diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..2bb9022a702f8125eb07dd8da1e68c2f60cfde0e 100644
--- a/module2/exo2/exercice.ipynb
+++ b/module2/exo2/exercice.ipynb
@@ -1,5 +1,123 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
+    "print(len(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "m = np.mean(a)\n",
+    "print(\"{:.2f}\".format(m))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.33\n"
+     ]
+    }
+   ],
+   "source": [
+    "sigma = np.std(a,ddof=1)\n",
+    "print(\"{:.2f}\".format(sigma))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.50\n"
+     ]
+    }
+   ],
+   "source": [
+    "med = np.median(a)\n",
+    "print(\"{:.2f}\".format(med))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.40\n"
+     ]
+    }
+   ],
+   "source": [
+    "maxi = np.max(a)\n",
+    "print(\"{:.2f}\".format(maxi))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.80\n"
+     ]
+    }
+   ],
+   "source": [
+    "mini = np.min(a)\n",
+    "print(\"{:.2f}\".format(mini))"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +134,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module2/exo3/exercice.ipynb b/module2/exo3/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..9f992676cc7faeb5909cdbc1f5ab3b989af89080 100644
--- a/module2/exo3/exercice.ipynb
+++ b/module2/exo3/exercice.ipynb
@@ -1,5 +1,80 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
+    "print(len(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": [
+    "plt.plot(a)\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": [
+    "n, bins, patches = plt.hist(a)\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +91,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb
index 26ad6d94fa840f788a57621b06dc6af83a848391..d6a2935188e1776f7e6ca157928121660095a754 100644
--- a/module2/exo5/exo5_fr.ipynb
+++ b/module2/exo5/exo5_fr.ipynb
@@ -40,263 +40,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 49,
    "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>Date</th>\n",
-       "      <th>Count</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Pressure</th>\n",
-       "      <th>Malfunction</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4/12/81</td>\n",
-       "      <td>6</td>\n",
-       "      <td>66</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>11/12/81</td>\n",
-       "      <td>6</td>\n",
-       "      <td>70</td>\n",
-       "      <td>50</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3/22/82</td>\n",
-       "      <td>6</td>\n",
-       "      <td>69</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11/11/82</td>\n",
-       "      <td>6</td>\n",
-       "      <td>68</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4/04/83</td>\n",
-       "      <td>6</td>\n",
-       "      <td>67</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6/18/82</td>\n",
-       "      <td>6</td>\n",
-       "      <td>72</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>8/30/83</td>\n",
-       "      <td>6</td>\n",
-       "      <td>73</td>\n",
-       "      <td>100</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>11/28/83</td>\n",
-       "      <td>6</td>\n",
-       "      <td>70</td>\n",
-       "      <td>100</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2/03/84</td>\n",
-       "      <td>6</td>\n",
-       "      <td>57</td>\n",
-       "      <td>200</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>4/06/84</td>\n",
-       "      <td>6</td>\n",
-       "      <td>63</td>\n",
-       "      <td>200</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>8/30/84</td>\n",
-       "      <td>6</td>\n",
-       "      <td>70</td>\n",
-       "      <td>200</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>10/05/84</td>\n",
-       "      <td>6</td>\n",
-       "      <td>78</td>\n",
-       "      <td>200</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>11/08/84</td>\n",
-       "      <td>6</td>\n",
-       "      <td>67</td>\n",
-       "      <td>200</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1/24/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>53</td>\n",
-       "      <td>200</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>4/12/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>67</td>\n",
-       "      <td>200</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>4/29/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>75</td>\n",
-       "      <td>200</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>6/17/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>70</td>\n",
-       "      <td>200</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>7/29/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>81</td>\n",
-       "      <td>200</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>8/27/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>76</td>\n",
-       "      <td>200</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>10/03/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>79</td>\n",
-       "      <td>200</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>10/30/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>75</td>\n",
-       "      <td>200</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>11/26/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>76</td>\n",
-       "      <td>200</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1/12/86</td>\n",
-       "      <td>6</td>\n",
-       "      <td>58</td>\n",
-       "      <td>200</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Date  Count  Temperature  Pressure  Malfunction\n",
-       "0     4/12/81      6           66        50            0\n",
-       "1    11/12/81      6           70        50            1\n",
-       "2     3/22/82      6           69        50            0\n",
