diff --git a/module2/exo5/exo5_en.ipynb b/module2/exo5/exo5_en.ipynb
index 6a0919d7ba4c9594341883f6d283db9ea050de72..70f613c8f7ea4dc6f4966a4371c19ea0e73f4ed8 100644
--- a/module2/exo5/exo5_en.ipynb
+++ b/module2/exo5/exo5_en.ipynb
@@ -260,30 +260,30 @@
        "</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,
@@ -449,7 +449,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -519,10 +519,10 @@
        "  <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, 27 Sep 2023</td> <th>  Deviance:          </th> <td> 0.22231</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>               <td>19:12:05</td>     <th>  Pearson chi2:      </th>  <td> 0.236</td>  \n",
+       "  <th>Time:</th>               <td>22:48:16</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",
@@ -550,8 +550,8 @@
        "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:                        19:12:05   Pearson chi2:                    0.236\n",
+       "Date:                Wed, 27 Sep 2023   Deviance:                      0.22231\n",
+       "Time:                        22:48:16   Pearson chi2:                    0.236\n",
        "No. Iterations:                     4   Covariance Type:             nonrobust\n",
        "===============================================================================\n",
        "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
@@ -606,7 +606,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -681,6 +681,261 @@
     "from all angles in order to to explain what's wrong."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Problem: the above analysis omitted a possible confounding variable (Pressure)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see in the examples below that 1) pressure and temperature are related, and 2) very low or very high pressure may increase frequency of malfunction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count     23.000000\n",
+       "mean     152.173913\n",
+       "std       68.221332\n",
+       "min       50.000000\n",
+       "25%       75.000000\n",
+       "50%      200.000000\n",
+       "75%      200.000000\n",
+       "max      200.000000\n",
+       "Name: Pressure, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Pressure'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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.plot(x=\"Temperature\",y=\"Pressure\",kind=\"scatter\")\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+8dUOJZKk9t26cu2QxrfXDl8Kk/Ycx6YtW7YZ27RlC5P2HNehRJLUvhOndg1pfHvt8KWw14SduejTh7LL2FFM3HkMu4wdxUWfPtQ3myW9LRw/dV8O6dp1m7FDunat7c3md8QbzScfvh8fO3hvjz6S9LZ0y3nHFUcfrfox3z7jIx59NBz2mrCzZSDpbev4qfvyg2fHcVyNhQDvgN1HkqT2WQqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpEqtpRAR0yNiVUT0RMTcPtZHRHy1XL80Io6oK8v19z/J2Vfdw/X3P1nXQ+gt6Fm7jhc3bKJn7bpOR5Ea7cKblvPwM+u48KbltT5ObaUQEaOBy4AZwFTgtIiY2mvaDGBKeZkNfKOOLMf85SLOvXoptz30LOdevZRj/3JRHQ+jIbrg+mWccMkdrH5xAydccgcX3LCs05GkRjpo7s1860c/Y9MbW/jWj37GQXNvru2x6txSOAroycxHM3MjsACY2WvOTGB+FpYAe0TEe4YzxPX3P8kzL2/cZuzplze6xdBhPWvXMX/JE9uMzb/zCbcYpF4uvGk5W3qNbSnH6xCZWc8dR8wCpmfm2eXy6cDRmTmnZc73gb/KzP9XLt8OnJ+Z9/a6r9kUWxIAhwCr2s0xZs/3vm/UzuP36D2+5fUNv9j84pqfDvHH6svewPPDcD91aWS+UeN332vMbvscAPDGhpcYPX53ADa//NzjWza89EIns/Whkb/DXsy4/RqZb+w+B3w4Ro/ZCbZ9ruQbmzdueu7xoWxe/1Jm7jPYpDFvLWZboo+x3g3Uzhwy85vAN4cj1HCLiHsz88hO5+hP0/NBkXHzS882NuPb5Xdoxu3T9HwwMs+VOncfrQYmtyxPAta8hTmSpBFSZyncA0yJiAMjYifgVODGXnNuBM4oj0I6BngpM5+uMZMkaQC17T7KzM0RMQe4BRgNXJGZKyLinHL9PGAhcBLQA2wAzqorT40auVurRdPzQfMzNj0fmHE4ND0fjEDG2t5oliS9/fiJZklSxVKQJFUshSGIiD0i4nsR8XBEPBQRx0bEuyJiUUT8pPzvnh3OeF5ErIiI5RHx3YjYpZMZI+KKiHg2Ipa3jPWbJyI+X572ZFVE/FoHM15c/n9eGhH/HBF7tKxrRMaWdZ+LiIyIvTuVsb98EfEHZYYVEXFRp/L1lzEiDo+IJRHxQETcGxFHdSpjREyOiMXla8uKiPif5fjIPl8y00ubF+Aq4Ozy+k7AHsBFwNxybC7wpQ7m2w94DBhXLl8NfKaTGYH/ABwBLG8Z6zMPxelQHgR2Bg4EfgqM7lDGE4Ex5fUvNTFjOT6Z4mCOnwF7dypjP7/DacBtwM7l8rub9jsEbgVmlNdPAn7Qwd/he4AjyusTgUfKHCP6fHFLoU0RsRvFH9W3ATJzY2b+guJUHVeV064CfrMzCStjgHERMQYYT/G5j45lzMw7gJ/3Gu4vz0xgQWa+npmPURyVdhQ16ytjZt6amZvLxSUUn6FpVMbSJcAfs+2HPkc8Yz/5/hvFGQteL+c826l8A2RMYLfy+u68+TmpTvwOn87M+8vr64CHKP6hN6LPF0uhfQcBzwF/HxE/jojLI2JXoCvLz1aU/313pwJm5lPAl4EngKcpPvdxa5MylvrLsx/QelKq1eVYp/0e8C/l9cZkjIiTgacy88Feq5qS8f3AJyLiroj4YUR8tBxvSj6Ac4GLI+JJiufO58vxjmaMiAOAjwB3McLPF0uhfWMoNj2/kZkfAV6h2JRrjHJf40yKTcn3ArtGxH/ubKohaeu0JyMpIr4AbAb+cetQH9NGPGNEjAe+AFzQ1+o+xjrxexwD7AkcA/wRcHVEBM3JB8XWzHmZORk4j3JPAB3MGBETgGuBczPz5YGm9jG23RkthfatBlZn5l3l8vcoSmJtlGd2Lf/7bD+3HwknAI9l5nOZuQm4DvjlhmVkgDyNOu1JRJwJ/Efgd7PciUtzMr6PovwfjIjHyxz3R8S+NCfjauC6LNxNcXLPvRuUD+BMiucJwDW8ufulIxkjYixFIfxjZm7NNaLPF0uhTZn5DPBkRBxSDh0PrKQ4VceZ5diZwA0diLfVE8AxETG+/BfZ8RT7JZuUEfrPcyNwakTsHBEHUnzPxt0dyEdETAfOB07OzA0tqxqRMTOXZea7M/OAzDyA4gXiiPLvtBEZgeuBTwJExPspDs54vkH5oHgR/ZXy+ieBn5TXRzxj+Zz9NvBQZv5Ny6qRfb7U+W76jnYBDgfuBZZS/MHvCewF3E7xx3Q78K4OZ/wz4GFgOfAdiiMTOpYR+C7F+xubKF64/stAeSh2ifyU4vToMzqYsYdif+0D5WVe0zL2Wv845dFHncjYz+9wJ+Afyr/F+4FPNu13CHwcuI/iKJ67gO4O/g4/TrH7Z2nL391JI/188TQXkqSKu48kSRVLQZJUsRQkSRVLQZJUsRQkSZXavnlNarqIeANYRvE8eAg4M7f9TIL0juOWgt7JXs3MwzPzQ8BG4JzWlVEYsedIRIweqceS+mMpSIV/BQ6OiAPK89l/neIDV5Mj4sSIuDMi7o+Ia8pz0xARfxURK8vvXPhyOXZKFN9l8WBE3FGOfSYivrb1gSLi+xFxXHl9fUT8eUTcBRwbEd3lyePui4hbtp7eQBoploLe8crTjM+g2JUEcAgwP9888eEXgRMy8wiKT7T/YUS8C/gU8MHMPBT4P+VtLwB+LTMPA05u4+F3pTi//9EUn6i9FJiVmd3AFcCFw/EzSu3yPQW9k42LiAfK6/9Kcd6Z9wI/y8wl5fgxFF9m8qPi1DTsBNwJvAy8BlweETcD3y/n/wi4MiKu5s0TrQ3kDYoToEFRRh8CFpWPNZritAzSiLEU9E72amYe3jpQvhi/0joELMrM03rfuPzqxuOBU4E5FOf2OScijgZ+HXggIg6nOPV261b5Li3XX8vMN1oea0VmHrt9P5b01rn7SBrYEuBjEXEwFN9jEBHvL99X2D0zF1J8Ucvh5fr3ZeZdmXkBxRlBJ1OcrO7wiBgVEZPp/9uxVgH7RMSx5X2NjYgP1vnDSb25pSANIDOfi4jPAN+NiJ3L4S8C64AbImIXin/hn1euuzgippRjt1OcfROK785exptnDO3rsTZGxCzgqxGxO8Xz8yvAimH/waR+eJZUSVLF3UeSpIqlIEmqWAqSpIqlIEmqWAqSpIqlIEmqWAqSpMr/B7ZTo8X/XbLnAAAAAElFTkSuQmCC\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",
+    "\n",
+    "data.plot(x=\"Pressure\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us redo the analysis by adjusting for the Pressure variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>    20</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>     2</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> -3.7926</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>           <td>Wed, 27 Sep 2023</td> <th>  Deviance:          </th> <td>  2.7576</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>               <td>22:48:16</td>     <th>  Pearson chi2:      </th>  <td>  4.19</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>    2.5202</td> <td>    8.541</td> <td>    0.295</td> <td> 0.768</td> <td>  -14.220</td> <td>   19.260</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Temperature</th> <td>   -0.0983</td> <td>    0.110</td> <td>   -0.894</td> <td> 0.371</td> <td>   -0.314</td> <td>    0.117</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Pressure</th>    <td>    0.0085</td> <td>    0.019</td> <td>    0.451</td> <td> 0.652</td> <td>   -0.028</td> <td>    0.045</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:                       20\n",
