55N6AXN7Bp6QJ2LwlSFiiM1YOhkmVO/R4CgRAESsBfOudXDoQQYjqUY2HEesc/cRwZ79sDoDWe7vPYZ57G3/Q0Vf2nUg5HtJfnnLU85jRyLNhIXV0NW2qDfKkmwMIp9DqY7MrIRKsYDU1GTvbxmYEfsKV3jxCTU3ifUB0L/+WTBC++QKD1AP6OExg6tdzdFV3MM85annY20FSyiYrKaq6vC3HfshJ8GS5tmgkeZVAc8FJcFMIbLJmVFYuEEGI2yETfHgAcm4HmY0Re/Q+qLz3LIqs15XCPLuIJZxPPerbRX7ONdUsquKM2wL2l05uWJ7oyMl6AYChFwGfEe/i4fXwKbXV+qqR3jxCTk/9gQWu8fRcItrrBQeDiITxWf8opUe3hkL6ap+31HPRsoKh2NdvrirnGMTn0/Dlael7jlfOKoMrsUvF0GMq98xIMFVFUXIYKlLrJyUJkmSyvCzF1U+nbk9DdP0D7y/sINe9ldd9+6kitwNei5/M7GmmafwOhZQ1sXlzMn2S4wMZ4KyOONxTfYpSapBzweQh6DQI+t1KRz1N4K/OZIr17hJicvAULRqSXiuf/jmDrC3j7L444ftpZzF5nHU85G2grW8/mpRVctyTIO6rcUnCtV0z+8ufnMrtUPE2GUhQHfARDIYJFZeAvkQBB5JQsrwsxPRPq2xPXH3U40XSJ2Jm91HY+xxbrRdap1EpLL+ulvFS0nb7FO1m84lp2LPRjZPEOfbqVkT7LS5tTwsKyJckAwWMoivxeigPu1ti5dH2Q3j1CTE7eggVPbzMlr/4y+efLuoxnnHU87aznBbWeutparqsL8vElQapKRg4zY0vFGeDxBSktLaWkuATlH7+knBDZIsvrQkzPWE3XTMvheFuUs6+9RmnLXjaE93OHehmPis9Fyi2ycdK3htaFN+BbuYv6pcvYlsMP4kNXRvp1kG6K0d4AZfMq8Xi9hPweSgJeQj7PrNlWNFnSu2d66uvrKS0txePx4PV6OXDgQL6HJLJs3GBBKfUd4G1Au9Z63RjnbQWeA35fa/2j8Z5Xo9hru3kHe5319JSs4LqlRVy/JMjHqsevFT2dpeJMMDx+gqUVFJfNJxiQ1QNRGGR5XYjpSzRdW1ga4MSlKL85082V5hOs7H6ON6qD3GnEe/jEd+qYBDhb0oBZt5N51+xiXvE85uVp7FXzQnzwlk38zb9fYMDy4PcqvnD7OrYsq6A4kL+eQoVEevdM35NPPsnChQvzPQyRIxNZWXgY+AbwyGgnKKU8wBeBxyb6wid1PV9b9FdcXxfkL5YEqSufXKnQySwVZ4qhFMFQCaHy+YSKy+WiKwqOLK8LMXW2ozndEeNga4QXL/QSuvQib9AH+KjnILWqE4YsGvd6yrlUeQP+q3bB4q2UegOU5m/oOJ6g21HZX8r2csV36+vpj1rUVRSxfGGJfBAeRnr3CDFx4wYLWuunlFL145z2/wA/BrZO9IVXLgzy5dsqJ3r6CGMtFWeSQhEKeCkqKSdUugB8M+tDlyS7zi2yvC7ExGmtOddlcajV5GBLhNMXu9hqHWG35yB/bByhzDOQcn5PcAnRpTuxl+0iuvBafIYHPcpz54I2fDj+EhxfCcobcJuhBbwU+TysXCQfgkV2KKW49dZbUUpxzz33cPfdd+d7SCLLpp2zoJRaDNwJ3Mw4wYJS6m7gboDFi2un+9LJpeJs5CgEfB6KAj6KSuZjFFXMyD4Ikuw698jyuhCj01pzodfmUIsbHBxujeA3L7Pbc5A/Mg5yvfESfr+d8pj+imuJLdtFeMlOrPJleRr5IK087gqCrxiPv4hiv4figGdO5yCILAuH4ac/hTNnYOVK9j7+OItXrqS9vZ3du3ezevVqbrzxxnyPUmRRJj4BfwX4tNbaGe9CpbXeA+wB2LhhnZ5s45hs8xoGRX4vRaEAvuIKCJSDMXPLx0my69wky+tCDGrvtzjUEuFga4RDLRHa+y1WqQvsNg5wn+cAm4JnU86Pag+HnFU8zzqu2v5W1l29Mu/luDWG21XZX4o3WExZwEuR3+1/IERWvfAC+tZb0TELNdCPLiqm1ueFxx5j0dat3Hnnnezfv1+ChVkuE8FCI/CDeKCwEHiLUsrSWv9szEdNsHFMtnmUQcjvoSjgJRAqhWAZ+GfHlg1JdhVCzDVdYZvDrREOtUY42BKhucfCwGGLeoWPeg6y23+A5UZbymMcXwmdldv4VtMSnrOuIYy7Wu3b18XfVJl5KcetUWhfEdpfSrCozN0O6/fM6v4HosCEw+hbb0VduULiY9lAfx8OUHLrrQy8+iqPPfYYn/3sZ/M5SpED0w4WtNbLE79XSj0M/GrcQAGIOXrMxjHZpFCE/J54T4RiNzgIlM66kqeS7CqEKDTZqrDX3mfxgZ+0cbYrBkCQCDuN49znPcAtnsMsUKkN0qyiKsJ1Owkv2Ulk0UZOXhzgiXOnUs7JRzluxxNEB8oIFZdRHApQ7JftRSJPfvpTd0VhyLfacPed09ODtXEj7733Xm677bb8jE/kzERKp34fuAlYqJRqBj4H+AC01g9O9YUt28l5nwS/193bWeQPYMyCbUbjkWRXIUQBepgsVNjrMh26ujp4h+cwt3kOsMs4RoBoyjnRiqsIL3EDhFjFKhjyITyf5bg1Btpfgq+4gvKSYkr8XskzEvl35gxqoD/lWyuAF3Hzf9THPgZ/8Rd5GZrIrYlUQ3rPRJ9Ma/3BCb+wx8jJhdlQiS6VXvxeDwTLITR/VgcJCZLsKoQoNNmqsHeV0crB4B9jMLiSqpWHyKKN8QBhB3ZJzaiPz3U5bo0CXzH+4jJCReUUBb2yxUgUlpUr0UXFqP6+EYd0UTFqxYo8DErkQ95K/PgMldULc9DnpdjvIeT3oJQB/hIomg+eyfVzSGcmlSOVZFchxEwymQp7Q4V0GAMPjjeEWbONcN0uzNrrcAJlE3p8LspxKxS+UDH+onkEi8sI+mdek7SZNP+JaXr721Ef/3jaQ8rnhbe/PccDEvmSv3qgWbgwKxRFAS+lQS8+wwBfyF1J8BenLDdPh5QjFUKIrPoKE6ywN7Qc95qaYk41/jVNRWspKy2Z0nySjXLcCkWoqJji0nKCxfNmZBnuBJn/5phgEPXYYyOqISmfF/XYYxCU/Me5Qmmdn5YyGzes07/68fcy8lw+j0FxwEux34uhlBskFC8EbyAjzz/U2Ut9vOVrT49IGv61lCMVQuSYUuqg1rox3+OYrPg2pF+lS3BWSp2DZE7lQmAAuHu8whkrr75Gr3zXn+W1ut4gRdDvJ1S+kOLSclQGVrQLgcx/c1Siz8LZs7BihbuiIIHCrDPWfDJjb3Gk5CIk9nl6AxCqgEBmL1pDl13DMVvKkQohRJZMtcLepd4IS/NQXW8ov9dDKBSiuGwBnlB5xla0C4WU456jQiF473vzPQqRRzMuWEjJRUC5F+NAmdsfIQsrCcOXXT9xy1VSjlQIIaYoWxX2hq+R56rsqd/rocjvJVRchreofNb06UlHynELMTfNkGBBUez3UhqK5yKAu+8zVOEGClm8ezO8C/KjB5r5xC2r+Oq/n5ZypEIIMUnZqrA3fBbIdtnToM9LaUmxm4cwC/v0pCPluIWYmwo6WDCUojjgoyTgwZsMEnxukBCcWIWL6Rq+7NrabfLIc6/x3Q9tQ6OlHKkQQhSAytJAshx3tsqeKhShgI+S8goCxRUZW82eKRWGpBy3EHNTQQYLAZ+HYr/b2l4l7hflOEhISLfs2jUQpbI0IHs0hRCiQBT7vfzNHdkpe+r3eigO+ikqW4gRmpfRPj0zrcKQlOMWYu4pmA4wCkVJwEdVWYhFJUGK/V43UPCFoLQKKpblPFCAwWXXoM99q2TZVQghClC87OmamjJq5k0/UPB5DOaF/NRUlFJVvYSS6lUYxZlv6Dl8q6sZc/jUo0c439E/ziOFECI38r6y4FEGpaEhZU/BzUcIlLlfea5JLcuuQggxV7j5ccUBDwG/P76anfmqRlJhTwgxk+Ttk7hCMa/IT0nAO1jVyFcUb6JWlK9hpSXLrkIIMTspFEG/h6DPoMjnxfAFITQP/CVZKZ4hFfaEEDNN3oIFr0dRGog3qvEFoXgReLNXuUIIIYRI8Hs9lAa8bhluZbjBQbDcnY+ySCrsCSFmmjzv8fFA0Xz3Ai2EEEJkmd/roSzoJeTzDm55DZbnrPSpVNgTQsw0+QsWDC9U1M+6DpdCCCEKi6EURfFcBL/X664iBErzsuVVKuwJIWaa/FVDUoYECkIIIbLCowyK/T4qS4LUzgtRUVqEv2wRVCx3K+zlKTdOKuwJIWaavFdDEkIIITLBUIqQ30ux30PAG99W5Au624wCpfkdXJxU2BNCzDQSLAghhJjxvIaidl7Ira6XLL9d6jb0LDBSYU8IMZNIsCCEEGLGM5RCJcpvB+RDuBBCZIoEC0IIIWY+jx/KF+d7FEIIMevkL8FZCCGEyBQpmCGEEFkhwYIQQgghhBAiLQkWhBBCCCGEEGlJsCCEEEIIIYRIa9xgQSn1HaVUu1Lq+CjH36eUOqqUOqaUelYptTHzwxRCCCGEEELk2kRWFh4Gbhvj+DngDVrr9cBfA3syMC4hhBBCCCFEno1bOlVr/ZRSqn6M488O+eM+YEkGxjVpjqM539FPW49JVZl0xBRCCCGEEGK6Mt1n4SPAv412UCl1N3A3wNKlSzP2oo6j+c1LF/nUo0cwYw5Bn8ED79rEbWurJWAQQogCopT6DvA2oF1rvS7N8fcBnwYU0Avcq7V+MbejFEIIkZCxBGel1M24wcKnRztHa71Ha92otW6srKzM1EtzvqM/GSgAmDGHTz16hPMd/Rl7DSGEEBnxMLK1VQghZoyMBAtKqQ3APwF3aK07MvGck9HWYyYDhQQz5tDea+Z6KEIIIcagtX4K6Bzj+LNa6674H/O6tfXspT6eO3OZs5f6cBydj2EIIUTeTXsbklJqKfAT4P1a61emP6TJqyoLEvQZKQFD0GewqDSYj+EIIYTIDNnaKoQQeTaR0qnfB54DrlFKNSulPqKU+phS6mPxUz4LLAD+QSl1RCl1IIvjTat+QTEPvGsTQZ/74yQu7PULinM9FCGEEBkgW1uFEKIwTKQa0nvGOf5R4KMZG9EUGIbitrXVrL5vF+29JotKpRqSEELMVEO2tr650La2rqgsyfVwhBAirzJdDSlvDEOxorJELuRCCDGDydZWIYQoLBmrhiSEEEKMR7a2CiHEzDJrVhaEEEIUPtnaKoQQM4sEC0IIIcQwsrVVCCFcsg1JCCGEEEIIkZYEC0IIIYQQQoi0JFgQQgghhBBCpCXBghBCCCGEECItCRaEEEIIIYQQaUmwIIQQQgghhEhLggUhhBBCCCFEWhIsCCGEEEIIIdKSYEEIIYQQQgiRlgQLQgghhBBCiLS8+R6A42jOd/TT1mNSVRakfkExhqHyPSwhhBBCCCHmvLwGC46j+c1LF/nUo0cwYw5Bn8ED79rEbWurJWAQQggxaXIDSgghMiuv25DOd/QnAwUAM+bwqUePcL6jP5/DEkIIMQMlbkC95WtP855/fJ63fO1pfvPSRRxH53toQggxY+U1WGjrMZOBQoIZc2jvNXM6DsfRnL3Ux3NnLnP2Up9MLEIIMQMVwg0omU+EELNNXrchVZUFCfqMlIAh6DNYVBrM2RhkK5QQQswOY92AWlFZkvXXl/lECDEb5XVloX5BMQ+8axNBnzuMxIW1fkFxxl9rtLs9hXAnSgghxPQlbkANla0bUOnmFJlPhBCzUV5XFgxDcdvaalbft4v2XpNFpdlJRhvrbk++70QJIYTIjMQNqOHX+kzfgBptTqks9ct8IoSYdfJeOtUwFCsqS7J6IR3tbs/q+3YVxFYoIYQQ05erG1CjzSk/vPs6mU+EELPOuNuQlFLfUUq1K6WOj3JcKaW+ppR6VSl1VCnVkPlhTs9Yqwe53AolhBBzXbbnlMQNqOtWLGRFZUlWcgVGm1MGorbMJ0KIWWciKwsPA98AHhnl+JuBVfGv7cC34r8WjLFWD3J1J0oIIQQwi+eUqrIg25cvkPlECDGrjLuyoLV+Cugc45Q7gEe0ax8wTylVk6kBZsJ4qwe5uBMlhBBi9s8pMp8IIWabTOQsLAaahvy5Of691gw8d0bI6oEQQswYMqcIIUQByWmCs1LqbuBugKVLl+bypXOSSC2EECJ3ZE4RQojsy0SfhQtA3ZA/L4l/bwSt9R6tdaPWurGysjIDLy2EEGKWkTlFCCEKSCaChV8AfxivYHEd0K21LpjlYiGEEDOKzClCCFFAxt2GpJT6PnATsFAp1Qx8DvABaK0fBH4NvAV4FRgAPpStwQohhJjZZE4RQoiZZdxgQWv9nnGOa+DjGRuREEKIWUvmFCGEmFkysQ1JCCGEEEIIMQsp9yZOHl5YqUvAa3l5cddC4HIeX38shTo2GdfkFerYZFyTV4hjW6a1lsxeZE4ZQ6GOCwp3bDKuySvUscm4Jm7U+SRvwUK+KaUOaK0b8z2OdAp1bDKuySvUscm4Jq+Qxybyr1D/fRTquKBwxybjmrxCHZuMKzNkG5IQQgghhBAiLQkWhBBCCCGEEGnN5WBhT74HMIZCHZuMa/IKdWwyrskr5LGJ/CvUfx+FOi4o3LHJuCavUMcm48qAOZuzIIQQQgghhBjbXF5ZEEIIIYQQQoxh1gcLSqnblFKnlFKvKqU+k+b4B5VSl5RSR+JfH83RuL6jlGpXSh0f5bhSSn0tPu6jSqmGAhnXTUqp7iHv12dzNK46pdSTSqkTSqmXlFKfSHNOzt+zCY4rX+9ZUCm1Xyn1YnxsX0hzTkAp9cP4e/a8Uqq+QMaVl/8v46/tUUodVkr9Ks2xnL9forDInJLxccmcMvlxyZwy+XHJnDIdWutZ+wV4gDPACsAPvAhcO+ycDwLfyMPYbgQagOOjHH8L8G+AAq4Dni+Qcd0E/CoP71cN0BD/fSnwSpq/y5y/ZxMcV77eMwWUxH/vA54Hrht2zh8DD8Z///vADwtkXHn5/zL+2p8C/k+6v7N8vF/yVThfMqdkZVwyp0x+XDKnTH5cMqdM42u2ryxsA17VWp/VWkeBHwB35HlMAGitnwI6xzjlDuAR7doHzFNK1RTAuPJCa92qtT4U/30vcBJYPOy0nL9nExxXXsTfh774H33xr+FJSncA343//kfALUopVQDjygul1BLgrcA/jXJKzt8vUVBkTsn8uPJC5pTJkzll8mbLnDLbg4XFQNOQPzeT/n+6u+JLjD9SStXlZmjjmujY8+H6+HLfvyml1ub6xePLdJtx7x4Mldf3bIxxQZ7es/jy5xGgHfit1nrU90xrbQHdwIICGBfk5//LrwB/BjijHM/L+yUKhswp2SFzShoyp2R0XCBzypTN9mBhIn4J1GutNwC/ZTDCE+kdwm0JvhH4OvCzXL64UqoE+DHwSa11Ty5feyzjjCtv75nW2tZabwKWANuUUuty9dpjmcC4cv7/pVLqbUC71vpgtl9LzGoyp0yOzClpyJwyOTKnZNdsDxYuAEOjxyXx7yVprTu01pH4H/8J2JKjsY1n3LHng9a6J7Hcp7X+NeBTSi3MxWsrpXy4F8/vaa1/kuaUvLxn440rn+/ZkDFcAZ4Ebht2KPmeKaW8QDnQke9x5en/yx3A7Uqp87jbS96olPrfw87J6/sl8k7mlAyTOWXy45I5ZfLjkjllemZ7sPACsEoptVwp5cdNHvnF0BOG7T+8HXd/YCH4BfCHynUd0K21bs33oJRS1Yn9dEqpbbj/hrL+Dzv+mt8GTmqtHxjltJy/ZxMZVx7fs0ql1Lz470PAbuDlYaf9AvhA/PfvAJ7QWmd1r+dExpWP/y+11v9da71Ea12Pe614Qmv9B8NOy/n7JQqKzCkZJnPK5Mclc8rkxyVzyvR48z2AbNJaW0qpPwH+L24Vi+9orV9SSv0VcEBr/QvgPqXU7YCFm4T1wVyMTSn1fdyKBguVUs3A53CTctBaPwj8GrcSw6vAAPChAhnXO4B7lVIWEAZ+P0f/sHcA7weOxfclAvw5sHTI2PLxnk1kXPl6z2qA7yqlPLiTyaNa618N+/f/beCflVKv4v77//0CGVde/r9MpwDeL1EgZE7JyrhkTpn8uGROmfy4ZE6ZBungLIQQQgghhEhrtm9DEkIIIYQQQkyRBAtCCCGEEEKItCRYEEIIIYQQQqQlwYIQQgghhBAiLQkWhBBCCCGEEGlJsCCEEEIIIYRIS4IFIYQQQgghRFoSLAghhBBCCCHS+v8BPGtBl5lHFUEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 957.6x295.2 with 2 Axes>"
]
@@ -3444,7 +3661,7 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": 67,
"id": "b5b96410",
"metadata": {
"scrolled": true
@@ -3452,7 +3669,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -3479,7 +3696,7 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 68,
"id": "709f2da0",
"metadata": {},
"outputs": [
@@ -3537,7 +3754,7 @@
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": 69,
"id": "02debff3",
"metadata": {},
"outputs": [
@@ -3556,6 +3773,7 @@
],
"source": [
"def get_sample(n=100):\n",
+ " # we will use this code again later in the notebook\n",
" x = np.sort(stats.uniform.rvs(0, 2, size=n))\n",
" y_th = 1 + 0.5 * x + 1.5 * x**2 + 0.3 * x**3\n",
" y = y_th + 2 * stats.norm().rvs(size=x.size)\n",
@@ -3583,13 +3801,13 @@
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": 70,
"id": "32746edb",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxgAAAEMCAYAAABHpRDAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABYYUlEQVR4nO3deXyc1Xn//c+Z0Vi75H0B2xg5Dk4MBIjADosxS8Cs+aVpE5OlWUhN2iTQJE/2lvShW/KkTUqa/FrckCZtEpy1jW0gbMYxJLbBgAMxGIyFsQ225VXWrlnO88eZkUbySJoZzcx936Pv+/XyS9ZoljOLdJ/rPtd1HWOtRUREREREpBBCXg9ARERERETKhwIMEREREREpGAUYIiIiIiJSMAowRERERESkYBRgiIiIiIhIwSjAEBERERGRgqnwegC5mDp1qp03b57XwxARCaSnnnrqsLV2mtfj8AsdU0RE8jfSMSVQAca8efPYunWr18MQEQkkY8yrXo/BT3RMERHJ30jHFKVIiYiIiIhIwRQ9wDDGfM8Y02qM+UPaZV83xuwwxjxrjPkfY8zEYo9DRETKmzFmojHm58njywvGmLd5PSYRkfGoFCsY3weWD7nsIeBMa+3ZwEvAF0swDhERKW93Ar+21i4E3gK84PF4RETGpaIHGNbajcDRIZc9aK2NJb/dDMwu9jhERKR8GWMagaXA3QDW2j5r7XFPByUiMk75oQbjI8D9Xg9CREQC7XTgEPCfxphnjDHfNcbUej0oEZHxyNMAwxjzZSAG/GiE66w0xmw1xmw9dOhQ6QYnIlImNuxo5aZVm4lMm3eW12MpogrgPODfrLXnAp3AF4ZeSceU7CUSlpZDHWzadZiWQx0kEtbrIYlIQHjWptYY8yHgeuAKa+2wf7WstauAVQDNzc366yYikoMNO1q5fc12ImEDNhEb/RaBtQ/YZ63dkvz+52QIMHRMyU4iYfn19gN8+qfb6IkmqIqE+Ma7z2H5opmEQsbr4YmIz3mygmGMWQ58DrjRWtvlxRhERMaDuza2EAkbaiYEatujnFlrDwB7jTFnJC+6AnjewyEF2u4jnf3BBUBPNMGnf7qN3Uc6PR6ZiARBKdrU3gNsAs4wxuwzxtwMfBuoBx4yxmwzxvx7scchIjIe7T3WRXUk7PUwSuWTwI+MMc8C5wD/4O1wguvgiZ7+4CKlJ5qgtb3HoxGJSJAU/ZSWtfamDBffXezHFRERmDOphtb2nrJfwQCw1m4Dmr0eRzmY0VBFVSQ0KMioioSYXl/l4ahEJCj80EVKRESK5JalTUTjlq6+ci6/kEKbN6WWb7z7HKoibpqQqsGYN0WNuURkdOV/SktEZBxbtnA6d+BqMTAh/c2XrIRChuWLZrLw1ktobe9hen0V86bUqsBbRLKig42ISJlbtnA6yxZOx9yy+zmvxyLBEQoZmqbV0TStzuuhiEjAKEVKREREREQKRgGGiIiIiIgUjFKkRESKZMOOVu7a2MLeY13MmVTDLUubWLZwutfDEhERKSqtYIiIFEFqB+3W9h4mVkdobe/h9jXb2bCj1euhiYiIFJUCDBGRIkjfQdsY9zUSNq6bk4iISBlTipSISBHsPdbFxOrIoMuqI2H2Hesq+VhSqVqRafPOKvmDi4jIuKMAQ0SkCDLtoN0djTN7Uk3RHjNTzQfA7Wu2EwkbsAntticiIkWnAENEpAhuWdrE7Wu209UXozoSpjsaJxq3/ZP+XI1WMJ6q+YiEzaCaj9oJ4f5ULRERkVJQDYaISBEsWzidO25cxPT6Ktq6o0yvr+KOGxfl1UUqm4Lx4Wo+Wg53Uh0JF/KpiYiIjEintEREiiS1g/ZYbNjRyq2rn6GzL0ZVRZhp9ZXUV0Xo6otx18aW/vsfruYDXGqWVjBERKRUtIIhIuJTqZWLrr44FSFDLGF5/XgP7T3RkwrG50yqoTsaH3T77mic06fUEI1buvpUfiEiIqWhAENExKdSaU+VFSGwhpAxGAOH2ntPKhi/ZWlTfyBhrfsajVu+cM2b+lO1MCEtY4iISNHpYCMi4lOptKepdZW83tYNCcBYemKJkwrGly2czh24oGTfsS5mDykEX7ZwOuaW3c9580xERGQ8UYAhIuJTqVa3DcnaisMdvfTGLLUTKjIWjBei5kNERGSslCIlIuJT6WlP9VUVzGys4pSJ1XxrxbkKJERExLcUYIiI+FQhW92KiIiUStFTpIwx3wOuB1qttWcmL5sM/ASYB+wG3m2tPVbssYiIBI3SnkREJGhKsYLxfWD5kMu+ADxirV0APJL8XkREREREAq7oAYa1diNwdMjF7wB+kPz/D4D/U+xxiIiIiIhI8XlVgzHDWrs/+f8DwAyPxiEiIiIiIgXkeZG3tdYCdrifG2NWGmO2GmO2Hjp0qIQjExERERGRXHkVYBw0xswCSH5tHe6K1tpV1tpma23ztGnTSjZAERERERHJnVcb7a0BPgh8Nfn1Vx6NQ0Sk5DbsaOWujS3sPdbFnCE7bkswJRKW3Uc6OXiihxkNVcybUksoZHK+johIOShFm9p7gGXAVGPMPuAruMDip8aYm4FXgXcXexwiIn6wYUcrt6/ZTiRsmFgdobW9h9vXbOcOGBRkKAgJjkTC8uvtB/j0T7fRE01QFQnxjXefw/JFM/sDiGyuIyJSLkrRReoma+0sa23EWjvbWnu3tfaItfYKa+0Ca+2V1tqhXaZERMrSXRtbiIQNNRMqMMZ9jYQNd21s6b9OKghpbe8ZFIRs2DFsNql4aPeRzv7AAaAnmuDTP93G7iOdOV1HRKRceF7kLSIynuw91kV1JDzosupImH3Huvq/zyYIkcyMMWFjzDPGmHWlesyDJ3r6A4eUnmiC1vaenK4jIlIuFGCIiJTQnEk1dEfjgy7rjsaZPamm//tsghAZ1m3AC6V8wBkNVVRFBh9OqyIhptdX5XQdEZFyoQBDRKSEblnaRDRu6eqLYa37Go1bblna1H+dbIIQOZkxZjZwHfDdUj7uvCm1fOPd5/QHEKn6inlTanO6johIufCqi5SIyLi0bOF07sClQe071sXsDAXctyxt4vY12+nqi1EdCdMdjZ8UhEhG/wJ8Dqgf7grGmJXASoC5c+cW5EFDIcPyRTNZeOsltLb3ML3+5A5R2VxHRKRcKMAQESmxZQunj9gRKpsgRAYzxlwPtFprnzLGLBvuetbaVcAqgObm5mE3eR1NppazTdPqaJpWN+xtQiEz6nVERMqBAgwRER8aLQjJSU8PHD9emPvyr4uAG40x1wJVQIMx5ofW2vcX+oHUclZEZGSqwRARKUfWQlsbvPoq7NkDJ054PaKistZ+MdkKfR6wAlhfjOAC1HLWK4mEpeVQB5t2HablUAeJRN4LUCJSZFrBEBEpJ319brXixAlIJEa9uuRupJazSn8qjqCuGmn3dhmvFGCIiASdtdDZ6QKLLrWytdZuADYU6/5TLWfTgwy1nC2u4VaNFt56iW+DuqAGRSKFoBQpEZGgisfh6FF45RV4/XUFFyUylpazSvPJTxA3KlQqnYxnWsEQEQmaVNF2e7tbvZCSyrflrM5o5y+Iq0ZKpZPxTCsYIlL2Nuxo5aZVm7n4a+u5adVmNuxo9XpIubPWBRR79gwUbSu48Eyq5eySpqk0TavLKkDQGe38BXGjQu3eLqMp5xVNrWCISFnbsKOV29dsJxI2TKyO0Nrew+1rtnMHBGNfiVjMdYNqa3P/l8DSGe38BXGjwlRQNHTFys9BkZROua9oKsAQkbJ218YWImFDzQT3565mQgVdfTHu2tji7wCjq8ulQXV0eD0SKZAgpvn4SdA2KgxiUCSlE8TGBblQipSIlLW9x7qojoQHXVYdCbPvmA8LouNxOHbMFW3v21e44CIeh02bCnNfkrcgpvnI2OSTSifjg9eNC4qdnqUVDBEpa3Mm1dDa3tO/ggHQHY0ze1KNh6MaorvbpUAVsmjbWti+Hdatg3vvhdYA1p2UGZ3RFpEUL1c0S5GepRUMESlrtyxtIhq3dPXFsNZ9jcYttyxt8nZgiYRLgdq9G/buLVzR9quvwre/DddcA+96F/zwh3DmmfDNb479vmXMdEZbxqqcC4PHEy9XNEvRcEIrGCJS1pYtnM4duFqMfce6mD2phluWNuVVf7FhRyt3bWxh77Eu5uRwP+m3O72ugo+9ZTIXzawq3E7bhw7BfffB2rXw3HNgDJx/PnzkI3DVVTBxorvepz5VmMcTEU+Ue2HweOLlimYpGk4owBCRsrds4fQxF3SP1I0KGDbw2LCjldt/9QcaYj28IdaFPdbNd3bvpuLyBSyePyX/AbW3w4MPuhSozZtdsPLmN8NnPwvXXQezZo3p+YqI/5R7YfB441XjglKkZ3kaYBhjPgV8FLDAc8CHrbX+3ZZTRMat4bpRffX+F+iKJjK3wX3DZH503zPMbTtCbarOPBIG4qzeujf3AKO3F37zGxdUPPoo9PXBnDlwyy1www0wf34hn7KI+IxaHUshlKKFsmcBhjHmVOBW4M3W2m5jzE+BFcD3vRqTiMhw9h7rYmJ1ZNBl1ZEwO1s7mD2pelDgEe/o4KdrtrDsnQvpeP0A9VWDb1dZEeZgW3d2DxyPwxNPwJo1bsWiowOmTIH3vMcFFWef7VKiRKTsqdWxFEIp0rO8TpGqAKqNMVGgBnjd4/GIiGQ0XDcqcIFGOB6jpqeTmp5OQrEYx3uiYC0zG6s52tlLVVqr3N5YnBmN1cM/mLWulmLdOldbcegQ1Na6eorrr4clS6DC6z/fIlJq2rxPCqXY6VmeHaGsta8ZY/4J2AN0Aw9aax/0ajwi4i/5FlQXyy1Lm7h9zXa6+mJUR8J0R+NEYwnOrIOKQweYSLT/uj1pAcSK5jncuX4nEKeyIkxvLE40blnRPOfkB2lpcUHFunWuG1QkAsuWuaBi2TKo0lnK8SaRsOw+0snBEz3MaFBb2/FOrY4lKLxMkZoEvAM4HTgO/MwY835r7Q+HXG8lsBJg7ty5pR6miHhgpIJqr4KM9G5Ura3HWVCV4MPnTIFENXeuP0RP2GQMIBbPn8JtwOqteznY1s2MxmpWNM8ZqL84eHCgA9T27S7dafFi+LM/cysWjY2ePF8ZWSkm/uoYJJkEbUdzGZ+MLdSmTrk+sDF/Aiy31t6c/P5PgSXW2r8Y7jbNzc1269atpRqiiHjkplWbT0pH6uqLMb2+intWLsnrPse8IhKLuc5NbW2uuDrNll1Hhg8gMjlxAh54wK1UbNniUqIWLXIrFdddBzNm5PUcR2POOOMpa21zUe48gPI9ppRq4t9yqINrv/XYSfn296ljkIj4gDFm2GOKl0m8e4AlxpgaXIrUFYCiBylLfkv38bvhCqr3HevK6/7yXhGx1hVVnzgBncNvQLR4/pTRO0L19MCGDS6o2LABolF6TpnNhkvfxYOnvxV7epMLTGaMoXWtlESpWoWqY5CIBJWXNRhbjDE/B54GYsAzwCqvxiPjWzEDAD+m+/jdcAXVsyfV5HV/w7WYvWtjS+b3oLfXrVS0t7suTvmKxdwKxbp1Ax2gpk6Fm27iD+cu5R/2VRCpCLnUqs5e7ly/k9tgbPtjSNGVauKvjkEiMhq/1ml52obEWvsV4CtejiEodAa8eIodAOQ8uZXMBdVxyy1Lm/K6v6xWROLxgRSo3t78B28tPPusq6m4/344fBjq6lw9xQ03uPqKcJi7f7KNSMVAd6mqseyPMZrKSteFSgqiVBN/dQwSkZH4uU5LfQ4DQGfAi6vYAUCh033Gg/SC6n3Hupg9xqB6xBWRri4XVHR0uOAgX7t2DXSA2rPHdYC67DJXV3HppSd1gDrQ1j22/TFGYgxUV7vAprbWjUUKplQTf3UMEpHhJBKW51477tud3RVgBIDOgBdXsQOAQqf7jBfLFk4v2Od76IpIb28flZ2d3HZOHezbl/f9Pr1lB3t/9DPOeuYxmlpfxRqDWbLE7ax91VXQ0DDsbfPaH2MkFRUumKipcV9DofzuR0ZVyom/OgaJyFCplYsdB06Mmq7pVQqVAowA0Bnw4ip2AFDodB/JXWpF5O5HdtC2/yBnVlpWLJ7N4jnDBwDDamuDBx/kxE9/yTnPPsN5WF6Z1cSPLn8vv33jYj544/lZpTjltD9GJqlVitpa92/ChNyfi+RNE38R8Uqq0cRHL2kaMV3TyxQqBRgBoDPgxVXsAKDQ6T6So0QC2ttZVtvLsrfPBGbmfh/d3fDooy79aeNG1wFqyiweuej/8PTZF9E6ZRYAPdHsayhG3R8jk0hkIKCortYqhYjIOJRqNPGLp/Zx6+UL+Nb6nRnTNYfrePfm2y4hYSnqqoYCjADQGfDiKkUAUMh0H8lSd/dAJ6h8aitiMdi0aaADVFcXTJsG73sf3HADt/32BPXVEzBpf5NzraHIqr1tVZWrpair0yqFz/m1m4uIlJdUo4n9bT389+ZXufniJsIhuGLhdM46dWL/351MHe8m1Uzg6T3H+dL/PFfUVQ0FGAGgM+DFpwDAGwXvjhaPuz0rMmyGlxVrYds2F1Tcdx8cPQr19XDNNa4D1AUXQNjVTMzcvq2wNRQpodBAHUVtrautEN/zczcXESkv6Y0m9rf1cPfjLXzj3ecMCi4gc8e7P2me3R9cQPEKw3XkCghNgKXcFKw7WvpmeF1d+a1WvPyyayu7bp0r+p4wwXWAuuEGWLrUtXkdYsw1FOkqKgZWKaqrGbQsIoFQqs33RESybTSRqePdG6fXl2QfHwUYIuKJMXdHS26Gt+WZV1j9xKscaOtmZjZ1DCn798O997qg4oUX3MrBhRfCJz4BV17pVi5GkFcNRboJEwaCiiptnBZ02nVbREopm0YTmQIRaynJPj4KMETEE7l2R9uwo5W7frOLIwcO88bKOB94ywwA7ly/k0jYUF8V4ehou2EfPw4PPOCCiiefdKsdb3kLfPnLLg1q2rScnkNWNRTpVE9RNMaYOcB/ATMAC6yy1t5ZqsfXrttSaKrpkUIYGogkErYk+/gowBAJoHLY2T2X7mi/eXYv3/j5kzRGu5kXNrR3x7lz/QlqImEiYTPybthdXa4D1Nq18PjjEI3C6ae7lYobboDTTivek0zf8K6uTvUUxRUDPmOtfdoYUw88ZYx5yFr7fCkeXLtuSyGppkeKpVT7+OhoJxIw33r4Jb6zYRexRILKcIh4IhHInd1H7Y4Wj7sOUG1t/O+vtjCpr/ekQGLvsS5OGzKBq6wIc/hoO/zmNy6oeOQRF2RMnw4f+IALKt70puLVOYRCrjg7tYv2KK1kyyFY9ANr7X5gf/L/7caYF4BTgZIEGNp1WwpJNT1STKXYx0cBhkiAbNjRync27CJhLZFwiLiFIx1RptRxUu2C3yeuw3ZHm1vn6iM6OvoLtg+0dVNfNTidqrLCBRu9sbgLOKylad9O3vrs4yx58Qnoanc7aV9/vfvX3NzfAargJkwYvD9FlsFLwQrdZRBjzDzgXGBLKR9Xm+9JoaimR4JOAYZIgNy1sYV4wlIRMhgMxkACS1tXlH2hgdqFoExc+7ujxWID7WX3HT/pejMbqzO2hJ09sZop+/dwyYubedsLm5nadoi+igjtb1tK3Yo/ch2gilHrUKBdtMdc6C4nMcbUAb8A/tJaeyLDz1cCKwHmzp1b4tFJOtUYDE81PeNXufxeKMAQCZC9x7qorAgRi9v+k+TGQG88Mah2IRAT10RicHvZEQxtCVt35CDLtm/mxn1PU/vKyySM4Q9zF/Hg5X/CG9/3TprPKsLEMRweCCiySH3KRq6F7jIyY0wEF1z8yFr7y0zXsdauAlYBNDc359HTWApBNQYjK3ZNTyJh2XO0k4Mneunsi3Ha5FpOnxrMiWw5KaffCwUYIgEyZ1INsXiCI519kHDBRdxaKkKhQTu7+3ri2tXlgoqODhdkZGHx/Cl8pu04LT/+JWc9vZGFr73kfnDOOfDXf01o+XLOnjqVs4fcbsuuI6zeujf3FrYpqVayqdSnAksvdD/RHeVwRy+9sQQ1E8Js2NHqn2AwAIwxBrgbeMFa+w2vxyMjU43ByIpZ05NIWNa/eJCdBzu485GdgZ/IFpqXKwjl9HuhAEMKwu/5/qVS7NchVRg9pXYC7T0xemMJwiHDx5fNH/Q4uXRoKomeHlew3d7u0qGy1dUF69fD2rW89fHHeWssBvPnw223uWLtOcNvardl15HcWtiCi9jSd9GORDJfr0BS7+eh9h4XNAIGqK0M+zKlzecuAj4APGeM2Za87EvW2vu8G5IMRzUGoytWTc/uI508u6+NVRtbymIiW0heryCU0++FAgwZs6Dk+xdbKV6HYQujh9z/qB2aSqGvbyCo6OvL/nbRKPz2twMdoLq7YeZM+NCHXFBxxhlZFVGv3rp39Ba24IKIVEBRU1PSXbRT7+etq