-       "3    11/11/82      6           68        50            0\n",
-       "4     4/04/83      6           67        50            0\n",
-       "5     6/18/82      6           72        50            0\n",
-       "6     8/30/83      6           73       100            0\n",
-       "7    11/28/83      6           70       100            0\n",
-       "8     2/03/84      6           57       200            1\n",
-       "9     4/06/84      6           63       200            1\n",
-       "10    8/30/84      6           70       200            1\n",
-       "11   10/05/84      6           78       200            0\n",
-       "12   11/08/84      6           67       200            0\n",
-       "13    1/24/85      6           53       200            2\n",
-       "14    4/12/85      6           67       200            0\n",
-       "15    4/29/85      6           75       200            0\n",
-       "16    6/17/85      6           70       200            0\n",
-       "17  7/29/85      6           81       200            0\n",
-       "18    8/27/85      6           76       200            0\n",
-       "19   10/03/85      6           79       200            0\n",
-       "20   10/30/85      6           75       200            2\n",
-       "21   11/26/85      6           76       200            0\n",
-       "22    1/12/86      6           58       200            1"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Date  Count  Temperature  Pressure  Malfunction\n",
+      "0    4/12/81      6           66        50            0\n",
+      "1   11/12/81      6           70        50            1\n",
+      "2    3/22/82      6           69        50            0\n",
+      "3   11/11/82      6           68        50            0\n",
+      "4    4/04/83      6           67        50            0\n",
+      "5    6/18/82      6           72        50            0\n",
+      "6    8/30/83      6           73       100            0\n",
+      "7   11/28/83      6           70       100            0\n",
+      "8    2/03/84      6           57       200            1\n",
+      "9    4/06/84      6           63       200            1\n",
+      "10   8/30/84      6           70       200            1\n",
+      "11  10/05/84      6           78       200            0\n",
+      "12  11/08/84      6           67       200            0\n",
+      "13   1/24/85      6           53       200            2\n",
+      "14   4/12/85      6           67       200            0\n",
+      "15   4/29/85      6           75       200            0\n",
+      "16   6/17/85      6           70       200            0\n",
+      "17   7/29/85      6           81       200            0\n",
+      "18   8/27/85      6           76       200            0\n",
+      "19  10/03/85      6           79       200            0\n",
+      "20  10/30/85      6           75       200            2\n",
+      "21  11/26/85      6           76       200            0\n",
+      "22   1/12/86      6           58       200            1\n",
+      "Entre 6 et 6 joints ont été testés,\n",
+      "entre 53°F et 81°F,\n",
+      "à des pressions allant de 50psi à 200psi, \n",
+      "ce qui a permis de déceler 9 dysfonctionnements. \n"
+     ]
     }
    ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
     "data = pd.read_csv(\"shuttle.csv\")\n",
-    "data"
+    "print(data)\n",
+    "\n",
+    "print(\"Entre {} et {} joints ont été testés,\".format(np.min(data['Count']), np.max(data['Count'])))\n",
+    "print(\"entre {}°F et {}°F,\".format(np.min(data['Temperature']), np.max(data['Temperature'])))\n",
+    "print(\"à des pressions allant de {}psi à {}psi, \".format(np.min(data['Pressure']), np.max(data['Pressure'])))\n",
+    "print(\"ce qui a permis de déceler {} dysfonctionnements. \".format(np.sum(data['Malfunction'])))"
    ]
   },
   {
@@ -322,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +216,7 @@
        "22   1/12/86      6           58       200            1"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -448,12 +239,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 54,
    "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>"
       ]
@@ -500,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -524,10 +315,10 @@
        "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -2.5250</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>           <td>Sat, 13 Apr 2019</td> <th>  Deviance:          </th> <td> 0.22231</td> \n",
+       "  <th>Date:</th>           <td>Thu, 06 Jan 2022</td> <th>  Deviance:          </th> <td> 0.22231</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>               <td>19:11:24</td>     <th>  Pearson chi2:      </th>  <td> 0.236</td>  \n",
+       "  <th>Time:</th>               <td>17:43:52</td>     <th>  Pearson chi2:      </th>  <td> 0.236</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Iterations:</th>         <td>4</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
@@ -555,8 +346,8 @@
        "Model Family:                Binomial   Df Model:                            1\n",
        "Link Function:                  logit   Scale:                          1.0000\n",
        "Method:                          IRLS   Log-Likelihood:                -2.5250\n",
-       "Date:                Sat, 13 Apr 2019   Deviance:                      0.22231\n",
-       "Time:                        19:11:24   Pearson chi2:                    0.236\n",
+       "Date:                Thu, 06 Jan 2022   Deviance:                      0.22231\n",
+       "Time:                        17:43:52   Pearson chi2:                    0.236\n",
        "No. Iterations:                     4   Covariance Type:             nonrobust\n",
        "===============================================================================\n",
