scipy_TP_solutions.ipynb 263 KB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5a5210d",
   "metadata": {},
   "source": [
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    "## Q\n",
    "\n",
    "Import `numpy`, `pandas`, the `pyplot` module from `matplotlib`, `seaborn`, and the `stats` module from `scipy`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac6cc32",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "529c5f56",
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   "metadata": {
    "hidden": true
   },
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   "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",
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   "id": "93ad4aaf",
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   "metadata": {},
   "source": [
    "# Comparison of two group means"
   ]
  },
  {
   "cell_type": "markdown",
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   "id": "0e4fd0d9",
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   "metadata": {},
   "source": [
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    "## Q\n",
    "\n",
    "Load the `mi.csv` data file located in the `data` directory of the course repository."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c1dd12",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "00130518",
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   "metadata": {
    "hidden": true
   },
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   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "      <td>Yes</td>\n",
       "      <td>94.9627</td>\n",
       "      <td>20.13</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>37.4</td>\n",
       "      <td>9.350</td>\n",
       "      <td>40</td>\n",
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       "      <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",
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       "      <td>No</td>\n",
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       "      <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",
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       "      <td>No</td>\n",
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       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>36.0</td>\n",
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       "      <td>40</td>\n",
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       "    <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",
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       "      <td>36.7</td>\n",
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       "     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]"
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     "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": [
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    "## Q\n",
    "\n",
    "Anything missing?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d5dc74",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a648a9b",
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   "metadata": {
    "hidden": true
   },
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   "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",
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   "metadata": {
    "hidden": true
   },
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>OwnsHouse</th>\n",
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       "      <th>Sex</th>\n",
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       "      <th>MetabolicScore</th>\n",
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       "      <th>TroubleConcentrating</th>\n",
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       "      <th>RecentPersonalCrisis</th>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <td>No</td>\n",
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       "      <td>PhD</td>\n",
       "      <td>No</td>\n",
       "      <td>(1000-2000]</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.83</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
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       "      <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": [
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    "## Q\n",
    "\n",
    "Show a summary table for these data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6984434b",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a7a7d087",
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   "metadata": {
    "hidden": true
   },
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   "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",
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   "id": "04163591",
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   "metadata": {},
   "source": [
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    "## Q\n",
    "\n",
    "Inspect the distribution of variables `Age` and `OwnsHouse`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6baac23",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "id": "5de6412d",
   "metadata": {
    "hidden": true
   },
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   "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,
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   "id": "f793503f",
   "metadata": {
    "hidden": true
   },
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   "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",
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   "id": "1e94c17b",
   "metadata": {
    "heading_collapsed": true
   },
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   "source": [
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    "## Q\n",
    "\n",
    "Isolate the house-owners group from the others, draw their respective age distributions and check they are normally distributed."
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "id": "55d18f16",
   "metadata": {
    "hidden": true
   },
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   "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,
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   "id": "3d1a44e6",
   "metadata": {
    "hidden": true
   },
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   "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,
1124
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   "id": "ddf5d4b0",
   "metadata": {
    "hidden": true
   },
François  LAURENT's avatar
François LAURENT committed
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   "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",
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   "id": "24b49c4c",
   "metadata": {
    "hidden": true
   },
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   "source": [
    "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,
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   "id": "0f888c53",
   "metadata": {
    "hidden": true
   },
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   "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",
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   "id": "f35584b7",
   "metadata": {
    "hidden": true
   },
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   "source": [
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    "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). The sampling procedure clearly excluded people younger than 20 years old or elder than 70, which results in truncated distributions.\n",
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    "\n",
    "Here, we have comfortable sample sizes and these departures from normality may not affect the power of the statistical test."
   ]
  },
  {
   "cell_type": "markdown",
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   "id": "2cc80be1",
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   "metadata": {},
   "source": [
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    "## Q\n",
    "\n",
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    "Are the sample size and variance of the two groups similar enough for running a standard $t$ test?"
   ]
  },
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  {
   "cell_type": "markdown",
   "id": "cd58c73a",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": 12,
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   "id": "0dbb79f7",
   "metadata": {
    "hidden": true
   },
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   "outputs": [
    {
     "data": {
      "text/plain": [
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       "(288, 528, 14.830622606395378, 11.782179959362857)"
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      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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    "len(house_owners_age), len(others_age), np.std(house_owners_age), np.std(others_age)"
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   ]
  },
  {
   "cell_type": "markdown",
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   "id": "3e221ea2",
   "metadata": {
    "hidden": true
   },
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   "source": [
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    "`ttest_ind` allows standard deviation ratios [up to $2$](https://en.wikipedia.org/wiki/Student%27s_t-test#Equal_or_unequal_sample_sizes,_similar_variances_(1/2_%3C_sX1/sX2_%3C_2)). The groups can have different sample sizes."
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   ]
  },
  {
   "cell_type": "markdown",
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   "id": "d61f454a",
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   "metadata": {},
   "source": [
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    "## Q\n",
    "\n",
    "Test the group mean ages equal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b076e8e6",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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   "id": "1d238900",
   "metadata": {
    "hidden": true
   },
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   "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",
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   "id": "62b30b76",
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   "metadata": {},
   "source": [
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    "## Q\n",
    "\n",
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    "How would you report the result of this test?"