58hYS1VFWGm1VdSXxXxX0qbz1lrH8fFZxIAqjHwzsETPSQsZTORLSSvVxDK6fdCAYaMWSDy/UugVK9Df2H0KNfJJhApuHjcpT+dOOF22h5FKoXp4PFOlrTt5T0HtjHjt4+6DfEmToQbb3RBxVvfmnPNw3Cdpw62dQ9seFdbC5WVOd1vPkZa2Vq2cDoN1RHmTq7BpAU3vklpEykC7RvinRkNVYRNaXZzDhqvVxCy+b0IShG4AgwZM1/n+5eQ316HbAKRgunqch2g0lrLjmbLriP88ifrueLFzSx5YTNT2w7TWzGBwxctZeqKd8HFF4+pA1R65ylrQvRMqOKYiVA/eyKUsHtQNitbvktpEyky7RvinXlTajlrdiO3XbHgpBqM8R7geb2CMNrvhdcpXLnwNMAwxkwEvgucCVjgI9baTV6OSXKnyZEz7l6HaNQFFSdO5FZXsW8f3Hsvc374M77Wupe4CfH8/LP51bJ3s6XpHGonNfLNy88Z8/De87bT+YeNe4lV1xCqqaE7liAat3xm2YIx33cuslnZ8kVKm0iJlWrfkKCc8S2VUMhw+RkzeMO0Os6bO4muvhhz1UUK8MfK2ki/F16ncOXC6xWMO4FfW2v/2BgzASjTmVh50+TIGRevQzTqaio6OlzhdraOHoX773d1Fc88A8CJUxaw4eoP8tSbl9BR2+CuZ3EpTPkYsjfFkjdO4NOzTytamli2Bf3ZrGx5ltImUqZSQcWRzl5eP97D53/xrO/P+JZSKGSYN7WOeVNHnpQGKTgrxFj9vrLmdQpXLjwLMIwxjcBS4EMA1to+IIdKUPELTY6csn0dEgkXVJw44Qqus9XZCQ8/DOvWuaLteBwWLIBPfQquv567fns44+Z51ZEwn/rJtuxay4ZCrjA7VU8xZKfuYqWJ5VLQn+3KVklT2kTKWHoayc0XN3H34+qWlI8gpeMUcqylWlnLh9cpXLnwcgXjdOAQ8J/GmLcATwG3WWs7PRyT5EmTI6esXofUfhXt7VnXVdDXN7gDVE8PnHIK3HwzXH+96wCVtKK5etDmeb2xOO09MQwQs3b41rIVFQMBRYm7PqXkUtA/Lla2RIokn7PS6WkkxqhbUr6ClI4TpLGOhR9SuLLlZYBRAZwHfNJau8UYcyfwBeCv069kjFkJrASYW8LCTJFxKRod6AIVjWZ3m0QCnnrKBRUPPDDQAeqd73RBxXnnZewAtXj+FG7DtZM92NbNjMZqIqEQ0UTipNay//X7VhYvPqNkXZ9Gk0tBf9mubI2BMeYiYJu1ttMY837cseBOa+2rHg9NfCTfs9JD00iCcsbXb4KUjhOksY6F31O40nkZYOwD9llrtyS//zkuwBjEWrsKWAXQ3Nyc5WlUERlq2JqBeHxgv4psU6CshRdfdEHFvffC/v2u/uGKK1xQcdFFWXWAWjx/yqD0p5tWbaK+KtLf9alnQjU9Eypp6U3A5Mn5PvWCy7Wgv6xWtgrj34C3JFevP4Nr9vFfwKWejsojQcpzL6V8z0qnp5H84ql93Hr5Ar61Xt2SchWkdJwgjXWs/JzClc6zAMNae8AYs9cYc4a19kXgCuB5r8YjUs6G1gwcauviqz99ggmXzuHCU2qyT4Hau9cFFGvXwssvu3Sliy6Cz3zGBRc1Y+jTEA7TOHMae/sMobq6/tSnrr6Y7zpxKe1pzGLWWmuMeQfwbWvt3caYm70elBeClOdeavmelU5PI9nf1sNPtu5h1QeaiYRNyQO4IAePQUrHCdJYxwuvu0h9EvhRsoNUC/Bhj8cj4qlsOxPlKlUzMMlGqTlxnKrebnqjMX62sYsL33POyDc+csR1gFq3rr8DFM3N8JWvwPLlY1tZSNVT1NdDdTUrrq93gVA07uuJu9KexqzdGPNF4P3AUmNMCIiMcpuyNF5yx/OR71lpv6SRBD149MvrmI0gjXW88DTAsNZuA5q9HIOIX+TSmSgnvb2c2LefefRRYeP9F/fvap1JR4frALV2LWza5NKozjjDrVRcdx2cemr+45kwwQUVdXVuR+00QZq4K+1pTN4DvBe4ObmaPRf4usdjykqhz0iPl9zxfIzlrLQf0kjKIXj0w+uYrSCNdTzwegVDRJJy6Uw0qmjUBQknTkBvL2+MRDna2UfFkJawMxqrB27T1wcbN7qVivXrobfXBRJ/9meurmLBGDaoq6oaCCpGqc0o9cS9WKtGMjxr7QHgG2nf78HVYPhaMc5Ij6fc8VwF/ay0gkcZzxRgiPhELp2JMkoFFe3tJ22Ct6J5zkktYaNxy4rzToUnnhjoANXWBpMmceDt1/OTmW9hc8McZk6sYUVoMotzeTKpTe9SQUWFP//UFG3VSDIyxrQDmQp+DGCttQ0lHlJOinFGWrnjIwvyWWkFjzKe+fOoLzIO5dqZCBgxqEg3qCXs8S7O6z7Ie1uf5ZT/fgQOHnTF2VdeCddfzxMzF/IvG18hEjbUV4Qz70WRiTGujWwqqMjQmtZvCrpqJKOy1tZ7PYaxKMYZ6aCfpZfhKXgcn4Jc2F9ICjBEfCLrzkSx2EBb2WGCii27jrB6697Bu2FHOll8+LcuBaqlxa0qXHIJfO5zcPnl/R2g7vnJNiJhc9JeFKu37j05wAiHB4KK2lpPNr0bizGvGsmYGGOmA/2nc5OpUr5VrDPSQT5LL8PzMngs50mun59b0Av7C0kBhohPjFjgnEgM1FR0jTz53bLrCHeu30kkbDg11sk5v3mYyf+2Cfbvclc4/3z44Afh6qth0qSTbn+grZv6qsGT7kEF4aki7dpalwZVYKWsichr1UjGzBhzI/DPwClAK3Aa8AKwyMtxjUZnpCVXIwWPxZool/Mkt1TPLd/3phwK+wtFAYZIkh+KfQcVOFvrgon9+11wkeVeFf/z+Isse/53XLRjM2965Q+ErOXV6XNZ+/b3csOXV8KsWRlvl1r1ONrZx7HOPqbWV1Jb6f5EtBOm9tSpcPrpECleN9FS10RoPwvP/C2wBHjYWnuuMeYyXMtaX1M6kxRKMSfK5TzJLcVzG8t7o8L+AQowRPBZsW9vryu2bm937WGzkeoAtWYNdzyyngmxKIcmTuP+i97Bk4vexutTZ9PRE+WGEYKL1KrH1LoJHDzRx57OBI3V1fTV1NFtKrjjmkVFDS6g9DURQWqLW2ai1tojxpiQMSZkrX3UGPMvXg8qG+WWzuTndJNyVsyJcjlPckvx3Mby3gyXRjmzoYqWQx3j6vdMAYYIPij2TdVVJNvKZiUedx2g1q1zHaDa22HyZLa89XIeXbCY1057Y39NRG90SEvaIVZv3UskbDA1tcQrq0lMjtDaFeVgHM6bVF+ySbcXNRHaz8ITx40xdcBG3GarrUCnx2MaF9IDilmNVTy/v70sU2n8rpgT5XLuXlWK5zaW9yZTGuW333vuuPw9U4AhQnYT24KnUMXjAx2gRqmr6GctbN/ugop774XWVlecfdVVbq+Kt72NCa+2sWP9TiKxxOCWtM1zTr4/Y6CmhucTVVTOnI4Nuz8JtcAbaitp645yz8ol+T/HHKkmYtx4B9ADfAp4H9AI3OHpiMaBoakft17xBlZtbCnLVBq/K+ZEuZxrhUrx3Mby3mRKo7QWrvvXx8bd75kCDBFGn9gWLIUqFoPOThdUdHdnXVfB7t0uqFi3Dl55xaUqLV3qgorLLhtUbD2oJW1bNzNSXaRSHaCSQQX19a5QOxxm8qy97vkP7MPnycReNRHjg7U2fbXiB54NZJwZmvqRsHieSjNeU7SKOVEu51qhUjy3sb43Q9MoN+06POLvWbn+DijAEGH0ie2YUqhisYGViu7u7AfV2gr33eeCiueec4HBBRfARz7iOkA1Ng5708XzpwxuKZseVGTYo8IvE3vVRIwPQzbcmwBEgE6/b7QXdJlSP7xMpSnnbkejKfZEuRi1Qn6ZCBe7DqrQ781IKyLl/DugAEOE0Se2OdcG9PW5lYqOjtyCivZ2ePBBF1Rs3uza0y5a5PaquO46mDkztydWXQ0NDS6oCIeHvZqfJvbD1UT4ocuXFEb6hnvGGINLmSpdLt44NXSi84un9nHbFQu485GdnqTSlHO3o2wEqWFAOU+EMynkezPSikg5/w6MiwBDExPJxkjFvlnVBvT1DWyA19eX/QP39sJvfuOCikcfdbedMwc+9jGXAjV/fvb3lVqpSG1+V5H9r7ifi5191eVLCspaa4H/NcZ8BfiC1+MpZ0MnOse6+lgwo457P3kJhzpKn0pT6m5HuZyB98vZer8I4kTYL+/hSCsi5dzxq+wDDE1M/C8IAeBwKUQfe9scOHrUBRXZdn+CgQ5Qa9a4FYuODpg6FVasgBtugLPOyn5X7FBo8G7aQ9KfyoHnXb6koIwxf5T2bQhoxhV9SxGNNNGZP730k5lSdjvK5Qz8eDtbn42gTYSHew+vetMM9hzrKnnQMdyKSDl3/Bo1wDDGfBL4obX2WAnGU3CamPhbUALA9BSi/YfbmV8DHz1vKm+r7ITDWXbXtNbVUqxb52orDh1yAcHVV7uVisWLs19xCIVcQFFf71Yssg1GAiYVfD6x+yiVYcP0hqr+XcaL3b5WiuqGtP/HgN24NCkpMj+l5ZSy21EuZ+CDeLa+2II2Ec70Hn7t1y8QjSf4/C+e9U3gWM4dv7KZzcwAnjTGPA18D3gguaTtC6Od/fair76MLP09O9EdpbYyTGO1+yPl2wAwFmPZzAksu+bU3GoqwHV9WreO7l/+iurX9xINV/DCG8+l+qO3sWDFDVCV5R9oY1xQ0dBQ1kFFSnrwWVURoi+e4PXjPZwyEeqrImpfG2DW2g97PQbxXim7HeVyBj5oZ+tLIWgT4Uzv4fVnn9ofXIA/Asdy7vg1aoBhrf0rY8xfA1cBHwa+bYz5KXC3tXZXsQc4kmzOfquvvr8Mfc8OtPXQ3RensiLsvzPT0ehA96eeHLM3Dh6E++93KVDbt2ONYdecN7Fl+c08u2gxxyuqiEYtt73WyeL5IwQYo3R/Kmfpq49T6yp5va0bi6X1RA/hkFH72gAyxvwrA92jTmKtvbWEwxEfKNWKSi5n4IN2tr4UgjYRzvQehkPet2XOxE+rioWU1WwluWJxIPkvBkwCfm6M+f+KOLZRpU9AjHFfI2HDXRtb+q9zy9ImonFLV18Ma91XTUy8M/Q9q6wIgYFD7QP1C54GgL29cOQIvPqqW3k4dCj74OLECfjZz+CDH4RLL4V//Ed3+Re+wN985v/y9Zu+wBPNl9NTXUtVJEwkbFi9dW/m+6qthRkzoKkJTj3VrVqMo+AC3OpjdcR1vmqojnBKYzUTwiF645bp9VXcceMif61ySTa2Ak8BVcB5wM7kv3Nw7WrHxBiz3BjzojHmZWOMCsalX+oMfFXE/R0d6Qx8LtctZ4mEpeVQB5t2HablUAcATdPqWNI0laZpdb4NLiDze3j+aZP7v08Z74FjMWVTg3Eb8KfAYeC7wGettVFjTAh3YPhccYc4vGzSn/zUfjMIil1wPfQ9S52Z7onFsdZ6s/9CT8/ASkU0mvttN2xwdRUbNrjbz5sHH/+4ayvb5J7HS6s2UV8xuE1sZUWYg21p6VaVlS6QqK/PqftTuRq6+thQHaEibJheX1XS3cWlcKy1PwAwxvw5cLG1Npb8/t+Bx8Zy38aYMPAd4O3APlxq7xpr7fNjG7WUg1zOwGe67txJNb7oSFQqQS90H+49DFKaV9BlM4uZDPyRtfbV9AuttQljzPVjHUDyoLAVeM1am9P9ZZv+5Of2m35SioLrTJPG3licrr44bd3R0gSA1kJX18A+FbFYbrePxWDLFli71nWA6uyEadPgve91HaDOPPOk+oiZjdUc7eylKjIQZPTG4kybVAeTJrnAorKyEM+ubPhl8z8piklAA3A0+X1d8rKxuAB42VrbAmCMWY0rHFeAIUBuqSjp1w36ZDsffi10z6X1bKb3u9RpXn5pleuFUXMurLVfGRpcpP3shQKM4TYgr/vJNf1pw45Wblq1mYu/tp6bVm1mw47WsYy77GSTcjZWmd6zCRVhvrXiXB77/OXcs3JJcYILa10wsX8/7NoFr70Gx49nH1xYC7//Pfzd37n0p498BB56CJYvh+9/3+1j8aUvDdtedkXzHKJxS080TgLDkVAV++um8M53XeKCEwUXJ1m2cDp33LiI6fVVtHVHlRZVXr4KPGOM+b4x5gfA08A/jPE+TwXScw73JS8bxBiz0hiz1Riz9ZUDR/nmQy8BcNk/baDlUAfP7Wvj+n91iyl/t+55/iP59++Cv3+Ygyd62LTrCO+5axMAX/zls/x4yx4AFt3+azp6Yzz8/EFu/v6TANx6zzP8attrAMz7wr0A/Grba9x6zzMA3Pz9J3n4+YN09MZYdPuvAfjxlj188ZfPAvCeuzaxadcRDp7o4YK/fxiA/9jYwt+tczHT9f/6GM/ta6PlUAeX/dMGAL750Et6TgV+Tu/97mZuW/3MoMn2rauf4ZPJnwfxOY32Pn38x09nrFf45wdf9Ow5ffLHT/O3657n2m89xk3/sYVrv/UYf7vueT7546ezek7vuWsTW145Sm1lBbfes42maXXc/fgrRXufEgnLB763hav+ZSM3/ccWLv/n3/C/217joe0Hyub3aSTGy4ZQxpjZwA+Avwc+PdoKxhlnnmPPu/XfB6XvQHbpT+ln59PPhmrCMuDir61nYnUEkzZBttbS1h3lsc9fXrDHSaVhFT1lLbVS0d7ugotEYvTbDLVrl0t/WrcO9uyBCRPgsstc+tOyZdkHBuEwv3u9m1XbDrGrM8HsybVK1ZOSM8Y8Za1t9nocAMaYmcDi5LdbrLUHxnh/fwwst9Z+NPn9B4DF1tpPDHeb5uZmu3Xr1rE8rIwDm3Yd5qb/2HLS5atXLmZJ01QPRlT8M+Mthzq49luPnVTofp+HKxh+HNNIxjLeoKx8jHRM8TrR+19wNRz12Vz59ePdzG7vGZy+c+OirPKxtR/G6HLpuDWWWo2ip6ylBxXxeO63P3AA7r3XBRXPP++Kq5cscTtrX3WVq5HIRjg80P2ppoYL58OFl+Q+HJFyYYxZaK3dYYw5L3lRasXhFGPMKdbap8dw968Bc9K+n528TCRviYQlFre+6ipVipQtP7alDVr74HzHWy4peZ4FGMn6jVZr7VPGmGUjXG8lsBKgctKMvAME7Ycxumxz3n23OZ61gwu1c62pAGhrc/UUa9bAk0+6+zzrLPjSl3j6zUv475ZuDhzpZuZ9u1jRPIfF86dkvh9jXAeohgb3tcz3qhDJ0adxf8//OcPPLDCWpdIngQXGmNNxgcUK4L1juD8Rdh/p5K9+9Ry3Xr6Ab63f2T/h+9q7zs57sj3Ws9OlqI8YS1vaYp19D1r74HzH69f6l1x5uYJxEXCjMeZaXMvCBmPMD62170+/krV2FbAKYOLchYPyuXIJELQfxuiy7bjli9WgRILHntnNj9c/z5FDxzm1fsLIE/9Murvh0UfdSsXGjQMdoD7xCbez9rx5bNl1hDvX7yQSNtRXRTja2cud63dyGwx+rKqqgQ5Q4fBwjygyrllrVya/XlaE+44ZYz4BPACEge9Za7cX+nFkfDl4oodXj3Tz35tf5eaLmzDGnX86dWJVXpPmQpydLtWZ/Hz2Zyjm2Xc/rqqMJN/xBm2lZjieBRjW2i8CXwRIrmD8P0ODi6ESQ+pFcgkQ8u1IU+y2rX6TTfqSZ6tBiUT/KsWW5/byb4+85FZRJoSHn/gPFYvBpk0uqHjwQZdONX06vP/9LqhYtGjQqsPqrXvdTtLJ7k/ua5zVW/ey+IwZLqhoaHC1GSKSFWPMnwC/tta2G2P+Crcnxt9aa58Zy/1aa+8D7ivEGEVg4Cz0/rYevvPoy4A7C/2u807qH5CVQpyd9vOZ/GKefS/2Zn+FXnnJd7x+fn9z4XUNRk6sJe+Wlfnsh+G7VCCfKOlqUCLh2sCeOOGCgWSQufrJPcNP/IcGGNbCtm0uqLj/freRXkODK9S+/no4//xhVx0OtHX37zAOYDEkauv4AxP697gQkZz9tbX2Z8aYi4Erga8D/85A0beILxT6rHkhzk778Ux+anL+0sH2op59z7SqUojAoFgrL/msAvnx/c2HLwIMa+0GYMNo1ztlYjXT66vy7j6Ua3GxL1KBfKjo+xOkgor2dvc1Q6ezoRN/cBvXvXq4g0/9ZBsH2rp5S+8RPnDoWU797SOwb5/r+HTZZS6ouPTSrFYdUvtXhKtr6Kqqobuqho6YDdyZBBGfSXVfuA5YZa291xjzd14OSCSTQp81L8TZ6WKfyc9V+uT8o5c0lfTse6ECAz/VPfjt/c2XLwKMbNVXVZR0B18VhmdWlN3RYzEee2Y396x/nqOHjzOroWrEmopMG9cd6+qj5ughFj/7IBe+sJm5B18lYQzHzz2fiR//uOsAVZfDH4qKCv74yrP460f3YionBHqzt/GW6ie+95ox5i7crttfM8ZUksW+TCJeyOcs9HAKdXa6kGMaq/TJ+S+e2ndSQXwxz74XKjDwW92Dn97ffAUqwCg1FYYPryCtZmOx/nayW/6wj39LFlM3VFaMWlOxonkOd67fCcSZFO3mLds389ZnH+fs/TsBeOWU+ay+6gP8bsH5hGfM4Jt/dE52YwqFXBDS0AA1NVzUBLdPmlKafTuKRKl+4kPvBpYD/2StPW6MmQV81uMxBU5QeuWXmp9fl3I5O50ufXK+v62nvyD+7FMbWDCjvqjPr1CBQbnUPfiJAowRFD0VaDyKRgf2qOjp6b94xGLqDAHG4lNq+H/Du+n93zUs3LmNikScPRNn8p/NN/LYGYvpO2U2tZUVbgPvtu6Rx2QM1NS4oKKu7qTWskXft6PIlOonfmOt7TLGtAIXAzuBWPLruJft5LhceuUXWhBel3I4O51u6OR8f1sPdz/eUpIN8AoVGJRL3YOfKMAYQVFSgcaj3l4XUHR0uP9nMFxNxcH04CAahd/9zhVrP/wwC7q6YMYMXn/ne/jXmoU8UzuDhDWujeCJXqY3QDhkmNFYnXlclZUDXaDKuLWsUv3Eb4wxXwGagTOA/wQiwA9x7cvHrVwmx37KGfcTvS6l5+XkvJApZ+W2suQ1BRijCPrZa09Y6/aY6Ox0QUU0OupNMtVU9MbizGiogqefhrVrXQeoY8dcQHD99e5fczNf//lzHO3sZUrc0trei0nWhB9u72VyXSUrmtM2903trt3Y6AKMcUCpfuJD7wTOBZ4GsNa+boyp93ZI3stlcuy3nHG/8Mvr4uc0rULzcnJeyMcut5UlrynAkMJIdX7q6HBfE4nRb5MmvaaisiLM1P2vsnj777h691NwcL8LBi6/3AUVS5cO6gCVWv1ILYAc6+ojGrdY4LbLF7gUq5oaF1QMSYEaD8XPSvUTH+qz1lpj3OkAY4zyEMhtcqyc8cy8fl0SCcueo508vec4X/qf53ybplVoXk7OFRj4kwIMyV8sNhBUpO1RkY/F86fw2YP72ffjX/CWZzYy9/A+bCiMuehC+PRfwpVXDtsBKn31o66qgrqqCnqicSbW17D4/AUusIgMThHasKOVr97/AjsPdRAJhZjRUFm2xc9K9RM/McYYYF2yi9REY8yfAR8B/sPbkXkvl8mxcsYzK/TrkstKRCrFbceBE6za2KI0LRnXFGBIbvr6Buop0oq083b0KDzwAKxdy7lPPcW5AOecA3/+Ycw118CUEXblThq6+tEWinCsbiIfflczTJ160vVTXZVa23sIG4MF9rf1csrEKiJh01/8XE6rG0r1E79Irlz8CfBp4ASuDuN2a+1D3o7Me7lMjpUznlkhX5dcC8ZTKW4fvaTJF2laIl4KVIDR3hPjplWby2LC52dDJ9Z/fsEslp5a41Yr+vrG/gBdXfDII65Y+/HH3UrI/Pnwl3/pUqDmzBn1LtItnj+FWyvCfO+5o7zUE2LW1Aa+NMJnI9VVKZ6whI3BGEMCy6H2Xk6fWsu+Y11q7SpSXE8Dx621ak2bJtfJsVJDMivU65JrwXh6ipvS12S8C1SA8frxbma392jCV0QbdrRy+6/+QH28l3mJXnhlP/++8yUqU7UM+YpG4be/dcXajzziisBnzYIPfQhuuAHOOOOk9rCjMgZqa6GxkSULFrDk7dndPtVVaUI4RCxuMQYSiQRdMdhxoJ2aCWG+9usdgWntOtpKSzmtxEjZWAy8zxjzKtCZutBae7Z3Q/IHBQ3+kWvBeCrFrdSbzYn4UaACDGMIxIQvkOJx6Ozkp2u2MO9YG9WR5Ka6FYYea4bdj2JEicRAB6hf/xqOH4eJE+HGG92/885zG9vlKhJxdRWNjXm1l011VZpaV8nrbd3EYwliyfIRA9RWhnmptYPZEwefbSp2a9d8AoHRVlq0EiM+dbXXAxAZTa4F4+kpbv+9+VU+feUCFs5qIBa3nJYMLsZTdykZ3wIVYISGnOFWL/8x6usbKNLudvtNHGs9Ovp+FKN58UUXVNx7L7z+OlRVwRVXuPSniy8e1AEqJ3V1LqioHdtZoFRXpUjYcEpjFXuPuedWVRFiZmMV9VURjnVFOXiil4bqgbEWs7VrvoHAaJvoaZM98SNr7atej6GUNKkMplwLxtNT3I529vLa8R5W/vdT/bf99nvPpS9mfb0JoEihBCrASAzpUqRe/nno7h5oJZuhnmLY/SiG26wuZd8+F1CsWwcvveRWFi6+2NVVXHll/kFBRYXb92LiRPf/DHI98z+0q1IkHGJmQ+WgYGJGfSX7jneXrLVrvoHAaJvoaZM9EW8FYWdpv/FLQJZPwXgqxQ3g/Xc/Mah+49l9beouVQR++bwUQ5CfW6ACDGvJOOFTjvkIEglXVJ0KKuLxEa8+tCNTb8y9zoM2q0s5etRtfrd2LTzzjLvs3HPh9tvhmmtg8uT8xmyMW61oaBg1MMn3zH96V6WbVm2mtX1wR6yKcIgF0+qYVFtZktau+QYCo22ip032RLxV6p2lgzwhAf8FZPnWxGSq30hY1F2qwPz2eSmkoD+3QAUYp0ysZnp91aAJH6Ac86Hi8YFWsjnuT7F4/hRuA1Zv3cvBtm5mNFazonnOQP1FZ6cr0l671hVtx+PwxjfCZz4D114Ls2fnP+6qKhdU1NdnXVtRiBSg4Tai++vr3lyyz1C+gcBom+hpkz0Rb5VyZ+mgT0ig9AFZsWSq3wgbdZcqtHL5vGQS9OcWqACjvqqCe1YuGXTZTas2K8ccCro/xeL5UwYXdPf1wfr1Lv3pkUfc/Z9yCtx8s6urOOOM/B8sHHZBRWNjXrUZhUgB8sNGdPkGAqON3Q/PTWQ8K+XO0kGfkEBpA7JiylS/cdbsRm2OWGDl8nnJJOjPLVABRibjOse8p2cgqCjE/hTpEgl46im3UvHAAwMdoN75ThdU5NkBasuuI6zeupc9XXEaZ07nT686k2XTpuU9zEKlAGWzEV0xU/HGEgiMNnZtsifinVLuuB30CQmUNiArpuHqNwBtjlhA5fJ5ySTozy3wAca4yjG3dnA9RSxW+PtP7wC1fz9UV7sOUDfcABdd5FrE5mnLK8f4+0376audSEVjNa9G49y+9nnuMCbvCXCpUoBK0e5VgYBI+SnljttBn5BAaQOyYhuufkP7nBSO3z8vY6mJSn9uk2om8CfNs3nj9Hqsdffr98DU2Bzy873W3Nxst27dOuiy9Ilf+gTzjhsXlcdkLbk/RX9QUYz3a+9el/60bh28/LLr1nTxxW6l4ooroGaMwVp1NTQ28t4f/4GDnX2DgsGuvhjT66tOSn3LRWploZgpQKlC8EKPXaSUjDFPWWubvR6HX2Q6pgRZOdRgwMCkTGf5JRt+/bzk8vs4XCCSSFj2HO3k6T3H+dL/POe73+uRjimerWAYY+YA/wXMACywylp7Z673Uy455unpN/PqI3zsrTO4+JTq/v0pCu7IEdcBat26gQ5Qb30r/M3fwNVX598BKiVDbcWetp6ipLOV4sz/uE7FE5FAKOVqSTFpN3PJhV8/L9nWRI0WiCQs/cHFSPfjN16mSMWAz1hrnzbG1ANPGWMestY+n+sdBT21ZMMLB/n7XzxNfbyXNyb6iB/r5f++8gqRyxfkvnv2SDo64OGHXQrUpk2DO0Bdf70r3B6r6mpXq1FX59rNpglyOluQxy4i44dfJ1siQZZPqlO2NVGjBSJBra3yLMCw1u4H9if/326MeQE4Fcg5wAiaDTta+Y9HX+Jw61HmVxt62to5JR7v39wuEgkDcVZv3Tv2AKOvDx57zAUV69dDby+ceir82Z/Bdde5AGOssuwEFeSWqUEeu4iUh6DvcSESRPmmHmZbEzVaABHU2ipfFHkbY+YB5wJbMvxsJbASYO7cuaUdWKH19PD4M7v5zppnqLExTqsI09kdZ9+xHmY2TIC03bMrK8IcbMszPSqRgCefdOlPDzwAbW0waRL88R+7lYpzzz1pdSEvydoK6uuzur8gp7MFeezlTJtsynjh1/oKBT1S7vJt/5xtAfpoAYTfC9mH43mAYYypA34B/KW19sTQn1trVwGrwBXkFfrxizpBydD16RcPP0uNjfWvVlRFwkTCcKSjj7qqgRz/3licGY3VuT3WCy8MdIA6eNAVZ195pQsqLrxwTB2g+oVCA6sVlZU53zwo6WzDfS6CMPbxohSdvUT8wo97XPg16AkKBWfBkG+KUrY1UaMFEEGtrfI0wDDGRHDBxY+stb8s9eMXZYKSSAzu+pQY/KE80NZNfdXgif6U2gkcONFLTzROZUWY3phLv1nRPGf0x9uzZ6AD1K5dLoi45BL4/Ofh8svdKkMh5LhaEWSauAZDIXZxFwkKP+Zh+zHoCYqgBWfjORgaS4pSNjVR2QQQQayt8rKLlAHuBl6w1n7DizEUbILS1+eCia4u92+EVrIzG6s52tnbv4IBUBEOMW9yDQ01EzjY1s2MxmpWNM8Zvv7i0KGBDlC//7277Pzz4YMfhKuuculQhRAOu4Bi4sS8dtkOKk1cg0GdvSTFGPN14AagD9gFfNhae9zTQRVYqfKwc5lI+jHoGatSTaSDFJwFLRhKKdR7WYoUpSAGEKPxcgXjIuADwHPGmG3Jy75krb2vVAMY0wSlq2tgpSIazfoxVzTP4c71O4HBqxV/cfkbRi7o7uiAhx4a6ACVSMDChfDZz7pi7Vmzsh7DqMbRakUmmrgGgzp7SZqHgC9aa2PGmK8BXwQ+7/GYCqoUk5xME8lvv/dcTp9SR2v7yZO0oBafDqeUE+liBmeFDpKCFgztPtLJkc5eXj/ew+d/8eyY38ugpih5zcsuUo8Dnr47OU1QUvUU7e0usIjH83rMxfOncBuweuve0Vcr+vpg40ZYswY2bHAdoGbPhpUr3c7ab3hDXmPIKFVbMc5WKzLRxDUY1NlLUqy1D6Z9uxn4Y6/GUiylmOQMnUhOqpnAzoMdfOLHz2ScpAW1+HQ4pZxIFys4K0aQFJSVqvTnfvPFTdz9eEvB3styXGEoNs+LvL006gRllHqKfC2eP2X41Yp4HJ54YqADVHs7TJkC7363W6k455zCripUVbmgIkCrFcXuHKSJazCos5cM4yPAT7weRDEUe5IzdCL5R+fN5s5Hdg47SSu3M7ulnEgXKzgrRpAUlJWq9OduDIEIisrZuA4wMk1QPnbhXC6dVQmvvTZqPUXBWAvbt7ug4t57obXVdYC66ioXVFx4IVQU8K0yxgUUkybl1QnKS6UowNbENTjU2Wv8MMY8DMzM8KMvW2t/lbzOl3GbuP5ohPspn9bnBTZ0IpnNJK2czuyWciJdrOCsGEFSUFaqhj73IARF5WxcBxiQnKCcVu9WKDo7obcDDnaU5sF373ZBxdq17v+RCCxd6trKXnZZ4TpApUQirraisdEVcOfID3sOlKoAWxNXEX+x1l450s+NMR8CrgeusHb4M0PFbn0eZEMnkmEzviZpxZ5IZ6qNKHRwVowgKSgrVenP/RdP7ePWyxfwrfU7fR0UlbPxGWDE4wNF2mOop8hLa6vrALV2LTz3nDtFdP758NGPuhWLxsbCP2ZtrUuDqs3/F8svrVtVgC0iQxljlgOfAy611uqPQZ6GTiRnNlRxxswG35+5LpRiTqRLVUCeb5A0WmF4EFaq0p/7/rYefrJ1D6s+0EwkbMZda10/MCOc6PGd5uZmu3Xr1vxu3NMzEFR057lDdr7a2+HBB91qxebNrpbjzW92hdrXXgszM636j1Eo5IKViRMLssHeTas2n1T43NUXY3p9FfesXDLm+w/aOESCyBjzlLW22etxFJox5mWgEjiSvGiztfZjo91uTMeUcSI18fTzmevR+GEPh5ZDHVz7rcdOWlm4rwgF5Lm+Z0FtQ5tJOXxeg2SkY0qgVjB2HGjnplWbs0vNiUYH9qXo6irtKgW4jk+/+Y1bqdiwwXWEmjsXPvYxlwI1f35xHreqaqDFbChUsLv1y8qBCrBFZChrbQFb6km6IJy5HkksluDeP+wvSLvSsShlAXmu71mQ2tCOJuif13ISqACjImSGT81JJAaCic7OnPamKJh4HLZscUHFgw+67lNTp8KKFS6oOPvs4nRqCoddi9nGxqK1mPVL61YVYIuISDYSCcvvWo70Bxfg3eTZz52YgtKGVoIlUAEGDCnqndcwsIN2qdOeUqx1tRTr1sF997ldtmtr4eqrXQeoJUsK2wEqXW2tCyzq6oreYtZPKwfFKsD2QxG7iIgUxu4jnWx99agvJs9+7sTk5+BHgitwAUZlbzeNPV1ED+6DPZO9G0hLi2spu27dQAeoZcvcSsWyZS5VqRgqKgY6QRUrcMmg3FcO/FLELiIihXHwRA8J649OWH7uxDRa8OOHGhYJnkAFGJFYH1PaDtETjTOt3oP9Gw4edKsUa9e6fSuMgQsucB2grr7arSYUgzFutaKxcUydoMaqnFu3lqr9rYiIlMaMhirW/v61k9qVfu1dZzNvSm3JJ85+rQ8YKfgppwJwKa1ABRhY6Emm5qxonlOaxzxxwu2ovW6dq6+wFhYtgi9+Ea65BmbMKN5jp/ataGgo6WpFqfkhNckvRewiIlIY86bU8vnlb+Jrv36Bmy9uIhyC5tMmc2HTFABNnNMMF/yUUwG4lFagZq0Ja5lcW8mK5jksnj+leA/U0+M6P61b575Go3DaafDxj7sUqNNPL95jG+NqKhob3W7eZe5bD7/EdzbsIpZIUBkOEU8k+lOTgJIFHn4pYhcRGY1SVrLTf2Z+Zv1JZ+ZbDnVo4pwFFYBLvgIVYJw+tZZvvuec4tx5LOb2qFi3znWA6uyEadPgfe9zxdpnnVXcQuqKCrdnRZ67bAfRhh2tfGfDLhLWEgmHiFs40hFlSh189f4X6IomSlYT4acidhGR4ShlJTfDnZnXxDk7KgCXfAUqwCg4a+HZZ11Nxf33w+HDbvVg+XK3Cd4FFxR/sl9T4wKLuvH3B+2ujS3EE5aKkMFgMAYSWNq6ohztjDJ7UnXJaiLKvYhdRMqDUlYKQxPn7Pi5+5X42/gMMHbtcisV69bBnj1u74jLLnMrFcuWQWWRC8hDIVdXMXFi0fatCIK9x7qorAgRi9v+xSFjoDeeoCIUojoyOLgrdk1EORexi0h50Jn3wtDEOTt+7n4l/jZ+AowDBwY6QD3/vJvkL1kCf/7n8Pa3u52vc7Rl1xFWb93LgbZuZjZWj14bUlnpgooC77IdVHMm1RCLJzjS2QcJF1zEraUiFKJpai3d0bhqIkRE0ujMe2Fo4pw9v3a/En8r7wDj+HFXT7F2LTz5pEuJOvNM1wHq2mthev5nq7fsOsKd63cSCRvqqyIc7ezlzvU7uQ0GBxmpou2JE6G6eqzPqKyk6h6m1E6gvSdGbyxBOGT4+LL5nD17omoiRESG0Jn3wtHEWaR4yi/A6OmB9etd+tPGja4D1Omnwyc+4TpAzZtXkIdZvXUvkbChKpnG477GWb11rwswPNoQL0hGq3tQTYSIyGA68x4s6vgl41V5zHxjMdi0aaADVFeXW514//tdULFoUcE7QB1o66a+avC+CZUVYfZ0xWHWLLdqUcyuUxn4YT+JXI1U96CaCBGRk+nMezCo45eMZ54GGMaY5cCdQBj4rrX2q1nf2Fr4/e9d+tN998HRo6624dprXVBR5A5QMxurOdrZS1UkjMXQXVXDoXA1kyY35FXPMVYbdrRy+5rtJWvrKiIiIsNTxy8ZzzwLMIwxYeA7wNuBfcCTxpg11trnR7zhyy+7oOLee2HvXlc4fdllLqi49NIxdWXKpWh7RfMc/vk3uzk+oQ5bX09nHKJxyxc8qhG4a2MLkbApWVtXERERGZ46fsl45uUKxgXAy9baFgBjzGrgHcDwAcauXa6VbCgEb3ub21n77W8vyB4SWRdtA9TVsfjS2fzF6fN9UyOw91gXE6sHp2wVu62riIiIZKaOXzKeeRlgnArsTft+H7B46JWMMSuBlQBnRyLwV38F11wDU6cWdDCjFm2Hw65ge+LE/qLtZQtrMgYUXtRCzJlUQ2t7j9q6ioiI+IA6fsl45vsib2vtKmAVQPOZZ1o+8IGiPM6IRdszZ7q6iiyKtr2qhUi1fFVbVxEREe+p45eMZ14GGK8Bc9K+n528zBMjFm03NGR9P8WshRhpZWS0lq8iIiJSWur4JeOVlwHGk8ACY8zpuMBiBfBerwazonkO39jQQtuEOhL1DXkXbReqFmJoMPG2psn8/OnXRlwZUVtXEREREfGaZwGGtTZmjPkE8ACuTe33rLXbPRlMbS2LLzmFPy9A0XYhaiEypVl9Z8MuJtdGaKx2xWHqEhXMfT9EREREyp2nNRjW2vuA+zx58Aw7bS9bWDfmCWohaiEypVnFE5a2rihT6wa6T4znLlHa90NEJHf57iytHalFJBe+L/IuuNpaF1QUoLVtJoWohciUZlVZEaInFh902XjuEqV9P0REcpPvztLakVpEcjU+AoxUi9nGRohERr/+GI21FiJTmlV9VQWxLjvuukQNlwalfT9ERHKT787S2pFaRHIV8noARVVTA7NmQVOT2zejBMFFIdyytIlo3AUT1rqvEyrCfHzZfKbXV9HWHWV6fRV33LiorM/Wp9KgWtt7BqVBbdjRypxJNXRHtaIjIpKtkXaWLsbtRGT8Kr8VjIoK11a2sZENu45x133bA1cEPFKa1a1eD66ERkqD0r4fIiK5yXdnae1ILSK5Kp8AI1VbUVsLxhS8CLjUHYvUcnbklr/a90NEJDf57ixdrjtSj1S4rqJ2kbEJdoARDrvViokTT0p/KmQRsDoWeWO0lr8KwkREspfvztLluCP1SIXrgIraRcYomDUYlZUwY4arrZg2LWNtxd5jXVRHwoMuy7cIOD1YMcZ9jYQNd21syfspyOgy1aIoDUpEhmOM+Ywxxhpjpno9Fr9K7Sy9pGkqTdPqsp4w53s7vxqucH33kc4RfyYi2QlWgBEOw5w5cNppLh3KDP8HrpBFwIUMViR7yxZO544bF42rwnYRyY8xZg5wFbDH67GI/41UuK6idpGxC1aKVCQC1dVZXbWQRcCF2J1b8qM0KBHJ0jeBzwG/8nog4n+jFa6rqF1kbIK1gpGDQp79VqqOiIh/GWPeAbxmrf19FtddaYzZaozZeujQoRKMTvwoVbheFXHToPTC9ZF+JiLZMdZar8eQtebmZrt161ZPHjvVRUodi0QkqIwxT1lrm70eRz6MMQ8DMzP86MvAl4CrrLVtxpjdQLO19vBo9+nlMUW8l+oUlalwfaSfiYgz0jElWClSHlKqjoiId6y1V2a63BhzFnA68Hvj6vJmA08bYy6w1h4o4RAlYFKF65l2Ix/pZyIyOgUYIiISWNba54D+sz+5rGCIiEhxlG0NhoiIiIiIlJ5WMEREpGxYa+d5PQYRkfFOKxgiIiIiIlIwWsHIQ6qj1N5jXcxRRykRERERkX5awcjRhh2t3L5mO63tPUysjtDa3sPta7azYUer10MTEREREfGcVjBydNfGFiJh07+rd82ECrr6Yty1saVkqxhaQRERERERv/JkBcMY83VjzA5jzLPGmP8xxkz0Yhz52Husi+pIeNBl1ZEw+451leTxtYIiIiIiIn7mVYrUQ8CZ1tqzgZeAL3o0jpzNmVRDdzQ+6LLuaJzZk2pK8vjpKyjGuK+RsOGujS0leXwRERERkZF4EmBYax+01saS327G7bwaCLcsbSIat3T1xbDWfY3GLbcsbSrJ43u9giIiIiISNImEpeVQB5t2HablUAeJhPV6SGXNDzUYHwF+MtwPjTErgZUAc+fOLdWYhrVs4XTuwK0k7DvWxewS10DMmVRDa3tPfw0IlHYFRURERCRIEgnLr7cf4NM/3UZPNEFVJMQ33n0OyxfNJBQyXg+vLBUtwDDGPAzMzPCjL1trf5W8zpeBGPCj4e7HWrsKWAXQ3Nzsi3Bz2cLpYwooxlKkfcvSJm5fs52uvhjVkTDd0XhJV1BEREREgmT3kc7+4AKgJ5rg0z/dxsJbL6FpWp3HoytPRQswrLVXjvRzY8yHgOuBK6y1vggcSiFVpB0Jm0FF2ndAVkGG1ysoIiIiIkFy8ERPf3CR0hNN0NreowCjSDxJkTLGLAc+B1xqrR1XxQOFaHM71hUUERERkfFiRkMVVZHQoCCjKhJien2Vh6Mqb151kfo2UA88ZIzZZoz5d4/GUXIq0hYREREpnXlTavnGu8+hKuKmvakajHlTaj0eWfnyZAXDWvsGLx7XD1SkLSIiIlI6oZBh+aKZLLz1Elrbe5heX8W8KbUq8C4ir1Ywxi2v29yKiIiIjDehkKFpWh1LmqbSNK1OwUWRKcAosWULp3PHjYuYXl9FW3eU6fVV3HHjItVUiIiIiEhZ8MM+GONO0Iu0x9JmV0REJBuJhGX3kU4OnuhhRoNSWkSCRAGG5GSsbXZFRERGo43RRIJNKVKSk/Q2u8a4r5Gw4a6NLV4PTUREysRwG6PtPtLp8chEJBsKMCQnarMrIiLFNtLGaCLifwowJCdzJtXQHY0PukxtdkVEpJBSG6Ol08ZoIsGhAENyoja7IiJSbNoYTSTYVOQtOVm2cDp34Gox9h3rYra6SImISIFpYzSRYFOAITkLeptdERHxv9TGaE3T6rweiojkSClSIiIiIiJSMAowRERERESkYBRgiIiIiIhIwSjAEBERERGRglGAISIiIiIiBWOstV6PIWvGmEPAq16PI4OpwGGvB5EljbU4NNbi0FgL6zRr7TSvB+EXORxT/Pze+nls4O/xaWz50djyU45jG/aYEqgAw6+MMVuttc1ejyMbGmtxaKzFobGKH/j5vfXz2MDf49PY8qOx5We8jU0pUiIiIiIiUjAKMEREREREpGAUYBTGKq8HkAONtTg01uLQWMUP/Pze+nls4O/xaWz50djyM67GphoMEREREREpGK1giIiIiIhIwSjAGIExZrkx5kVjzMvGmC9k+PmnjTHPG2OeNcY8Yow5Le1ncWPMtuS/NT4Y64eMMYfSxvTRtJ990BizM/nvgz4Y6zfTxvmSMeZ42s9K/bp+zxjTaoz5wzA/N8aYbyWfy7PGmPPSflbq13W0sb4vOcbnjDG/M8a8Je1nu5OXbzPGbPXBWJcZY9rS3uvb03424ufHg7F+Nm2cf0h+Ricnf1bS11Vyl8Xfo0pjzE+SP99ijJmX9rMvJi9/0RhztQdj8+wY5Odjjp+PMX4+pvj5GOLnY4afjxHGmDnGmEeTfye2G2Nuy3Cd4nzmrLX6l+EfEAZ2AU3ABOD3wJuHXOcyoCb5/z8HfpL2sw6fjfVDwLcz3HYy0JL8Oin5/0lejnXI9T8JfM+L1zX5eEuB84A/DPPza4H7AQMsAbZ48bpmOdYLU2MArkmNNfn9bmCqj17XZcC6sX5+SjHWIde9AVjv1euqfzm/t9n87fwL4N+T/1+R+jsPvDl5/Urg9OT9hEs8Nk+OQX4+5vj9GOPnY4qfjyF+Pmb4+RgBzALOS/6/Hngpw+9qUT5zWsEY3gXAy9baFmttH7AaeEf6Fay1j1pru5LfbgZml3iMKaOOdQRXAw9Za49aa48BDwHLizROyH2sNwH3FHE8I7LWbgSOjnCVdwD/ZZ3NwERjzCxK/7qOOlZr7e+SYwFvP6/ZvK7DGctnPS85jtXTz6vkLJvP0zuAHyT//3PgCmOMSV6+2lrba619BXg5eX8lG5uHxyA/H3N8fYzx8zHFz8cQPx8z/HyMsNbut9Y+nfx/O/ACcOqQqxXlM6cAY3inAnvTvt/HyW9KuptxEWBKlTFmqzFmszHm/xRhfOmyHeu7kstfPzfGzMnxtoWS9eMll/tPB9anXVzK1zUbwz2fUr+uuRr6ebXAg8aYp4wxKz0a01BvM8b83hhzvzFmUfIy376uxpga3B/fX6Rd7MfXVQZk83nqv461Nga0AVOyvG2xx5aulMcgPx9zgn6MCcoxxY/HEF8fM7w+RhiX3nkusGXIj4rymavIa5QyiDHm/UAzcGnaxadZa18zxjQB640xz1lrd3kzQgDWAvdYa3uNMbfgzshd7uF4srEC+Lm1Np52md9e18AxxlyGOzhcnHbxxcnXdTrwkDFmR/KsjFeexr3XHcaYa4H/BRZ4OJ5s3AD81lqbfibLb6+rlCGfHoOCcMzRMSYPPj2GBOGY4dkxwhhThwts/tJae6LQ95+JVjCG9xowJ+372cnLBjHGXAl8GbjRWtubutxa+1ryawuwARc1ejZWa+2RtPF9F3hrtrctsFwebwVDlhJL/LpmY7jnU+rXNSvGmLNx7/87rLVHUpenva6twP9Q2FSPnFlrT1hrO5L/vw+IGGOm4tPXNWmkz6svXlc5STafp/7rGGMqgEbgSJa3LfbYvDoG+fmYE/RjjK+PKX49hgTkmOHJMcIYE8EFFz+y1v4yw1WK85mzRSosCfo/3OpOC275NFUYtGjIdc7FFQ8tGHL5JKAy+f+pwE6KWIia5Vhnpf3/ncBmO1DE80pyzJOS/5/s5ViT11uIK34yXr2uaY87j+ELy65jcHHUE168rlmOdS4uT/zCIZfXAvVp//8dsNzjsc5Mvfe4P7h7kq9xVp+fUo41+fNGXA5urdevq/7l9L5m87fz4wwu8v5p8v+LGFzk3UJhi7x9ewzKcmyeHHOy/RuBh8eYUf72eXpMGWVsnh5DRhmbp8eMkcaW/Lknx4jka/BfwL+McJ2ifOYK+gKX2z9cZf1LuD/gX05edgfuTBHAw8BBYFvy35rk5RcCzyU/yM8BN/tgrP8IbE+O6VFgYdptP5L8o/Ey8GGvx5r8/m+Arw65nRev6z3AfiCKyz+8GfgY8LHkzw3wneRzeQ5o9vB1HW2s3wWOpX1etyYvb0q+pr9Pfka+7IOxfiLt87qZtANaps+Pl2NNXudDuILf9NuV/HXVv7ze39H+dlYBP0v+Hj8BNKXd9svJ270IXOPB2Dw7BmUxNs+OOaONLfn93+DBMSaLv32eHVOyGJtnx5AsxubZMWO0sSWv8yE8OEbg0tgs8Gza+3ZtKT5z2slbREREREQKRjUYIiIiIiJSMAowRERERESkYBRgiIiIiIhIwSjAEBERERGRglGAISIiIiIiBaMAQ0RERERECkYBhoiIiIiIFIwCDJExMsacb4x51hhTZYypNcZsN8ac6fW4REQkeHRMkXKgjfZECsAY83e4HX+rgX3W2n/0eEgiIhJQOqZI0CnAECkAY8wE4EmgB7jQWhv3eEgiIhJQOqZI0ClFSqQwpgB1QD3urJOIiEi+dEyRQNMKhkgBGGPWAKuB04FZ1tpPeDwkEREJKB1TJOgqvB6ASNAZY/4UiFprf2yMCQO/M8Zcbq1d7/XYREQkWHRMkXKgFQwRERERESkY1WCIiIiIiEjBKMAQEREREZGCUYAhIiIiIiIFowBDREREREQKRgGGiIiIiIgUjAIMEREREREpGAUYIiIiIiJSMAowRERERESkYP5/qtqz50BkG3sAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 957.6x295.2 with 2 Axes>"
]
@@ -3627,7 +3845,7 @@
},
{
"cell_type": "code",
- "execution_count": 70,
+ "execution_count": 71,
"id": "9a3edfab",
"metadata": {},
"outputs": [
@@ -3701,7 +3919,7 @@
"4 2.486747 0.075106 0.005641"
]
},
- "execution_count": 70,
+ "execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
@@ -3713,7 +3931,7 @@
},
{
"cell_type": "code",
- "execution_count": 71,
+ "execution_count": 72,
"id": "24c100a8",
"metadata": {},
"outputs": [],
@@ -3733,7 +3951,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 73,
"id": "d6ac4ac8",
"metadata": {},
"outputs": [],
@@ -3744,7 +3962,7 @@
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 74,
"id": "078047e2",
"metadata": {},
"outputs": [
@@ -3801,7 +4019,7 @@
},
{
"cell_type": "code",
- "execution_count": 74,
+ "execution_count": 75,
"id": "fba76906",
"metadata": {},
"outputs": [],
@@ -3812,7 +4030,7 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 76,
"id": "424e6080",
"metadata": {},
"outputs": [
@@ -3849,7 +4067,7 @@
},
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": 77,
"id": "74c19b10",
"metadata": {},
"outputs": [
@@ -3930,7 +4148,7 @@
"6 445.311406 "
]
},
- "execution_count": 76,
+ "execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
@@ -3946,8 +4164,8 @@
},
{
"cell_type": "code",
- "execution_count": 105,
- "id": "d52c5826",
+ "execution_count": 78,
+ "id": "572d19c8",
"metadata": {},
"outputs": [],
"source": [