        "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
@@ -567,7 +358,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -605,12 +396,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAGzdJREFUeJzt3X+UVOWd5/H3t6tBGhohoGGAJoHM4cA6UX41jUriNkYBc+KvWQ2io4k7LHEnJJPdIxs5J7OaWT1n57S7h0zWiIwyTOLR1nEVNcsG1E3HiauxQRAEhh9riDadBDGj0Noo3f3dP+6t6qrqbrq6qO6qevy8zulD3VvPfe7z7aI+dfupW7fM3RERkbBUFHsAIiJSeAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEA9RvuZrbBzI6a2Rt93G9m9rdmdsjMdpnZ3MIPU0REBiKXI/eNwNLT3H8FMD3+WQncf+bDEhGRM9FvuLv7i8AfTtPkauDHHnkFGGtmEws1QBERGbjKAvQxGXg7bbklXvfb7IZmtpLo6J6qqqp5U6ZMyWuHXV1dVFSE8XaBailNodQSSh2gWpIOHDhwzN3P7a9dIcLdelnX6zUN3H09sB6gtrbWt23bltcOm5qaqK+vz2vbUqNaSlMotYRSB6iWJDP7TS7tCvEy2AKkH4LXAK0F6FdERPJUiHB/BrglPmvmQuB9d+8xJSMiIkOn32kZM3sUqAfOMbMW4E5gGIC7rwM2A18GDgEfArcO1mBFRCQ3/Ya7uy/v534HvlmwEYlIWTh16hQtLS2cPHlySPY3ZswY9u3bNyT7Gmy51DJixAhqamoYNmxYXvsoxBuqIvIJ1NLSwujRo5k6dSpmvZ1XUVgnTpxg9OjRg76fodBfLe7Ou+++S0tLC9OmTctrH2GcVyQiQ+7kyZOMHz9+SIL9k8bMGD9+/Bn9VaRwF5G8KdgHz5n+bhXuIiIB0py7iJStRCLB+eefn1retGkTU6dOLd6ASojCXUTKVlVVFTt37uzz/o6ODiorP5kxp2kZEQnKxo0buf7667nyyitZvHgxAA0NDcyfP58LLriAO++8M9X2nnvuYcaMGVx22WUsX76ce++9F4D6+nqSl0c5duxY6q+Bzs5OVq9enerrgQceALovJ3Ddddcxc+ZMbrrpJqKzxKG5uZmLL76YWbNmUVdXx4kTJ1iyZEnGi9LChQvZtWtXQX8Pn8yXNBEpqO8/u4e9rccL2ud5k87mziv/5LRt2tvbmT17NgDTpk3jqaeeAuDll19m165djBs3jq1bt3Lw4EFeffVV3J2rrrqKF198kVGjRtHY2MiOHTvo6Ohg7ty5zJs377T7e+ihhxgzZgzNzc189NFHLFy4MPUCsmPHDvbs2cOkSZNYuHAhL730EnV1dSxbtozHHnuM+fPnc/z4caqqqrjlllvYuHEja9eu5cCBA3z00UdccMEFBfitdVO4i0jZ6mta5vLLL2fcuHEAbN26la1btzJnzhwA2traOHjwICdOnODaa69l5MiRAFx11VX97m/r1q3s2rWLJ554AoD333+fgwcPMnz4cOrq6qipqQFg9uzZHD58mDFjxjBx4kTmz58PwNlnnw3Atddey8KFC2loaGDDhg18/etfP7NfRC8U7iJyxvo7wh5qo0aNSt12d9asWcM3vvGNjDZr167t83TDyspKurq6ADLONXd3fvjDH7JkyZKM9k1NTZx11lmp5UQiQUdHB+7e6z5GjhzJ5ZdfztNPP83jjz9OvlfIPR3NuYtI0JYsWcKGDRtoa2sD4MiRIxw9epRLLrmEp556ivb2dk6cOMGzzz6b2mbq1Kls374dIHWUnuzr/vvv59SpUwAcOHCADz74oM99z5w5k9bWVpqbm4Hok6kdHR0ArFixgm9/+9vMnz8/9VdGIenIXUSCtnjxYvbt28dFF10EQHV1NQ8//DBz585l2bJlzJ49m89+9rN88YtfTG1z++2389WvfpWf/OQnXHrppan1K1as4PDhw8ydOxd359xzz2XTpk197nv48OE89thjfOtb36K9vZ2qqiqef/55AObNm8fZZ5/NrbcO0rUW3b0oP/PmzfN8/fznP89721KjWkpTKLUMZh179+4dtL57c/z48UHt/8477/SGhoZB3UfS8ePH/ciRIz59+nTv7Ozss11vv2Ngm+eQsZqWEREZYo888ggLFizgnnvuGbSvDtS0jIgIcNdddw3Zvm688cYeb/AWmo7cRSRv7r1+XbIUwJn+bhXuIpKXESNG8O677yrgB4HH13MfMWJE3n1oWkZE8lJTU0NLSwvvvPPOkOzv5MmTZxR2pSSXWpLfxJQvhbuI5GXYsGF5f0tQPpqamlKfMi13Q1GLpmVERAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEA5RTuZrbUzPab2SEzu6OX+8eY2bNm9rqZ7TGzWws/VBERyVW/4W5mCeA+4ArgPGC5mZ2X1eybwF53nwXUA//NzIYXeKwiIpKjXI7c64BD7v6mu38MNAJXZ7VxYLSZGVAN/AHoKOhIRUQkZ9bfN5eb2XXAUndfES/fDCxw91VpbUYDzwAzgdHAMnf/X730tRJYCTBhwoR5jY2NeQ26ra2N6urqvLYtNaqlNIVSSyh1gGpJWrRo0XZ3r+2vXS5fkG29rMt+RVgC7AQuBf4YeM7M/sndj2ds5L4eWA9QW1vr9fX1Oey+p6amJvLdttSoltIUSi2h1AGqZaBymZZpAaakLdcArVltbgWe9Mgh4NdER/EiIlIEuYR7MzDdzKbFb5LeQDQFk+4t4EsAZjYBmAG8WciBiohI7vqdlnH3DjNbBWwBEsAGd99jZrfF968D/guw0cx2E03jfNfdjw3iuEVE5DRymXPH3TcDm7PWrUu73QosLuzQREQkX/qEqohIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIByinczWypme03s0NmdkcfberNbKeZ7TGzXxR2mCIiMhCV/TUwswRwH3A50AI0m9kz7r43rc1Y4EfAUnd/y8w+PVgDFhGR/uVy5F4HHHL3N939Y6ARuDqrzY3Ak+7+FoC7Hy3sMEVEZCDM3U/fwOw6oiPyFfHyzcACd1+V1mYtMAz4E2A08AN3/3Evfa0EVgJMmDBhXmNjY16Dbmtro7q6Oq9tS41qKU2h1BJKHaBakhYtWrTd3Wv7a9fvtAxgvazLfkWoBOYBXwKqgJfN7BV3P5Cxkft6YD1AbW2t19fX57D7npqamsh321KjWkpTKLWEUgeoloHKJdxbgClpyzVAay9tjrn7B8AHZvYiMAs4gIiIDLlc5tybgelmNs3MhgM3AM9ktXka+KKZVZrZSGABsK+wQxURkVz1e+Tu7h1mtgrYAiSADe6+x8xui+9f5+77zOxnwC6gC3jQ3d8YzIGLiEjfcpmWwd03A5uz1q3LWm4AGgo3NBERyZc+oSoiEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIByinczWypme03s0Nmdsdp2s03s04zu65wQxQRkYHqN9zNLAHcB1wBnAcsN7Pz+mj3N8CWQg9SREQGJpcj9zrgkLu/6e4fA43A1b20+xbwP4GjBRyfiIjkwdz99A2iKZal7r4iXr4ZWODuq9LaTAYeAS4FHgJ+6u5P9NLXSmAlwIQJE+Y1NjbmNei2tjaqq6vz2rbUqJbSFEotodQBqiVp0aJF2929tr92lTn0Zb2sy35FWAt81907zXprHm/kvh5YD1BbW+v19fU57L6npqYm8t221KiW0hRKLaHUAaploHIJ9xZgStpyDdCa1aYWaIyD/Rzgy2bW4e6bCjJKEREZkFzCvRmYbmbTgCPADcCN6Q3cfVrytpltJJqWUbCLiBRJv+Hu7h1mtoroLJgEsMHd95jZbfH96wZ5jCIiMkC5HLnj7puBzVnreg11d//6mQ9LRETOhD6hKiISIIW7iEiAFO4iIgFSuIuIBEjhLiISoJzOlhEZLJt2HKFhy35a32tn0tgqVi+ZwTVzJhd7WJIjPX6lS+EuRbNpxxHWPLmb9lOdABx5r501T+4GUECUAT1+pU3TMlI0DVv2p4Ihqf1UJw1b9hdpRDIQevxKm8Jdiqb1vfYBrZfSosevtCncpWgmja0a0HopLXr8SpvCXYpm9ZIZVA1LZKyrGpZg9ZIZRRqRDIQev9KmN1SlaJJvuulsi/Kkx6+0KdylqK6ZM1lhUMb0+JUuTcuIiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgPQdqiKSoavL6XSns8txhy6Plru6onVdyXVdTpdHbTq7utt0xcvpbaKf7vVdXaS173ubzi5Sbfa9dYq3X/lNarm7PXGfcT/ueHx/97hJG0e0nN0muW16ne6Zv4vM9k6nkzEeT+/D02qNl5P7rJ8E9fWD+zgq3KVkZD8hkqHicRBET55kG9KehJlP8vQnWXKb04ZH6knXHQxvHDnFse0tWWPKDLfsfWYHTPc2PYOlOxy6t+m+Py18soIlo413B1SX9+yz050PPzzJWS+/EPeT2Wdv++3yYv8v6MfeN3JqVmFQYUZFhZEwi5YrjES8bPG6RIVRYfH6CsMMEvFytH3cT2pdtDy8siJubyTifsBIVGT22b0tGcuf+uh3g/t7IsdwN7OlwA+ABPCgu//XrPtvAr4bL7YB/97dXy/kQAdD8kmf+YSnO0RSr7ZpbdJDp7cnd3/bpx21uDu7ftfB8ddb+36SZh0Z9NwHWUcSWUHQ1d1PakwZYdMzJLt6CZLMcfUMlU532to+ZETzz+Px9AzAjPBLO5JJLpec3fn9F06GQPTETwuF7Cd7HDLpwVIRbxMFUWawGFEflRUVnFVpcX+k2ifDKn2/R3//eyZNPKc76LL67G2/6X2mB1oqsNLWRTWQ6is7BLtDMnus3WNJtkn1k94mDthEhfHKyy/zhYULM+usyBpDfNvMCvt/ocCamo4N+j76DXczSwD3AZcDLUCzmT3j7nvTmv0a+Nfu/i9mdgWwHlgwGAP+xYF3+N4vP6TqtV9kBFnGEVp6qPU4ius+qvJSyZOdO864i+wndnqYdB8xdD+Zsp8MmUcZWdvHbXo7WrG0J+CxinYm/tHYVD+ZT0LSjpjiJ3bqqCrtyCrtiZ1ZU+YTurfwSMT9ZuwjOzxS9VtakKSHV7T9tldf5eKLLszpd5PcPjn2UtLU1ER9/axiD6MgPjWignNHn1XsYZSNXI7c64BD7v4mgJk1AlcDqXB39/+b1v4VoKaQg0xXfVaCCaMqmPDp6h5BlgqKrCd1X0FD9vanOUrp7jvz6Cc9ELqDI3P/GUdyWcG0fds2Llwwv8f22WPv3nd32Gb3XWxRkMwp9jAK4u1RFUwZN7LYwxDJm3k/h69mdh2w1N1XxMs3AwvcfVUf7W8HZibbZ923ElgJMGHChHmNjY15DbqtrY3q6uq8ti01qqU0hVJLKHWAaklatGjRdnev7a9dLkfuvR0S9vqKYGaLgD8HvtDb/e6+nmjKhtraWq/P8+3i6Agxv21LjWopTaHUEkodoFoGKpdwbwGmpC3XAK3ZjczsAuBB4Ap3f7cwwxMRkXzk8iGmZmC6mU0zs+HADcAz6Q3M7DPAk8DN7n6g8MMUEZGB6PfI3d07zGwVsIXoVMgN7r7HzG6L718H/GdgPPCj+I29jlzmhEREZHDkdJ67