+       "Model Family:                Binomial   Df Model:                            2\n",
+       "Link Function:                  logit   Scale:                          1.0000\n",
+       "Method:                          IRLS   Log-Likelihood:                -3.7926\n",
+       "Date:                Wed, 27 Sep 2023   Deviance:                       2.7576\n",
+       "Time:                        22:48:16   Pearson chi2:                     4.19\n",
+       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
+       "===============================================================================\n",
+       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "-------------------------------------------------------------------------------\n",
+       "Intercept       2.5202      8.541      0.295      0.768     -14.220      19.260\n",
+       "Temperature    -0.0983      0.110     -0.894      0.371      -0.314       0.117\n",
+       "Pressure        0.0085      0.019      0.451      0.652      -0.028       0.045\n",
+       "===============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 10,
+     "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",
+    "\n",
+    "logmodel=sm.GLM(data['Frequency'], data[['Intercept', 'Temperature', 'Pressure']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
+    "\n",
+    "logmodel.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the sign of the temperature effect, although still not significant (likely due to the small number of data), is reversed compared to the non-adjusted analysis. Now, low temperature are associated with increased frequencies of malfunctions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "data_pred_50 = pd.DataFrame(\n",
+    "    {\n",
+    "        'Temperature': np.linspace(start=30, stop=90, num=121), \n",
+    "        'Pressure': 50, \n",
+    "        'Intercept': 1\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "data_pred_200 = pd.DataFrame(\n",
+    "    {\n",
+    "        'Temperature': np.linspace(start=30, stop=90, num=121), \n",
+    "        'Pressure': 200, \n",
+    "        'Intercept': 1\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "data_pred_50['Frequency'] = logmodel.predict(data_pred_50[['Intercept', 'Temperature', 'Pressure']])\n",
+    "data_pred_200['Frequency'] = logmodel.predict(data_pred_200[['Intercept', 'Temperature', 'Pressure']])\n",
+    "\n",
+    "\n",
+    "import seaborn as sns\n",
+    "\n",
+    "with sns.axes_style('darkgrid'):\n",
+    "    fig, ax = plt.subplots(figsize=(7, 5), nrows=1, ncols=1)\n",
+    "\n",
+    "\n",
+    "    data_pred_50.plot(x=\"Temperature\", y=\"Frequency\", kind=\"line\", ylim=[0,1], ax=ax, label='Pressure=50')\n",
+    "    data_pred_200.plot(x=\"Temperature\", y=\"Frequency\", kind=\"line\", ylim=[0,1], ax=ax, label='Pressure=200')\n",
+    "    ax.scatter(x=data[\"Temperature\"], y=data[\"Frequency\"])\n",
+    "    \n",
+    "    ax.set_ylabel(\"Frequency\")\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conclusion: by adjusting for a possible confounder, the malfunction frequency is predicted to be much higher than in the non-adjusted model (between 0.5 and 0.8 depending on the pressure, compared to 0.2 in the naive analysis)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -706,7 +961,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,