   ]
  },
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  {
   "cell_type": "markdown",
   "id": "efeac3ab",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 54,
   "id": "341157b6",
   "metadata": {
    "hidden": true
   },
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   "outputs": [
    {
     "data": {
      "text/plain": [
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       "(814, -10.305953282828284, -0.7954424784394866, -0.7954424784394866)"
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      ]
     },
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     "execution_count": 54,
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     "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",
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    "# * the mean difference (this is almost an effect size, not compared with the associated variability),\n",
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    "mean_difference = np.mean(house_owners_age) - np.mean(others_age)\n",
    "\n",
    "# * and the effect size.\n",
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    "t, _ = stats.ttest_ind(house_owners_age, others_age)\n",
    "cohen_d = t * np.sqrt(1/n1 + 1/n2)\n",
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    "\n",
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    "#   alternatively:\n",
    "import pingouin as pg\n",
    "unbiased_cohen_d = pg.compute_effsize(house_owners_age, others_age)\n",
    "\n",
    "degrees_of_freedom, mean_difference, cohen_d, unbiased_cohen_d"
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   ]
  },
  {
   "cell_type": "markdown",
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   "id": "b79f8a5c",
   "metadata": {
    "hidden": true
   },
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   "source": [
    "«**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",
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   "id": "f72698b7",
   "metadata": {},
   "source": [
    "## Q\n",
    "\n",
    "\\[optional; good for playing with Python rather than statistical methods\\]\n",
    "\n",
    "Although tractable in principle, the group difference in variance is quite large and -- had we smaller samples -- we could instead use the Welch's $t$ test that is known to better control for type-1 errors in cases of differing variances, but also a slightly lower power.\n",
    "\n",
    "As it is now clear we have a relationship between age and owning a house, let us compute the rejection rate (or power) as a function of sample size.\n",
    "\n",
    "Proposal:\n",
    "* loop over decreasing sample sizes (*e.g.* 200, 50, 20, 10, 5),\n",
    "* randomly pick a subsample of that size from each group,\n",
    "* compare their means using the standard Student $t$-test and Welch $t$-test,\n",
    "* observe whether each test successfully rejects $H_0$ for a constant significance level (*e.g.* 5%),\n",
    "* replicate this procedure many times (*e.g.* 100)\n",
    "* and compute the rejection rate for each sample size and type of test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a2bc253",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Help: subsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fd050fbc",
   "metadata": {
    "hidden": true
   },
   "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": [
    "# let us consider an example sample\n",
    "sample = others_age\n",
    "\n",
    "# and a subsample size\n",
    "n = 200\n",
    "\n",
    "# we need a random generator\n",
    "rng = np.random.default_rng()\n",
    "\n",
    "# now we can pick n observations from the original sample\n",
    "# calling the `choice` method of the random generator\n",
    "subsample = rng.choice(sample, n)\n",
    "\n",
    "# in principle the smaller sample will exhibit similar\n",
    "# properties as the original sample; both are drawn from\n",
    "# the population in similar ways\n",
    "bins = np.arange(20, 70+1, 5)\n",
    "sns.histplot(sample, bins=bins)\n",
    "sns.histplot(subsample, bins=bins);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44a7b2b",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35541f89",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "significance_level = 0.05\n",
    "\n",
    "sample1 = house_owners_age\n",
    "sample2 = others_age\n",
    "sample_size = min(len(sample1), len(sample2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2ae175e5",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "sample_sizes = []\n",
    "test_types = []\n",
    "rejection_rates = []\n",
    "\n",
    "rng = np.random.default_rng()\n",
    "\n",
    "for relative_sample_size in (1, .2, .1, .05, .025):\n",
    "    n = int(relative_sample_size * sample_size)\n",
    "    nreplicates = 100\n",
    "    rejections = defaultdict(lambda: 0)\n",
    "    for _ in range(nreplicates):\n",
    "        subsample1 = rng.choice(sample1, n)\n",
    "        subsample2 = rng.choice(sample2, n)\n",
    "        for test_type in ('Student', 'Welch'):\n",
    "            t, pv = stats.ttest_ind(subsample1, subsample2, equal_var=test_type=='Student')\n",
    "            if pv <= significance_level:\n",
    "                rejections[test_type] = rejections[test_type] + 1\n",
    "    for test_type in rejections:\n",
    "        rejection_rates.append(rejections[test_type] / nreplicates)\n",
    "        sample_sizes.append(n)\n",
    "        test_types.append(test_type)\n",
    "            \n",
    "result = pd.DataFrame({'sample size': sample_sizes, 'test': test_types, 'power': rejection_rates})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d9641fa6",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample size</th>\n",
       "      <th>test</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>288</td>\n",
       "      <td>Student</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>288</td>\n",
       "      <td>Welch</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57</td>\n",
       "      <td>Student</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "      <td>Welch</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Student</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28</td>\n",
       "      <td>Welch</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>Student</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>Welch</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>Student</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>Welch</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample size     test  power\n",
       "0          288  Student   1.00\n",
       "1          288    Welch   1.00\n",
       "2           57  Student   0.97\n",