u28GNmetW5d2ewXQ4w1UkaG2accRGrbsp/W9diaNrWL1khkAPdZdM2fykOx7MPaTi+9t2s2jv3qb73z+FH++ZjPLF0zh7mvOL8pYpDj0CVUJxqYdR1jz5G7aT3UCcOS9dlb/4+tgcKrTU+vWPLkboKDB29u+B2M/ufjept08/MpbqeVO99SyAv6TQxcOk2A0bNmfCtekU12eCvak9lOdNGzZP+j7Hoz95OLRX709oPUSJoW7BKP1vfZBaXsm/RV6P7no7OOzK32tlzAp3CUYk8ZWDUrbM+mv0PvJRaKPTyv3tV7CpHCXYKxeMoOqYYmMdcMqjGGJzFCrGpZIvdE6mPsejP3kYvmCKQNaL2HSG6oSjOQbl8U4W6avfRfjbJnkm6bJOfaEmc6W+QRSuEtQrpkzuddAHYqQ7WvfxXD3Nedz9zXn09TUxP+7qb7Yw5Ei0LSMiEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISoJzC3cyWmtl+MztkZnf0cr+Z2d/G9+8ys7mFH6qIiOSq33A3swRwH3AFcB6w3MzOy2p2BTA9/lkJ3F/gcYqIyADkcuReBxxy9zfd/WOgEbg6q83VwI898gow1swmFnisIiKSo8oc2kwG3k5bbgEW5NBmMvDb9EZmtpLoyB6gzcz2D2i03c4BjuW5balRLaUplFpCqQNUS9Jnc2mUS7hbL+s8jza4+3pgfQ77PP2AzLa5e+2Z9lMKVEtpCqWWUOoA1TJQuUzLtABT0pZrgNY82oiIyBDJJdybgelmNs3MhgM3AM9ktXkGuCU+a+ZC4H13/212RyIiMjT6nZZx9w4zWwVsARLABnffY2a3xfevAzYDXwYOAR8Ctw7ekIECTO2UENVSmkKpJZQ6QLUMiLn3mBoXEZEyp0+oiogESOEuIhKgkg93MxthZq+a2etmtsfMvh+vH2dmz5nZwfjfTxV7rLkws4SZ7TCzn8bL5VrHYTPbbWY7zWxbvK5caxlrZk+Y2T+b2T4zu6gcazGzGfHjkfw5bmbfKdNa/kP8fH/DzB6Nc6Ds6gAws7+M69hjZt+J1w16LSUf7sBHwKXuPguYDSyNz8i5A3jB3acDL8TL5eAvgX1py+VaB8Aid5+ddr5uudbyA+Bn7j4TmEX0+JRdLe6+P348ZgPziE5ueIoyq8XMJgPfBmrd/fNEJ3LcQJnVAWBmnwf+HdEn/WcBXzGz6QxFLe5eNj/ASOA1ok/I7gcmxusnAvuLPb4cxl8TP5CXAj+N15VdHfFYDwPnZK0ru1qAs4FfE59cUM61ZI1/MfBSOdZC9yfexxGd0ffTuJ6yqiMe5/XAg2nLfwX8p6GopRyO3JNTGTuBo8Bz7v4rYILH59LH/366mGPM0VqiB7YrbV051gHRJ5C3mtn2+LISUJ61fA54B/j7eLrsQTMbRXnWku4G4NH4dlnV4u5HgHuBt4guYfK+u2+lzOqIvQFcYmbjzWwk0SnjUxiCWsoi3N2906M/NWuAuvhPnbJiZl8Bjrr79mKPpUAWuvtcoiuCftPMLin2gPJUCcwF7nf3OcAHlMGf+6cTf9jwKuAfiz2WfMTzz1cD04BJwCgz+7Pijio/7r4P+BvgOeBnwOtAx1DsuyzCPcnd3wOagKXA75NXnoz/PVrEoeViIXCVmR0murLmpWb2MOVXBwDu3hr/e5RoXreO8qylBWiJ/xoEeIIo7MuxlqQrgNfc/ffxcrnVchnwa3d/x91PAU8CF1N+dQDg7g+5+1x3vwT4A3CQIail5MPdzM41s7Hx7SqiB/6fiS558LW42deAp4szwty4+xp3r3H3qUR/Mv8fd/8zyqwOADMbZWajk7eJ5kPfoAxrcfffAW+b2Yx41ZeAvZRhLWmW0z0lA+VXy1vAhWY20syM6DHZR/nVAYCZfTr+9zPAnxI9NoNeS8l/QtXMLgD+gegd8wrgcXf/azMbDzwOfIboP8P17v6H4o00d2ZWD9zu7l8pxzrM7HNER+sQTWs84u73lGMtAGY2G3gQGA68SXT5jArKs5aRRG9Gfs7d34/Xld3jEp/yvIxoCmMHsAKopszqADCzfwLGA6eA/+juLwzFY1Ly4S4iIgNX8tMyIiIycAp3EZEAKdxFRAKkcBcRCZDCXUQkQLl8QbbIkIpPE3shXvwjoJPoEgEAde7+cVEGdhpm9m+BzfF58yJFp1MhpaSZ2V1Am7vfWwJjSbh7Zx/3/RJY5e47B9BfpbsPyUfR5ZNH0zJSVszsaxZd33+nmf3IzCrMrNLM3jOzBjN7zcy2mNkCM/uFmb1pZl+Ot11hZk/F9+83s+/l2O/dZvYq0XWNvm9mzfH1uddZZBnR5agfi7cfbmYtaZ+svtDMno9v321mD5jZc0QXK6s0s/8e73uXma0Y+t+qhEjhLmUjvmDctcDF8YXkKoku5QAwBtgaX8zsY+Auoo+tXw/8dVo3dfE2c4EbzWx2Dv2+5u517v4y8AN3nw+cH9+31N0fA3YCyzy6nnp/00ZzgCvd/WZgJdEF5eqA+UQXYftMPr8fkXSac5dychlRAG6LLjlCFdFH7QHa3f25+PZuosvEdpjZbmBqWh9b3P1fAMxsE/AFoudBX/1+TPelFgC+ZGargRHAOcB24H8PsI6n3f1kfHsx8K/MLP3FZDrRR9JF8qZwl3JiwAZ3/6uMlWaVRCGc1EX0DV7J2+n/z7PfZPJ++m33+I2p+Lot/wOY6+5HzOxuopDvTQfdfxlnt/kgq6a/cPcXECkgTctIOXke+KqZnQPRWTV5TGEstug7U0cSXTP8pQH0W0X0YnEsvirmv0m77wQwOm35MNFX3ZHVLtsW4C/iF5Lk96BWDbAmkR505C5lw913x1cLfN7MKoiusncb0DqAbn4JPAL8MfCT5NktufTr7u+a2T8QXd74N8Cv0u7+e+BBM2snmte/C/g7M/sd8OppxvMA0ZUBd8ZTQkeJXnREzohOhZRPjPhMlM+7+3eKPRaRwaZpGRGRAOnIXUQkQDpyFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJ0P8HfLcy7/zjy3oAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -648,20 +439,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.06521739130434782\n"
+      "Probabilité de défaillance d'un joint = 0.06521739130434782\n",
+      "Probabilité de défaillance de deux joints = 0.004253308128544423\n",
+      "Probabilité de défaillance d'un lanceur = 0.01270572944054793\n"
      ]
     }
    ],
    "source": [
     "data = pd.read_csv(\"shuttle.csv\")\n",
-    "print(np.sum(data.Malfunction)/np.sum(data.Count))"
+    "p = np.sum(data.Malfunction)/np.sum(data.Count)\n",
+    "print(\"Probabilité de défaillance d'un joint = {}\".format(p))\n",
+    "print(\"Probabilité de défaillance de deux joints = {}\".format(pow(p,2)))\n",
+    "print(\"Probabilité de défaillance d'un lanceur = {}\".format(1-pow(1-pow(p,2),3)))"
    ]
   },
   {
@@ -686,6 +482,390 @@
     "analyse et de regarder ce jeu de données sous tous les angles afin\n",
     "d'expliquer ce qui ne va pas."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": true
+   },
+   "source": [
+    "# Conserver la multiplicité des dysfonctionnements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Garder l'étude préliminaire"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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 numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "data = pd.read_csv(\"shuttle.csv\")\n",
+    "\n",
+    "%matplotlib inline\n",
+    "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
+    "\n",
+    "data[\"Frequency\"]=data.Malfunction/data.Count\n",
+    "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vérifier les grandeurs statistiques\n",
+    "\n",
+    "Attention, Python fait des erreurs de calculs en stat..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Generalized Linear Model Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>      <td>Frequency</td>    <th>  No. Observations:  </th>  <td>    23</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>    21</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Link Function:</th>        <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -3.9210</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>           <td>Thu, 06 Jan 2022</td> <th>  Deviance:          </th> <td>  3.0144</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>               <td>18:22:03</td>     <th>  Pearson chi2:      </th>  <td>  5.00</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Iterations:</th>         <td>6</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>   <td>    5.0850</td> <td>    7.477</td> <td>    0.680</td> <td> 0.496</td> <td>   -9.570</td> <td>   19.740</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Temperature</th> <td>   -0.1156</td> <td>    0.115</td> <td>   -1.004</td> <td> 0.316</td> <td>   -0.341</td> <td>    0.110</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                 Generalized Linear Model Regression Results                  \n",
