diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb
index 26ad6d94fa840f788a57621b06dc6af83a848391..7d6ca1565f8fd3755598b296b5390e3f362e9957 100644
--- a/module2/exo5/exo5_fr.ipynb
+++ b/module2/exo5/exo5_fr.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -261,33 +261,33 @@
        "</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"
+       "        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,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -322,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +425,7 @@
        "22   1/12/86      6           58       200            1"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -448,12 +448,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 16,
    "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 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -524,10 +524,10 @@
        "  <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>Wed, 25 Mar 2020</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>18:06:02</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 +555,8 @@
        "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:                Wed, 25 Mar 2020   Deviance:                      0.22231\n",
+       "Time:                        18:06:02   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 +567,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -605,12 +605,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 18,
    "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>"
       ]
@@ -648,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -686,6 +686,618 @@
     "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": {},
+   "source": [
+    "### réponse \n",
+    "\n",
+    "La régression a été effectué sur le jeu de données complet (pression = 50 et 200 bar).\n",
+    "\n",
+    "### hyp. 1\n",
+    "Il faut retirer la données correspondant à la pression de 50 bars pour voire l'influence de la température.\n",
+    "\n",
+    "ccl = ce n'est pas la pression qui pose problème."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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>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>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>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>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",
+       "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",
+       "13   1/24/85      6           53       200            2\n",
+       "20  10/30/85      6           75       200            2\n",
+       "22   1/12/86      6           58       200            1"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"shuttle.csv\")\n",
+    "data = data[data.Malfunction>0]\n",
+    "data = data[data.Pressure>50]\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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[\"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": [
+    "### hyp. 2\n",
+    "La fréquence de malfonction doit être calculée sur l'ensemble des données (pas de filtre sur data.malfunction) car on masque tous les test pour lesquelles on n'as pas eu de pb.\n",
+    "\n",
+    "La fréquence doit être calculée comme la somme des malfunction / somme des counts pour une température donnée. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "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": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"shuttle.csv\")\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data = data[data.Pressure == 200]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "nb_count = []\n",
+    "nb_malfunction = []\n",
+    "\n",
+    "unique_temp = data.Temperature.unique()\n",
+    "for temp in unique_temp:\n",
+    "    nb_count.append(data['Count'][data['Temperature'] == temp].sum())\n",
+    "    nb_malfunction.append(data['Malfunction'][data['Temperature'] == temp].sum())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_data = pd.DataFrame()\n",
+    "df_data['Temperature'] = unique_temp\n",
+    "df_data['Count'] = nb_count\n",
+    "df_data['Malfunction'] = nb_malfunction\n",
+    "df_data['Frequency'] = df_data['Malfunction']/df_data['Count']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "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": [
+    "df_data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "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>    16</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>    14</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> -2.4880</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>           <td>Wed, 25 Mar 2020</td> <th>  Deviance:          </th> <td>  1.1965</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>               <td>18:42:18</td>     <th>  Pearson chi2:      </th>  <td>  1.82</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>    6.8667</td> <td>    8.822</td> <td>    0.778</td> <td> 0.436</td> <td>  -10.424</td> <td>   24.157</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Temperature</th> <td>   -0.1458</td> <td>    0.143</td> <td>   -1.023</td> <td> 0.306</td> <td>   -0.425</td> <td>    0.134</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:                   16\n",
+       "Model:                            GLM   Df Residuals:                       14\n",
+       "Model Family:                Binomial   Df Model:                            1\n",
+       "Link Function:                  logit   Scale:                          1.0000\n",
+       "Method:                          IRLS   Log-Likelihood:                -2.4880\n",
+       "Date:                Wed, 25 Mar 2020   Deviance:                       1.1965\n",
+       "Time:                        18:42:18   Pearson chi2:                     1.82\n",
+       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
+       "===============================================================================\n",
+       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "-------------------------------------------------------------------------------\n",
+       "Intercept       6.8667      8.822      0.778      0.436     -10.424      24.157\n",
+       "Temperature    -0.1458      0.143     -1.023      0.306      -0.425       0.134\n",
+       "===============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "\n",
+    "df_data[\"Success\"]=df_data.Count-df_data.Malfunction\n",
+    "df_data[\"Intercept\"]=1\n",
+    "\n",
+    "logmodel=sm.GLM(df_data['Frequency'], df_data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
+    "\n",
+    "logmodel.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "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",
+    "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",
+    "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
+    "plt.scatter(x=df_data[\"Temperature\"],y=df_data[\"Frequency\"])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probabilité de défaillance d'un joint:  0.9126150661497261\n",
+      "Probabilité de défaillance de deux joints:  0.8328662589634689\n",
+      "Probabilité de défaillance d'un lanceur sur trois:  0.9953313383250852\n"
+     ]
+    }
+   ],
+   "source": [
+    "proba = logmodel.predict([1, 31])\n",
+    "print(\"Probabilité de défaillance d'un joint: \", str(proba[0]))\n",
+    "print(\"Probabilité de défaillance de deux joints: \", str(proba[0]**2))\n",
+    "proba3 = 1.-(1-proba**2)**3\n",
+    "print(\"Probabilité de défaillance d'un lanceur sur trois: \", str(proba3[0]))"
+   ]
   }
  ],
  "metadata": {
@@ -705,7 +1317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,