       "3           57    Welch   0.97\n",
       "4           28  Student   0.80\n",
       "5           28    Welch   0.80\n",
       "6           14  Student   0.55\n",
       "7           14    Welch   0.55\n",
       "8            7  Student   0.25\n",
       "9            7    Welch   0.23"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ffbdab",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Both tests give similar results and quickly loose quite a lot of power as the sample size decreases.\n",
    "$0.8$ is often considered as a reasonnable (some would say «minimal») power for a(ny) test.\n",
    "\n",
    "Here, the quick decrease in power is likely to be intensified by the asymmetries in opposite directions, known to be deleterous for the $t$-tests."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d98432",
   "metadata": {},
   "source": [
    "# Comparing two distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f5453a9",
   "metadata": {},
   "source": [
    "Now let proceed to comparing age between people living with kids and those living without kids.\n",
    "Plot the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0aeaeee7",
   "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.boxplot(x='LivesWithKids', y='Age', data=df)\n",
    "sns.swarmplot(x='LivesWithKids', y='Age', data=df, linewidth=1, size=3);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02cbfb3c",
   "metadata": {},
   "source": [
    "## Q\n",
    "\n",
    "How do the common descriptive statistics (mean, variance) compare?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2f481d2",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e9c1cc3c",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47.758187772925766, 44.85779329608938, 16.298908849529322, 9.611832029475966)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lives_with_kids = df['Age'][df['LivesWithKids']=='Yes']\n",
    "lives_without_kids = df['Age'][df['LivesWithKids']=='No']\n",
    "np.mean(lives_without_kids), np.mean(lives_with_kids), np.std(lives_without_kids), np.std(lives_with_kids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d7ad6b1",
   "metadata": {
    "hidden": true
   },
   "source": [
    "A difference in group means is very unlikely, and the ratio of the group standard deviations is large but $<2$.\n",
    "\n",
    "The main feature to notice is the double mode in the *lives without kids* group.\n",
    "Similar samples drawn from the same population could be (more) biased towards elder or younger people, and this could result in mean differences, in a direction or another, even to the point such differences become significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b6bfdb58",
   "metadata": {
    "hidden": true
   },
   "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(lives_without_kids);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef7a30a",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As a consequence, there is no point in comparing the two groups in terms of central tendency (means). A $t$-test is not suitable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34b67889",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Q\n",
    "\n",
    "How can we compare the two groups to state they differ from one another?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89181560",
   "metadata": {},
   "source": [
    "## A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef4093df",
   "metadata": {},
   "source": [
    "We need a two-sample goodness-of-fit test.\n",
    "\n",
    "This can be done in two ways:\n",
    "\n",
    "* with a $\\chi^2$ test of homogeneity, binning the age;\n",
    "* with a two-sample Kolmogorov-Smirnov test.\n",
    "\n",
    "### Q\n",
    "\n",
    "Bin the two groups, extract frequencies and proceed to performing a $\\chi^2$ test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4338fe92",
   "metadata": {},
   "source": [
    "### A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f57a8ff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([75, 32, 26, 23, 22, 30, 39, 56, 94, 61]),\n",
       " array([ 2, 12, 44, 67, 65, 53, 57, 34, 16,  8]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.arange(20, 70+1, 5)\n",
    "lives_without_kids_freqs, _ = np.histogram(lives_without_kids, bins)\n",
    "lives_with_kids_freqs, _ = np.histogram(lives_with_kids, bins)\n",
    "lives_without_kids_freqs, lives_with_kids_freqs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980a9794",
   "metadata": {},
   "source": [
    "Let us check we did not miss any observation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de19e80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "816"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assert np.sum(lives_without_kids_freqs) + np.sum(lives_with_kids_freqs) == len(df)\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbbc965c",
   "metadata": {},
   "source": [
    "Check there are at least 5 observations per combination of factor levels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "32afe08a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([22, 23, 26]), array([ 2,  8, 12]))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(lives_without_kids_freqs)[:3], np.sort(lives_with_kids_freqs)[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b021e03f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "χ²(9) = 228.0, p-value = 4.33e-44\n"
     ]
    }
   ],
   "source": [
    "chi2, pvalue, dof, _ = stats.chi2_contingency(np.stack((lives_with_kids_freqs, lives_without_kids_freqs), axis=0))\n",
    "print(f'χ²({dof}) = {chi2:.1f}, p-value = {pvalue:.3g}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b829782",
   "metadata": {},
   "source": [
    "### Q\n",
    "\n",
    "Similarly, perform a two-sample Kolmogorov-Smirnov test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e66c8ce",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3c694746",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.31230026103290964, pvalue=1.1102230246251565e-16)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ks_2samp(lives_with_kids, lives_without_kids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "901dca51",
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   "source": [
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    "# Correlations"
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   "cell_type": "code",
   "execution_count": null,
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   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "toc": {
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   "number_sections": false,
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