+       "==============================================================================\n",
+       "Dep. Variable:              Frequency   No. Observations:                   23\n",
+       "Model:                            GLM   Df Residuals:                       21\n",
+       "Model Family:                Binomial   Df Model:                            1\n",
+       "Link Function:                  logit   Scale:                          1.0000\n",
+       "Method:                          IRLS   Log-Likelihood:                -3.9210\n",
+       "Date:                Thu, 06 Jan 2022   Deviance:                       3.0144\n",
+       "Time:                        18:22:03   Pearson chi2:                     5.00\n",
+       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
+       "===============================================================================\n",
+       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "-------------------------------------------------------------------------------\n",
+       "Intercept       5.0850      7.477      0.680      0.496      -9.570      19.740\n",
+       "Temperature    -0.1156      0.115     -1.004      0.316      -0.341       0.110\n",
+       "===============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "\n",
+    "data[\"Success\"]=data.Count-data.Malfunction\n",
+    "data[\"Intercept\"]=1\n",
+    "\n",
+    "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
+    "logmodel.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideOutput": true
+   },
+   "source": [
+    "## Garder l'étude graphique mais avec toutes les valeurs !!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": [
+    "%matplotlib inline\n",
+    "\n",
+    "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
+    "data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n",
+    "\n",
+    "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
+    "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Afficher l'intervalle de confiance de la prédiction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cI5KVYedTY/bgG7QNPqkdb+nX4STUrKfhHqEsZ2gPfk6Wk91HWnRM+CTm8Qd17lw162m4T0FoD76CfJeTF4600KLdJJNWTZOesapmNw33KcrOcPCp9RXkZDh4/nALHToiYVJq7/foaJFqVtNwn4aczFDAZzpt7DrcTNeADlWQjI7q3ruaxTTcpynP5eRTN1Vgtwm7DjXrxNtJ6HS7G29Ah3FWs5OG+wzMyc7g/nUVWMaw61AzA55AooukxvAFLE616oFVNTtpuM9QcW4mf7KuAo/fYtchnfAj2dQ0a9OMmp003KOgLN/FJ9eW0+fx8/zhZh16Nol0ub209mmvJjX7aLhHSWVhNvesmkdHv5eXjrYS1Em3k8axZh0KWM0+Gu5RtGRuLh9dUUpDzxB7TrTp8LNJ4oweWFWzkIZ7lN04v4CtS4o50z7AW2e7NOCTgC9gcaZtINHFUAp/0OKdc11x2ZaGewxsWFjIugVzONzYy8EGHcQqGRzVA6sqwXyBUKeLhjjNOaDhHgMiwq1LS7ihNJe9tV2c0unfEq6930OHW89YVYnh8QfZdaiJ5ovxO7iv4R4jIsL2G8uoLMzi9ZPtOpJkEjime+8qAULB3kxLb3x3LjTcY8hhs/GJ1eUUZmfwUk0rnW4dpiCRTrW5dShgFVcef5BnDzbR1hf/X40a7jGW6bRz37r5ZDhsvHCkBbdHZwlKFK/f4my7HlhV8THsC/LMgSY6+hOzU6fhHgd5Lif3rp2PL2DxwpEW7ZaXQMdatGlGxd6QL8AzB5sS+mtdwz1O5uZl8rHV8+ge9PHK0TY9ySlBmi8Oc3FQB3lTsTPkC/DsgSa6EtwMq+EeRwuLc7hjeSn1PUNUn+7QPvAJcrxFey+p2BgN9oHE70BouMfZqvkFbFxYyLGWfu0DnyAnWvuw9JeTirJkCnbQcE+Im5cUszTcB/5ot/beiLdBb5DzXYOJLoZKI8O+YFIFO0QY7iKyQ0ROi0itiHz3KutsE5HDInJcRP4Q3WKmFxHhrpVlzMt3sfOMpdPBJcBxPbCqomTYF+SZg8kV7BBBuIuIHfhn4B5gJfA5EVk5bp05wGPAvcaYG4EHY1DWtOKw2/jEmnJynbBbu0jG3YWuIQa8OrmKmplhX6gfe6IPnk4kkj33zUCtMea8McYH7ATuG7fO54HfGmMaAIwxHdEtZnrKyXTw1RV2/EHD7iOtOg58HFnGcEIPrKoZGDlBKVlPTpTJemyIyAPADmPMw+HbXwS2GGO+OWadnwJO4EYgD/hHY8y/TfBcjwCPAJSVlW3YuXPntArd1+/GKxnTemyycVpejvU5efKExcoi4YvLbdhEEl2saXFaXvy2zEQXI2J2m1CcM/H7aGBggNzc3DiXKPrSpR6QXHUxBi4O+QhM48C80y44Ld+063L77bcfMMZsnGw9RwTPNVHSjK+RA9gAfBTIAt4VkfeMMWcue5AxTwBPAGzcuNFs27Ytgs1faferb9Bsr5rWY5NNhaeO3PLF3Bro5Q9nOnmmOZ+t15ckuljTUuGpo9m1ONHFmJLNaypZUJR9xfLq6mqm+/5MJulSD0ieuoyMFdMWmN6xsvICF4VD52Nel0iaZZqABWNuVwItE6zzqjFm0BjTBbwFrI1OEWeHtZUFrK4oYH/9RU7qKJJxowdW1VR4A0GeO9SckLFipiqScN8HLBWRxSKSAXwWeGHcOs8DHxERh4hkA1uAk9EtanoTEW67YS6VhVm8ebKDll6d9zMeajsG8Oik5ioCvoDF84daaE2BYIcIwt0YEwC+CbxGKLCfNsYcF5GvicjXwuucBF4FaoAPgF8YY47FrtjpyW4TPr66nFyXgxdrWunXHjQx5w8aTrW5E10MleT8QYvnDjfTnEI7XRH1czfGvGyMucEYs8QY84PwsseNMY+PWefHxpiVxphVxpifxqrA6c7ltHPv2vkELcPuIy3agyYOdJx3dS3+oMXzh1viOtFGNOgZqkmoKCeDe1bNo3vApxNtx0Gn26snkqkJBYIWu4+00JiCk+1ouCepRSU53LK0hHOdg7xf15Po4qQ93XtX4wUtw4s1rdTHac7TaNNwT2I3LZjDivI83q/r4WyHtgvH0qk2tzaBqVGhYG+hLoXHINJwT2Iiwh3LSykvcLHneHvSngmXDnwBizPt+gWqwLIMLx9t5Xxn6gY7aLgnPYfNxsdXl+Ny2tld08KQT8dDiZWj2jQz61mW4dXjbdR2pP50jBruKSAn08En1pQz5Avyss7iFDNtfR463HpgdbYyxrDnRBun06RrrIZ7iijLd3HnilKae4f5w5nORBcnbemB1dnJGMPrJ9o52ZoewQ4a7ill+bx8NlQVcrS5T5sQYuRkqx5YnW2MMbx5siPtpl/UcE8xN19fzMLibKpP6xAFsaAHVmef35/uSMudJQ33FGMTYceN88h3OXmxplUn+YiBmqb0+6Crif3+dAdHGtPz9dZwT0Eup51PhocoeLGmlUBQmxGiqb3fQyCoB63T3R/OdHI4jSep13BPUUU5Gdx9Yxkdbi9vnurQIQqibEhHikxrfzzbycH6i4kuRkxpuKew6+bm8qHrijjV5uZQY/rugSSCxx/UoYDT1N6zXey/kN7BDhruKW/zoiKWzM1h79ku6rtT+4y6ZKMTeaSft2u72HdhdozVpOGe4kSEu1bOoygng1eOtdE3rAdYo6WmqU+bu9LIO7VdfDCLBuHTcE8DGQ4bn1hTDqBjwEdR75CfCyk6IqC63Dvnumbd6Koa7mliTnZoDPieQR+vn2jXPc4oOdyY/m2z6e6dc128f352BTtouKeVhcU5bL2+hNrOAfbNggNG8VDfPUTPoC/RxVDTNFuDHTTc0876qjksm5fHu+e7Od+Z+iPbJZoxuveeqt6pnb3BDhruaUdEuHN5KaV5mbx2vF33OqPgZKtbu0WmmL1nZ18b+3ga7mnIYbfx8TXl2G3C7poWvBpMM+ILWNotMoW8daZz1nR3vBYN9zSV73Ly8dXl9A/7eeV4G5YeYJ2RQw29WDqOftKrPt3BgTQ/8zRSGu5prKIwi9tumEt99xDvnutOdHFSmtsT4LSOFpm0jDH87lQ7h9J4rJip0nBPc6srClg1P5/99Rd1KNsZ2q97hEnJGMMbJ9N3dMfp0nBPcyLCtmWhSbZfP9Gu08jNQJfby4UuHeIhmViW4dVjbTqD1gQ03GcBu00uTbJ9pFUn2Z4B3XtPHkHL8NLRVk6lyZyn0abhPkvkZDr45Jpyhv1BXqpp1Um2p6mxZ4j2fv31k2j+oMULR5qp7dBzOa5Gw30WKc13sX1FGS19HqpP6xjw0/XeeT04nUjeQJBdh5q50KXj/lyLhvsss2xeHhsWFnKspZ8abaeclvOdg3rsIkGGfUGePdBM80WdP3gyGu6z0M1LillUnM1bZzpp7NG9n+mYzae1J4plDE/vb9RmsQhpuM9CNhF2rJrHnKwMXj7WqmPAT8O5zgG6BryJLsas0TPoo2fQr8NpTIGG