diff --git a/module4/Analyse Challenger.ipynb b/module4/Analyse Challenger.ipynb
index e461715080f9078ce1faedcaeba5ff35ea01464c..fbbc3a5a90aebb126444305e8113112b870ec5f4 100644
--- a/module4/Analyse Challenger.ipynb	
+++ b/module4/Analyse Challenger.ipynb	
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -320,7 +320,7 @@
        "  <th>Date:</th>           <td>Thu, 24 Sep 2020</td> <th>  Deviance:          </th> <td>  18.086</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>               <td>12:33:06</td>     <th>  Pearson chi2:      </th>  <td>  30.0</td>  \n",
+       "  <th>Time:</th>               <td>12:55:04</td>     <th>  Pearson chi2:      </th>  <td>  30.0</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Iterations:</th>         <td>6</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
@@ -349,7 +349,7 @@
        "Link Function:                  logit   Scale:                          1.0000\n",
        "Method:                          IRLS   Log-Likelihood:                -23.526\n",
        "Date:                Thu, 24 Sep 2020   Deviance:                       18.086\n",
-       "Time:                        12:33:06   Pearson chi2:                     30.0\n",
+       "Time:                        12:55:04   Pearson chi2:                     30.0\n",
        "No. Iterations:                     6   Covariance Type:             nonrobust\n",
        "===============================================================================\n",
        "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
@@ -360,7 +360,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -405,66 +405,66 @@
      "text": [
       "    Intercept  Temperature  Occurence\n",
       "0           1         30.0   0.834373\n",
-      "1           1         31.0   0.817482\n",
-      "2           1         32.0   0.799283\n",
-      "3           1         33.0   0.779759\n",
-      "4           1         34.0   0.758908\n",
-      "5           1         35.0   0.736749\n",
-      "6           1         36.0   0.713323\n",
-      "7           1         37.0   0.688694\n",
-      "8           1         38.0   0.662948\n",
-      "9           1         39.0   0.636197\n",
-      "10          1         40.0   0.608578\n",
-      "11          1         41.0   0.580244\n",
-      "12          1         42.0   0.551372\n",
-      "13          1         43.0   0.522149\n",
-      "14          1         44.0   0.492774\n",
-      "15          1         45.0   0.463449\n",
-      "16          1         46.0   0.434374\n",
-      "17          1         47.0   0.405744\n",
-      "18          1         48.0   0.377741\n",
-      "19          1         49.0   0.350531\n",
-      "20          1         50.0   0.324259\n",
-      "21          1         51.0   0.299049\n",
-      "22          1         52.0   0.275002\n",
-      "23          1         53.0   0.252193\n",
-      "24          1         54.0   0.230674\n",
-      "25          1         55.0   0.210474\n",
-      "26          1         56.0   0.191602\n",
-      "27          1         57.0   0.174050\n",
-      "28          1         58.0   0.157792\n",
-      "29          1         59.0   0.142789\n",
+      "1           1         31.0   0.817774\n",
+      "2           1         32.0   0.799911\n",
+      "3           1         33.0   0.780766\n",
+      "4           1         34.0   0.760339\n",
+      "5           1         35.0   0.738645\n",
+      "6           1         36.0   0.715721\n",
+      "7           1         37.0   0.691626\n",
+      "8           1         38.0   0.666441\n",
+      "9           1         39.0   0.640269\n",
+      "10          1         40.0   0.613235\n",
+      "11          1         41.0   0.585485\n",
+      "12          1         42.0   0.557181\n",
+      "13          1         43.0   0.528501\n",
+      "14          1         44.0   0.499631\n",
+      "15          1         45.0   0.470765\n",
+      "16          1         46.0   0.442092\n",
+      "17          1         47.0   0.413800\n",
+      "18          1         48.0   0.386066\n",
+      "19          1         49.0   0.359052\n",
+      "20          1         50.0   0.332904\n",
+      "21          1         51.0   0.307745\n",
+      "22          1         52.0   0.283679\n",
+      "23          1         53.0   0.260787\n",
+      "24          1         54.0   0.239124\n",
+      "25          1         55.0   0.218729\n",
+      "26          1         56.0   0.199617\n",
+      "27          1         57.0   0.181787\n",