@@ -3980,7 +4198,7 @@
},
{
"cell_type": "markdown",
- "id": "ddca584a",
+ "id": "648b703e",
"metadata": {},
"source": [
"Now if we get a new sample from the same population, and compute the coefficient of determination:"
@@ -3988,8 +4206,8 @@
},
{
"cell_type": "code",
- "execution_count": 112,
- "id": "a7c3be8a",
+ "execution_count": 79,
+ "id": "42c8cdf3",
"metadata": {},
"outputs": [],
"source": [
@@ -3998,13 +4216,13 @@
},
{
"cell_type": "code",
- "execution_count": 113,
- "id": "4b0254fb",
+ "execution_count": 80,
+ "id": "3131de8c",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -4035,7 +4253,7 @@
},
{
"cell_type": "markdown",
- "id": "c49be8f1",
+ "id": "d0a2fd9f",
"metadata": {},
"source": [
"...the over-complex models perform poorly."
@@ -4043,7 +4261,7 @@
},
{
"cell_type": "markdown",
- "id": "34e0a478",
+ "id": "70aafcbf",
"metadata": {},
"source": [
"### Model selection"
@@ -4081,8 +4299,8 @@
},
{
"cell_type": "code",
- "execution_count": 116,
- "id": "d9da7ebb",
+ "execution_count": 81,
+ "id": "43ac886e",
"metadata": {},
"outputs": [
{
@@ -4162,7 +4380,7 @@
"6 445.311406 "
]
},
- "execution_count": 116,
+ "execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
@@ -4173,7 +4391,7 @@
},
{
"cell_type": "markdown",
- "id": "1f9e1fa4",
+ "id": "86b40681",
"metadata": {},
"source": [
"$$\n",
@@ -4190,13 +4408,15 @@
"\n",
"The likelihood is the probability that the data are generated by the model: $L=P\\left(X,y|\\mathcal{M}(\\theta)\\right)$, denoting $\\mathcal{M}(\\theta)$ the model with parameters $\\theta$ (estimated coefficients).\n",
"\n",
- "In the case of a linear regression with normally-distributed residuals: $\\log{L}\\propto \\frac{\\sum_i(y_i - \\textbf{x}_i^\\top\\beta)^2}{\\sigma^2}$ with $\\beta$ are the regression coefficients."
+ "In the case of a linear regression with normally-distributed residuals: $\\log{L}\\propto -\\frac{\\sum_i(y_i - \\textbf{x}_i^\\top\\beta)^2}{2\\sigma^2}$ with $\\beta$ are the regression coefficients.\n",
+ "\n",
+ "`OLS` models rely on such a form for the likelihood and that is the reason why we must ensure the residuals are normally distributed."
]
},
{
"cell_type": "code",
- "execution_count": 127,
- "id": "29b6d783",
+ "execution_count": 82,
+ "id": "60322cd4",
"metadata": {},
"outputs": [
{
@@ -4255,16 +4475,97 @@
"## Generalized linear models"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "1d397a38",
+ "metadata": {},
+ "source": [
+ "What if -- instead of a continuous variable -- our response variable is a binary (or categorical) variable?"
+ ]
+ },
{
"cell_type": "markdown",
"id": "337f7ce4-9acf-461a-a9a3-2a78b2af2e01",
"metadata": {},
"source": [
- "What if -- instead of a continuous variable -- our response variable is a binary (or categorical) variable?\n",
- "\n",
"### Link function"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "982d6488",
+ "metadata": {},
+ "source": [
+ "Basically, we can apply a transformation to the response variable:\n",
+ "\n",
+ "$$\n",
+ "g(y) = \\textbf{X}\\beta + \\epsilon\n",
+ "$$\n",
+ "\n",
+ "$g$ is called a *link function*.\n",
+ "\n",
+ "This is similar to the additive models we have seen, with transformations of the predictors, BUT...\n",
+ "\n",
+ "...remember we need the residuals to be normally distributed.\n",
+ "\n",
+ "In addition, we may want the variables (be they response or explanatory) to take meaningful values."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c374bbc5",
+ "metadata": {},
+ "source": [
+ "### Example problems"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66c0391c",
+ "metadata": {},
+ "source": [
+ "#### Count outcome\n",
+ "\n",
+ "\\[copying [Wikipedia](https://en.wikipedia.org/wiki/Generalized_linear_model)\\]\n",
+ "Suppose we want to predict how many people will come to some type of outdoor places (*e.g.* beaches) as a function of temperature.\n",
+ "\n",
+ "If we observed the attendance in multiple occasions, mostly at temperatures in the 15-35°C range, a fitted linear model could predict impossible values, namely negative attendance, at low temperatures, say 5°C.\n",
+ "\n",
+ "A link function could be used to turn the attendance into a variation rate of attendance, so that the attendance can be multiplied or divided as a function of temperature increase/decrease, and never subtracted.\n",
+ "\n",
+ "To do so, we need an *exponential response* model with $g=\\log$. If $g(y)$ varies linearly with temperature, $y$ will be Poisson distributed."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0fe5847",
+ "metadata": {},
+ "source": [
+ "#### Odds ratio"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb2523f9",
+ "metadata": {},
+ "source": [
+ "Suppose now we want to model the probability of a two-option process, *e.g.* «survives *vs* dies».\n",
+ "\n",
+ "A response variable is always quantitative. If we choose it to be the raw probability, we may find situations such that the predicted probability takes a negative value, or falls above $1$.\n",
+ "\n",
+ "If we think of the effect of a change in the value of an explanatory variable, this effect better applies to the *odds* of survival, *i.e.* the ratio of survival over death, and again we want the model to make this value vary in a multiplicative way (no subtraction, no addition).\n",
+ "\n",
+ "This is a typical application of a *logit* link function, which relies on $y$ following a binomial distribution."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7c272448",
+ "metadata": {},
+ "source": [
+ "## Families"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -4324,7 +4625,12 @@
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
- "toc_position": {},
+ "toc_position": {
+ "height": "calc(100% - 180px)",
+ "left": "10px",
+ "top": "150px",
+ "width": "384px"
+ },
"toc_section_display": true,
"toc_window_display": true
}