+yyV6bDzybXlGBMaA94b0CEKpsIY3XuPl7Y+D0/vb9SzrKdIw30Wm5OdwcdWl9Mz5OO14+364Zmisx1uOrSJIKbquwd59mATwz7d+ZgqDfdZrqoom9uWzqWua5B3dIiCKTEG3j7XlehipK0TLf08f1hnFpsuDXfFmsoCVlcUcKD+Iidb+xNdnJRyoWtID0rHwL4LPew50abnY8xAROEuIjtE5LSI1IrId6+x3iYRCYrIA9Erooo1EeG2G+ZSWZjFmyc7aOnVbmZTsbdW996jxbJCA4DtPduFthLOzKThLiJ24J+Be4CVwOdEZOVV1vsH4LVoF1LF3sgQBbkuBy/WtNKvPWgi1tbn0UHZosAXsNhd06IDgEVJJHvum4FaY8x5Y4wP2AncN8F63wKeBTqiWD4VRy6nnfvWzidoDC/UaA+aqXjrTCf+oLYNT5fb4+c/DzRyvlMHZosWRwTrVACNY243AVvGriAiFcD9wB3Apqs9kYg8AjwCUFZWRnV19RSLG2L5hqmQumk9Ntk4LS8VnuSpS4UdvrwM/uW4j+qaOr6ywoZNJKLHJltdZmLKdfHAnjfOk5MZyUcqfgYGBqb9OYuXgGXoHfLjNIaKa6yXLu8vp18YsHwxf10ieSdO9Mke3xr2U+CvjDFBuUYQGGOeAJ4A2Lhxo9m2bVuExbzc7lffoNleNa3HJpsKTx3NrsWJLsZlsufBbYFefn+6k52Nedx2w9yIHpeMdZmu6dTFYRO+tGURBVnOGJVq6qqrq5nu5ywezra7ee14G/7MyRvY0+X9VV7gonDofMxfl0jCvQlYMOZ2JdAybp2NwM5wsJcAHxORgDHmuaiUUsXdmso5XBzyc7ixlznZTtZWzkl0kZJewDL84Uwn966dn+iiJD1jDO+e7+aDuh49cBojkYT7PmCpiCwGmoHPAp8fu4IxZvTrVER+CbyowZ76PrK0hL5hP3843UmBy8mikpxEFynpnesYoLbDzfWleYkuStLyBSxeO96mw/XG2KQHVI0xAeCbhHrBnASeNsYcF5GvicjXYl1AlTg2EXbcOI+SvExePtZKp1vHUonE70514PHrweiJXBz0sXNfgwZ7HETUz90Y87Ix5gZjzBJjzA/Cyx43xjw+wbpfMcY8E+2CqsTIcNi4d818Mh12XjjSwoBHZ3GazKA3SPXpzkQXI+mc6xzgN/sa6B7Qwb/iQc9QVZPKdTm4d+18vIEgLxzR08EjcbK1n/OduncKoROT/ni2MzRAnV/fO/Gi4a4iMjcvk4+tLqdr0MvLx1qx9LTwSb1+op0B7+z+peP2+HnmYBP7L1zUA6dxpuGuIraoOIfbl5VS3z3E73WavkkN+YK8eqxt1v6fzncO8B/vN+isSQmSXGdcqKS3uqKA/mE/++svkudysnlxUaKLlNQae4Z4v66HD11XnOiixE0gaPHH2i4ON/Qmuiizmoa7mrKblxQz4A3w7vlucl0OVpbnJ7pISe398z2U5btYPAu6kna4Pbx2rI0uPWiacBruaspEhDtXlDHoC/DmyXZyMuwsLJ5ecC3reIVbGh4jz9uOO7OMvVVf53TpPVEucWJZxvDy0VYe2rSAktzMRBcnumqehje/j+lrwpczn4OVX6OrZEeiS6XQNnc1TSOjSBbnZPLS0VbapjEj0bKOV9h+7ofke9sQDPneNraf+yHLOl6JQYkTyxeweP5wC0O+NDrAWvM07P429DUiGDIHm/no2R+k5euXijTc1bRlOuzct24+WU47LxxuoXN4agcOb2l4DKd1+ZeC0/JwS8Nj0Sxm0ugf9rPrUHPanOBk3vw78F9+sDSdX79Uo+GuZiQn08Gf3BQay+8Xx4NT6vqX522f0vJ00NHvZdeh5pQfTrmxZwj6mie8L51fv1Si4a5mrDA7g/vWzWfQD89NYc/UnVk2peXpoq3PM6X/UzJxe/y8VNPKMweaZu3rlyo03FVUlOW7+PIKG71Dfl440hLRxBV7q76O3+a6bJnf5mJv1ddjVcyk0dLr4en9jfSlyIxXvoDFO7Vd/Os7F0ZnnZrNr18q0HBXUbN0jo27byyjrc/DSzWtk05ufLr0Hl5f8jf0Z87DIPRnzuP1JX+Tdr1lrqZ7wMdT+xpo65v6weh4CVqGw429/PKdOt6v68EfvPSazvbXL9lpV0gVVUvL8vAFLd442cFrx9vYsWreNWdyOl16z6wOg0FvkKf3N7L1+hLWV83hWpPdxFPQMpxo6ef9um7c1xgsbra/fslMw11F3Y3zC/AGLP54tgvnyQ7uXFGaNKGVjIKW4a0znTT0DHLnijLyXImbyckbCHKsuY+D9b2zflycVKfhrmJifVUhvoDF+3U9OO3CbTfM1YCfxIWuIf71nQtsXFTExoWFOOzxazXtGvBypLGXU21uHfUzTWi4q5jZsrgIf9DiYEMvDruNrUuKNeAn4Q8a3j3XzbHmPm6qmsOqigIyHfaYbGvIF+BM+wAnWvppn8ZJaCq5abirmBERbrm+BH/QcKD+Inab8OFZNIDWTLg9Ad4608X7dT0sn5fH0tI8KguzZvTlaIyhc8BLY88Q5zoGaekb1mF405iGu4opEeH2ZXMJWoYP6nqwi+hIklO3iTAhAAAXeklEQVTg9VscaezjSGMfOZl25s/Jorwgi9K8TAqyneRlOiYMfI8/SL/Hjzdg8c65LjrdXlr7PAz7Uq9vvZoeDXcVcyLCR1eUYoVnvLcJbFykAT9Vg94gZ9sHONt+aYYnmwhOh+C02bDZBH/Qwh+wCIS7oVZ4/Jw435OoIqsE0nBXcWETYfvKMixjePtcNwbYpAE/Y5YxeP0GL3oQVF1Ow13FjU2Eu1fOQ2jnnXDAb9aAVyomNNxVXNlswl03loHAu+e6MZZh8+Ii7UWjVJRpuKu4s4lw18oybALv1fUQsAw3azdJpaJKw10lhE2E7SvKsIuwv/4iActw69ISDXilokTDXSWMiHDH8lIcNhuHG3vxBy3uWF56zbFolFKR0XBXCSUi3HpDCRkOGx9c6MEfsLjrxnnYbRrwSs2EhrtKOBHhw0uKyXDY2FvbhTfYwsdXl+OM49gqSqUb/fSopLFhYSEfXV5KQ/cQvz3YzHAKzlSkVLLQcFdJZVVFAR9bXU7ngJdn9jfR70mNmYqUSjYa7irpXF+ay5+sm8+AL8DT+xrpdHsTXSSlUo6Gu0pKlYXZPLihEhHhmQNN1HcPJrpISqUUDXeVtEpyM/nMxkryshy8cKSFYy19iS6SUilDw10ltTyXkwc3VFJZmM2bJzt4u7YLo4OQKzUpDXeV9DIddu5dO59V8/PZX3+Rl4624g/qKIhKXUtE4S4iO0TktIjUish3J7j/CyJSE/57R0TWRr+oajaz20Jns966tITznYP85/4m3NqTRqmrmjTcRcQO/DNwD7AS+JyIrBy3Wh1wmzFmDfBfgSeiXVClRISbqgq5d+18+ob9/OaDRpovDie6WEolpUj23DcDtcaY88YYH7ATuG/sCsaYd4wxF8M33wMqo1tMpS5ZVJLDQ5sWkOm08dtDTRxp6tV2eKXGiWT4gQqgccztJmDLNdb/n4FXJrpDRB4BHgEoKyujuro6slKOY/mGqZC6aT022TgtLxUerctUVdjh+tWG35wRqk934u7p4v4lNjLs0RmTJl1el3SpB6RPXZx+YcDyTTv/IhVJuE/0aZlwN0lEbicU7rdMdL8x5gnCTTYbN24027Zti6yU4+x+9Q2a7VXTemyyqfDU0exanOhiREUi6nLXTYaCuh7er+uhftjOx1eXMyc7Y8bPmy6vS7rUA9KnLuUFLgqHzjPd/ItUJM0yTcCCMbcrgZbxK4nIGuAXwH3GmO7oFE+paxMRPnRdMfetm4/bE+A3HzRytt2d6GIplXCRhPs+YKmILBaRDOCzwAtjVxCRKuC3wBeNMWeiX0ylrm1RcQ6f31xFUU4GLx9r4/enOghod0k1i03aLGOMCYjIN4HXADvwpDHmuIh8LXz/48D/BRQDj4Vn0gkYYzbGrthKXSk/y8kDGyp551wXBxt6aekbZseN8yjOzUx00ZSKu4jGczfGvAy8PG7Z42OuPww8HN2iKTV1K7te5ZG+x9jvnMf/Mfi/8tQHHj5d3sVfD/yIfF877swy9lZ9ndOl90R928s6XuGWhsfI88Z2O5HYVvsj1rbv4q1l3+OBA5/lSNn9VF9/xSkqKo3pGaoqbSzreIXt535IvreNO+yHeS3jr9gix9nZUspfDHyRTpNPvreN7ed+yLKOCTt0RWXbgonZdiKxrfZHrGt/FhuhZikbFuvan2Vb7Y/iXhaVOBruKm3c0vAYTsszertUevk354941PFL3rFuZIf3H3gpuAWn5eGWhsdium0gJtuJxNr2XVd0cZPwcjV7aLirtJHnbb9imU0MX3Hs4aWMv6FSOvmG/8/5hu9b+D3RHUJ4om1fa3ksCRMfSL7acpWeNNxV2nBnll31vuttLfw243v8peMp9libuNP3E063uaN2ZuvVtn2tMsWKucrH+mrLVXrSV1uljb1VX8dvc122LICDgDgBcIjFNxzPs8v1d+S7nLx6vI3dNa30D898ALKJtu23udhb9fUZP/dUHSm7/4qzDE14uZo9Iuoto1QqGOmZMr7HyvhlDVVf5t65qzjc2Mu757r51Xv1fOi6YtYtmIPdNr3hC6627UT0lhnpFTPSxm5h094ys5CGu0orp0vvmTBQxy+zAeurCrl+bi7VZzrZW9vFydZ+ti2bS2VhdlS3nQjV13+X6uu/S4Wnjn/c+n6ii6MSQJtl1KyWn+Xk3rXz+cSacnxBi2cPNvPKsVZ6vTrKpEptuueuFLBkbi5VRdnsr7/IgfqL/LjTsH5hNxsWFuK06z6QSj0a7kqFOe02PnxdMTeW53PgdD3v1/VwrKWPD11XzMryfGwSneGElYoH3SVRapz8LCd/utzOAxsqyct08ubJDn79fgPnOwd0UhCVMjTclbqKijlZfGZjJR9bNY+gZdhd08pT+xtp6BnSkFdJT5tllLoGEWFpWR5L5uZyoq2f98/3sOtQM/MLXGy5rpgFhVmINteoJKThrlQEbDZh1fwClpflcbyln/31F9l1qJnyAhebFhWxqDhbQ14lFQ13pabAYbexdsEcbpyfz/HWfg7UX+SFIy2U5GawoaqQpWV50z4RSqlo0nBXahocdhtrK+ewan4Bp9vdHLhwkddOtPP2uW7WhcPf5bQnuphqFtNwV2oG7DZhZXk+K+blcaF7iIP1F9lb28V757tZUZ7P2soCnQlKJYSGu1JRICIsLslhcUkOnW4vhxt7OdHaz9HmPirmZLGmsoDr5ubgsGkHNRUfGu5KRdncvEy2ryzjlutLON7ax9GmPl451kaW086K8jxWlufr3ryKOQ13pWIkK8POxoVFbKgqpKFniGPN/Rxu7OVgQy9l+ZmsKM/nhtI8sjK0bV5Fn4a7UjEmIiwszmFhcQ5DvgCn2tycaO2n+nQnb53pZGFxDsvK8lhckkOGQ5ttVHRouCsVR9kZDtZXFbK+qpBOt5fTbW5Ot7up6xrEYRMWleSwtDSXRcUa9GpmNNyVSpC5eZnMzctk6/XFtPR6ONPuprZzgNqOAew2oaoom+vCB2lzMvWjqqZG3zFKJZiIUFGYRUVhFrctm0trr4fajgHOdQ1Q1xWayLssP5PFJTksKs6hNC9Tz4ZVk9JwVyqJ2MYE/a03lNA14KOua5C6rkHeO9/De+d7yHLaWVCURVVRNlVF2eS5nIkutkpCGu5KJSkRGW262by4iCFfgIaeIS50D9HQPcSZ9gEA5mQ5qSzMorIwm/lzXBr2CtBwVyplZGc4WD4vn+Xz8jHG0D3oo6FniKaLw5xpH+BYSz8A+S4H8+dkUV7gwpZlsDKNTjQyC2m4K5WCRISS3ExKcjNZX1WIZRk6B7y09A7T3DtMffcQp9rc/B5w2s9Rlu8K/2VSlu8iL9Oh7fZpTsNdqTRgs8logN9UVYgxhr5hP56uRk4O59HW5+FQw0Ws8BwjLqeN0jwXc3MzKcnLYG5uJnOyM3REyzSi4a5UGhIR5mRnUFFqY56rFICAZdE14KO930On2zs6Bk4wPKuUXYTCHCfFOZkU5WRQnJtBUXYG+VlODf0UpOGu1CzhsNmYl+9iXr5rdFnQMlwc8tE14KVrIHTZ0jfM6Xb36Do2gYIsJ4XZGczJdjInK3RZkOUk1+XQ9vwkpeGu1Cxmt11qux/LF7DoGfRxccg3etk75Ke+Z4igdWn+WJtAnisU9PkuB/lZTvJcDvJcocvcDAc23etPCA13pdQVMhw25hW4mFfgumy5MYYBb4DeIT+9w376h/30Dfvp9/g51+ll2B+8bH0BcjId5GY6yMm0hy/Dfxl2cjIdZGfYyXLa9QBvlGm4q5QgAk67DaddwpeXrjvsNpw2wWG34bALTlv40i7YbTYcNsEe/rOJ4Ahf2myhk4ZsIoiEgmhEzf5mtm2swjJgMFgGLMsQtAxBE7r0By0CQUPAsvAFDL6ghS9g4Q0E8fotvAELjz+IJ3w7HYhIeK/cyYIJ7vcHLdyeAP0ePwOeAG5PALfXz6A3yMVBP40Xh/EFrvxfiECW0x4K+gw72U4HWeHQz3LacTltDBuLgRwvWU47mc7Q66pfCFen4a6ibiSIM8Jhm+GwXXbbabeR4QjdHgnpy247wstG7w/djucH2WETSvNdk68YoaBl8PiDDPuDDPuCDPmCDPkCDPuCDIavD/mCDHpDywJjmj5SidNuoygng6KcjKuu4w9aDHoDDHqDDIbrPVL/kf9N27CHYV8QX3D8F0HD6DW7CJlOG5kOGy6nnQxH6Hqm49L1jJH32sj7a2SZ3YbTEXovpusxg4jCXUR2AP8I2IFfGGN+NO5+Cd//MWAI+Iox5mCUy6qiwCaCwy6je7NOuy18KTjCe7yOMXu+DtulcHXYQ3tLGY7Q5UjwjgT2wfdauP/W63HadTTD8ew2GW2OmFDN0/D296GvCQoq8W37Wzj0Hzgb3hpdpbfsZpoWfYolNf+NrOE2Bl3zeGfRNwgELW5peIw8bzvuzDL2Vn2d8v4jrG3fxVvLvscDBz7LkbL7qb7+u1dsdlnHK1c89nTpPVddHsnjR7YtWBhsV2zbabcxJzuDLQOvcEvj5Y8FQs9n2nHnlvH7yq9ztHA7w/4guYNNNEopHr9Ffu8JCntqGA5Ct1VEg9zARauQ3iH/6K+nSL8fR97/o+/n0ff2mM/EyHXb5dftYz5LDpstfHnpl6LdJtjl8ttBy2BM7L+8Jw13EbED/wxsB5qAfSLygjHmxJjV7gGWhv+2AD8LX84aIiM/8UM/XUNNAIR/8gt2CfVFHmkGsIebBJwdNhaX5ITvC+2N2Ma8IUau22yE3zzhgLbZLls20vxgs419s135povlwa2RPXY1RTVPw+5vg384dLuvkYzn/+yK1Qrb36Gw/Z3R27meVu46+32MMYjlByDf28aOs48iWKPNTDYs1rU/S0GWkwOr/hZ/0OALBKlqfomPnPshTssz+tjt535Ief8RVnW+dMVy4LKAX9bxCtvHPf7us9/DhhndtoS3DVwW8BM99q6z3wcRHOZSXT5W90OcttB2Kxw2XK680GP7f4jT5oHw281vXLy+6G8uK18gaI1pKrPwh6/7ghb+oME/six8OzCy3AotH/JZBKzAaPNb0ArdF41cvmexk9tvn/nzXEske+6bgVpjzHkAEdkJ3AeMDff7gH8zoa+j90RkjoiUG2Nao15iQidg3HFDabidNNxeOsF1W/g64eU2EYRLQQyhwB277PLHTnw5st5IUI/cNx3V1efYdlNFtP41KhW9+f1LwT5VQR/j33k2JmjTBq6r/0+u+8rPLy38vx+HcLiOcFoe1nU8h5jgFcu3t/6cxXd8NXTcwTKsOPzz0XAeYefK5BNgbcdzdN76w/DetOG2g49f8VgHAcY/3Gl52Nb0MzwrPo2tzUZVUTa3HfzZFY91Wh5ua/oZAzd86rLlZoLyTBTOU8nrQDB8vMW69IUQsEaOv1x+PWhZBK3w8vAXRKbTxjJnzxS2OD0y2c8DEXkA2GGMeTh8+4vAFmPMN8es8yLwI2PM3vDtN4G/MsbsH/dcjwCPhG8uA05Ps9wlQNc0H5tstC7JKW512VBu2xCr5+4cMszNvhT/B1qtAzPZ7kweH4XHlgBd13rs2G0kuZm8vxYaY+ZOtlIke+4T7ZKO/0aIZB2MMU8AT0SwzWsXSGS/MWbjTJ8nGWhdklO61EVE9tf3WilfD0if1wTiU5dIGkib4LJeT5VAyzTWUUopFSeRhPs+YKmILBaRDOCzwAvj1nkB+JKEfAjoi1V7u1JKqclN2ixjjAmIyDeB1wh1hXzSGHNcRL4Wvv9x4GVC3SBrCXWF/GrsigxEoWkniWhdklO61CVd6gFalymZ9ICqUkqp1KOdkpVSKg1puCulVBpK+nAXEZeIfCAiR0TkuIj8XXh5kYi8LiJnw5eFiS5rJETELiKHwucGpHI9LojIURE5LCL7w8tStS5zROQZETklIidF5MOpWBcRWRZ+PUb++kXkL1K0Lv9b+PN+TER+E86BlKsHgIj8ebgex0XkL8LLYl6XpA93wAvcYYxZC6wDdoR75HwXeNMYsxR4M3w7Ffw5cHLM7VStB8Dtxph1Y/rrpmpd/hF41RizHFhL6PVJuboYY06HX491wAZCnRt2kWJ1EZEK4NvARmPMKkIdOT5LitUDQERWAf8LoTP91wKfEJGlxKMuxpiU+QOygYOExq05DZSHl5cDpxNdvgjKXxl+Ie8AXgwvS7l6hMt6ASgZtyzl6gLkA3WEOxekcl3Glf8u4O1UrAtQATQCRYR69L0Yrk9K1SNczgcJDbY4cvu/AP9nPOqSCnvuI00Zh4EO4HVjzPtAmQn3pQ9fliayjBH6KaEXduzgH6lYDwidgbxHRA6Eh5WA1KzLdUAn8D/CzWW/EJEcUrMuY30W+E34ekrVxRjTDPyE0Pi+rYTOm9lDitUj7Bhwq4gUi0g2oS7jC4hDXVIi3I0xQRP6qVkJbA7/1EkpIvIJoMMYkypjX0xmqzFmPaERQb8hIrcmukDT5ADWAz8zxtwEDJICP/evJXyy4b3Afya6LNMRbn++D1gMzAdyRORPE1uq6THGnAT+AXgdeBU4AgTise2UCPcRxpheoBrYAbSLSDlA+LIjgUWLxFbgXhG5AOwE7hCRfyf16gGAMaYlfNlBqF13M6lZlyagKfxrEOAZQmGfinUZcQ9w0BjTHr6danW5E6gzxnQaY/zAb4GbSb16AGCM+RdjzHpjzK1AD3CWONQl6cNdROaKyJzw9SxCL/wpQkMefDm82peB5xNTwsgYY/7aGFNpjFlE6Cfz74wxf0qK1QNARHJEJG/kOqH20GOkYF2MMW1Ao4gsCy/6KKHhrFOuLmN8jktNMpB6dWkAPiQi2SIihF6Tk6RePQAQkdLwZRXwKUKvTczrkvRnqIrIGuBfCR0xtwFPG2O+LyLFwNNAFaE3w4PGmNgPkhwFIrIN+I4x5hOpWA8RuY7Q3jqEmjV+bYz5QSrWBUBE1gG/ADKA84SGz7CRmnXJJnQw8jpjTF94Wcq9LuEuzw8RasI4BDwM5JJi9QAQkT8CxYAf+N+NMW/G4zVJ+nBXSik1dUnfLKOUUmrqNNyVUioNabgrpVQa0nBXSqk0pOGulFJpKJIJspWKq3A3sTfDN+cBQUJDBABsNsb4ElKwaxCR/wl4OdxvXqmE066QKqmJyKPAgDHmJ0lQFrsxJniV+/YC3zTGHJ7C8zmMMXE5FV3NPtoso1KKiHxZQuP7HxaRx0TEJiIOEekVkR+LyEEReU1EtojIH0TkvIh8LPzYh0VkV/j+0yLytxE+79+LyAeExjX6OxHZFx6f+3EJeYjQcNRPhR+fISJNY86s/pCIvBG+/vci8nMReZ3QYGUOEfnv4W3XiMjD8f+vqnSk4a5SRnjAuPuBm8MDyTkIDeUAUADsCQ9m5gMeJXTa+oPA98c8zebwY9YDnxeRdRE870FjzGZjzLvAPxpjNgGrw/ftMMY8BRwGHjKh8dQnaza6CfikMeaLwCOEBpTbDGwiNAhb1XT+P0qNpW3uKpXcSSgA94eGHCGL0Kn2AMPGmNfD148SGiY2ICJHgUVjnuM1Y8xFABF5DriF0Ofgas/r49JQCwAfFZG/BFxACXAAeGWK9XjeGOMJX78LWCEiY79MlhI6JV2padNwV6lEgCeNMf/lsoUiDkIhPMIiNIPXyPWx7/PxB5nMJM87bMIHpsLjtvy/wHpjTLOI/D2hkJ9IgEu/jMevMziuTl83xryJUlGkzTIqlbwBfEZESiDUq2YaTRh3SWjO1GxCY4a/PYXnzSL0ZdEVHhXz02PucwN5Y25fIDTVHePWG+814OvhL5KReVCzplgnpa6ge+4qZRhjjoZHC3xDRGyERtn7GtAyhafZC/waWAL8aqR3SyTPa4zpFpF/JTS8cT3w/pi7/wfwCxEZJtSu/yjw/4lIG/DBNcrzc0IjAx4ONwl1EPrSUWpGtCukmjXCPVFWGWP+ItFlUSrWtFlGKaXSkO65K6VUGtI9d6WUSkMa7koplYY03JVSKg1puCulVBrScFdKqTT0/wM4uYux49PXMgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pred_res = logmodel.get_prediction(data_pred[['Intercept','Temperature']]).summary_frame(0.05)\n",
+    "\n",
+    "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
+    "plt.fill_between(x=data_pred['Temperature'], y1=pred_res['mean_ci_lower'], y2=pred_res['mean_ci_upper'], alpha=.5, label='Confidence interval')\n",
+    "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prendre en compte la multiplicité des joints\n",
+    "\n",
+    "Ce n'est pas normal que :\n",
+    "1. la probabilité de dysfonctionnement à basse température soit minorée à 0 et majorée à 1\n",
+    "2. cette probabilité augmente à hautes fréquences\n",
+    "\n",