+      "28          1         58.0   0.165220\n",
+      "29          1         59.0   0.149886\n",
       "..        ...          ...        ...\n",
-      "31          1         61.0   0.116353\n",
-      "32          1         62.0   0.104800\n",
-      "33          1         63.0   0.094272\n",
-      "34          1         64.0   0.084702\n",
-      "35          1         65.0   0.076021\n",
-      "36          1         66.0   0.068164\n",
-      "37          1         67.0   0.061066\n",
-      "38          1         68.0   0.054663\n",
-      "39          1         69.0   0.048896\n",
-      "40          1         70.0   0.043710\n",
-      "41          1         71.0   0.039052\n",
-      "42          1         72.0   0.034871\n",
-      "43          1         73.0   0.031124\n",
-      "44          1         74.0   0.027768\n",
-      "45          1         75.0   0.024764\n",
-      "46          1         76.0   0.022078\n",
-      "47          1         77.0   0.019678\n",
-      "48          1         78.0   0.017533\n",
-      "49          1         79.0   0.015619\n",
-      "50          1         80.0   0.013911\n",
-      "51          1         81.0   0.012387\n",
-      "52          1         82.0   0.011028\n",
-      "53          1         83.0   0.009817\n",
-      "54          1         84.0   0.008738\n",
-      "55          1         85.0   0.007776\n",
-      "56          1         86.0   0.006920\n",
-      "57          1         87.0   0.006157\n",
-      "58          1         88.0   0.005478\n",
-      "59          1         89.0   0.004873\n",
-      "60          1         90.0        NaN\n",
+      "31          1         61.0   0.122744\n",
+      "32          1         62.0   0.110830\n",
+      "33          1         63.0   0.099940\n",
+      "34          1         64.0   0.090011\n",
+      "35          1         65.0   0.080981\n",
+      "36          1         66.0   0.072783\n",
+      "37          1         67.0   0.065357\n",
+      "38          1         68.0   0.058640\n",
+      "39          1         69.0   0.052575\n",
+      "40          1         70.0   0.047106\n",
+      "41          1         71.0   0.042180\n",
+      "42          1         72.0   0.037749\n",
+      "43          1         73.0   0.033767\n",
+      "44          1         74.0   0.030192\n",
+      "45          1         75.0   0.026985\n",
+      "46          1         76.0   0.024110\n",
+      "47          1         77.0   0.021535\n",
+      "48          1         78.0   0.019229\n",
+      "49          1         79.0   0.017166\n",
+      "50          1         80.0   0.015321\n",
+      "51          1         81.0   0.013671\n",
+      "52          1         82.0   0.012197\n",
+      "53          1         83.0   0.010880\n",
+      "54          1         84.0   0.009703\n",
+      "55          1         85.0   0.008653\n",
+      "56          1         86.0   0.007716\n",
+      "57          1         87.0   0.006879\n",
+      "58          1         88.0   0.006133\n",
+      "59          1         89.0   0.005467\n",
+      "60          1         90.0   0.004873\n",
       "\n",
       "[61 rows x 3 columns]\n"
      ]
@@ -473,7 +473,7 @@
    "source": [
     "dLogistic = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=61), 'Intercept': 1})\n",
     "sm.add_constant(dLogistic)\n",
-    "dLogistic['Occurence'] = LogisticModel.predict(X)\n",
+    "dLogistic['Occurence'] = LogisticModel.predict(dLogistic)\n",
     "print(dLogistic)"
    ]
   },
@@ -484,7 +484,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -508,6 +508,59 @@
     "Bien que l'utilisation d'un tel modèle pour prédire la probabilité d'un accident soit hautement discutable (trop peu de points et plusieurs points pathologiques), il en résulte sûrement un résultat surestimé. Ainsi, pour une valeur de $T = 31$ °F, la probabilité d'accident serait très importante."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Zone de confiance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous cherchons maintenant à évaluer l'incertitude pour le modèle logistique en fonction de la valeur en température envisagée."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sn.set(color_codes=True)\n",
+    "plt.xlim(30,90)\n",
+    "plt.ylim(0,1)\n",
+    "sn.regplot(x='Temperature', y='Occurence', data=d, logistic=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il apparaît ainsi que malgré l'incertitude, il y ait au moins une probabilité de 10% d'accident."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,