+    "Donc formater les données pour dupliquer les résultats pour compter chaque joint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Generalized Linear Model Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>      <td>Frequency</td>    <th>  No. Observations:  </th>  <td>   138</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   136</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Link Function:</th>        <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -25.300</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>           <td>Thu, 06 Jan 2022</td> <th>  Deviance:          </th> <td>  50.600</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>               <td>18:20:28</td>     <th>  Pearson chi2:      </th>  <td>  136.</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Iterations:</th>         <td>6</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>   <td>    4.6900</td> <td>    3.380</td> <td>    1.388</td> <td> 0.165</td> <td>   -1.934</td> <td>   11.314</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Temperature</th> <td>   -0.1138</td> <td>    0.052</td> <td>   -2.179</td> <td> 0.029</td> <td>   -0.216</td> <td>   -0.011</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                 Generalized Linear Model Regression Results                  \n",
+       "==============================================================================\n",
+       "Dep. Variable:              Frequency   No. Observations:                  138\n",
+       "Model:                            GLM   Df Residuals:                      136\n",
+       "Model Family:                Binomial   Df Model:                            1\n",
+       "Link Function:                  logit   Scale:                          1.0000\n",
+       "Method:                          IRLS   Log-Likelihood:                -25.300\n",
+       "Date:                Thu, 06 Jan 2022   Deviance:                       50.600\n",
+       "Time:                        18:20:28   Pearson chi2:                     136.\n",
+       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
+       "===============================================================================\n",
+       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "-------------------------------------------------------------------------------\n",
+       "Intercept       4.6900      3.380      1.388      0.165      -1.934      11.314\n",
+       "Temperature    -0.1138      0.052     -2.179      0.029      -0.216      -0.011\n",
+       "===============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_flat = pd.DataFrame({'Temperature':data.Temperature, 'Malfunction':data.Malfunction, 'Count':data.Count, 'Date':data.Date})\n",
+    "\n",
+    "count = 0\n",
+    "for i in range(len(data.Count)):\n",
+    "    for j in range(data.at[i, 'Count']):\n",
+    "        data_flat.at[(i-1)*data.at[i, 'Count']+j,'Temperature']=data.at[i, 'Temperature']\n",
+    "        if (data.at[i, 'Malfunction']>0 and j==0):\n",
+    "            data_flat.at[(i-1)*data.at[i, 'Count']+j,'Malfunction']=1\n",
+    "        else:\n",
+    "            data_flat.at[(i-1)*data.at[i, 'Count']+j,'Malfunction']=0\n",
+    "        data_flat.at[(i-1)*data.at[i, 'Count']+j,'Count']=1\n",
+    "        data_flat.at[(i-1)*data.at[i, 'Count']+j,'Date']=data.at[i,'Date']\n",
+    "        count += 1\n",
+    "        \n",
+    "data_flat[\"Frequency\"]=data_flat.Malfunction/data_flat.Count\n",
+    "data_flat[\"Intercept\"]=1\n",
+    "\n",
+    "logmodel_flat = sm.GLM(data_flat['Frequency'], data_flat[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
+    "logmodel_flat.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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": [
+    "data_pred_flat = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
+    "data_pred_flat['Frequency'] = logmodel.predict(data_pred_flat[['Intercept','Temperature']])\n",
+    "\n",
+    "pred_res_flat = logmodel_flat.get_prediction(data_pred_flat[['Intercept','Temperature']]).summary_frame(0.05)\n",
+    "\n",
+    "data_pred_flat.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
+    "plt.fill_between(x=data_pred_flat['Temperature'], y1=pred_res_flat['mean_ci_lower'], y2=pred_res_flat['mean_ci_upper'], alpha=.5, label='Confidence interval')\n",
+    "plt.scatter(x=data_flat[\"Temperature\"],y=data_flat[\"Frequency\"])\n",
+    "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       mean  mean_se  mean_ci_lower  mean_ci_upper\n",
+      "0  0.781614  0.31284       0.089721       0.992364\n",
+      "Probabilité de défaillance d'un joint à 30°F = 0.7816142740083805\n",
+      "Probabilité de défaillance de deux joints à 30°F = 0.6109208733336476\n",
+      "Probabilité de défaillance d'un lanceur à 30°F = 0.9411002031140461\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_pred_flat_30 = pd.DataFrame({'Temperature': [30], 'Intercept': [1]})\n",
+    "data_pred_flat_30['Frequency'] = logmodel_flat.predict(data_pred_flat_30[['Intercept','Temperature']])\n",
+    "\n",
+    "\n",
+    "pred_res_flat_30 = logmodel_flat.get_prediction(data_pred_flat_30[['Intercept','Temperature']]).summary_frame(0.05)\n",
+    "\n",
+    "p = data_pred_flat_30.at[0,'Frequency']\n",
+    "print(\"Probabilité de défaillance d'un joint à 30°F = {}\".format(p))\n",
+    "print(\"Probabilité de défaillance de deux joints à 30°F = {}\".format(pow(p,2)))\n",
+    "print(\"Probabilité de défaillance d'un lanceur à 30°F = {}\".format(1-pow(1-pow(p,2),3)))"
+   ]
   }
  ],
  "metadata": {
@@ -705,7 +885,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb
index 59d72b5b58a3ae26346460dd39e62a39c55243d7..6f5ec0a5d9e437196a6f35104c7eec11e0e8b982 100644
--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
@@ -37,6 +37,19 @@
     "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"syndrome-grippal.csv\"\n",
+    "\n",
+    "import os\n",
+    "import urllib.request\n",
+    "urllib.request.urlretrieve(data_url, data_file)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -364,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,