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Commit 905f21cb authored by d99396234d3f418896693d0c54c46d91's avatar d99396234d3f418896693d0c54c46d91
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Added Varicella incidence study

parent f6772dae
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Showing with 3543 additions and 48 deletions
module2/exo5/exo5_fr.ipynb
View file @ 905f21cb
...@@ -261,30 +261,30 @@ ...@@ -261,30 +261,30 @@
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" Date Count Temperature Pressure Malfunction\n", " Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n", "0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n", "1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n", "2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n", "3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n", "4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n", "5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n", "6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n", "7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n", "8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n", "9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n", "10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n", "11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n", "12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n", "13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n", "14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n", "15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n", "16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n", "17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n", "18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n", "19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n", "20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n", "21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 1, "execution_count": 1,
...@@ -355,6 +355,14 @@ ...@@ -355,6 +355,14 @@
" </thead>\n", " </thead>\n",
" <tbody>\n", " <tbody>\n",
" <tr>\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", " <th>1</th>\n",
" <td>11/12/81</td>\n", " <td>11/12/81</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -363,6 +371,54 @@ ...@@ -363,6 +371,54 @@
" <td>1</td>\n", " <td>1</td>\n",
" </tr>\n", " </tr>\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", " <th>8</th>\n",
" <td>2/03/84</td>\n", " <td>2/03/84</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -387,6 +443,22 @@ ...@@ -387,6 +443,22 @@
" <td>1</td>\n", " <td>1</td>\n",
" </tr>\n", " </tr>\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", " <th>13</th>\n",
" <td>1/24/85</td>\n", " <td>1/24/85</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -395,6 +467,54 @@ ...@@ -395,6 +467,54 @@
" <td>2</td>\n", " <td>2</td>\n",
" </tr>\n", " </tr>\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", " <th>20</th>\n",
" <td>10/30/85</td>\n", " <td>10/30/85</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -403,6 +523,14 @@ ...@@ -403,6 +523,14 @@
" <td>2</td>\n", " <td>2</td>\n",
" </tr>\n", " </tr>\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", " <th>22</th>\n",
" <td>1/12/86</td>\n", " <td>1/12/86</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -416,12 +544,28 @@ ...@@ -416,12 +544,28 @@
], ],
"text/plain": [ "text/plain": [
" Date Count Temperature Pressure Malfunction\n", " Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\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", "8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n", "9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 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", "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", "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" "22 1/12/86 6 58 200 1"
] ]
}, },
...@@ -431,7 +575,7 @@ ...@@ -431,7 +575,7 @@
} }
], ],
"source": [ "source": [
"data = data[data.Malfunction>0]\n", "data = data[data.Malfunction>=0]\n",
"data" "data"
] ]
}, },
...@@ -453,7 +597,7 @@ ...@@ -453,7 +597,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
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\n", 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -469,7 +613,7 @@ ...@@ -469,7 +613,7 @@
"pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n", "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"\n", "\n",
"data[\"Frequency\"]=data.Malfunction/data.Count\n", "data[\"Frequency\"]= [(1 if d>0 else 0) for d in data.Malfunction] #data.Malfunction/data.Count\n",
"data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)" "plt.grid(True)"
] ]
...@@ -509,10 +653,10 @@ ...@@ -509,10 +653,10 @@
"<table class=\"simpletable\">\n", "<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n", "<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n", "<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 7</td> \n", " <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 23</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 5</td> \n", " <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 21</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n", " <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
...@@ -521,16 +665,16 @@ ...@@ -521,16 +665,16 @@
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n", " <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5250</td> \n", " <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -10.158</td> \n",
"</tr>\n", "</tr>\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>Mon, 10 May 2021</td> <th> Deviance: </th> <td> 20.315</td> \n",
"</tr>\n", "</tr>\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>12:48:12</td> <th> Pearson chi2: </th> <td> 23.2</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n", " <th>No. Iterations:</th> <td>5</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n", "</tr>\n",
"</table>\n", "</table>\n",
"<table class=\"simpletable\">\n", "<table class=\"simpletable\">\n",
...@@ -538,10 +682,10 @@ ...@@ -538,10 +682,10 @@
" <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", " <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",
"<tr>\n", "<tr>\n",
" <th>Intercept</th> <td> -1.3895</td> <td> 7.828</td> <td> -0.178</td> <td> 0.859</td> <td> -16.732</td> <td> 13.953</td>\n", " <th>Intercept</th> <td> 15.0429</td> <td> 7.379</td> <td> 2.039</td> <td> 0.041</td> <td> 0.581</td> <td> 29.505</td>\n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Temperature</th> <td> 0.0014</td> <td> 0.122</td> <td> 0.012</td> <td> 0.991</td> <td> -0.238</td> <td> 0.240</td>\n", " <th>Temperature</th> <td> -0.2322</td> <td> 0.108</td> <td> -2.145</td> <td> 0.032</td> <td> -0.444</td> <td> -0.020</td>\n",
"</tr>\n", "</tr>\n",
"</table>" "</table>"
], ],
...@@ -550,19 +694,19 @@ ...@@ -550,19 +694,19 @@
"\"\"\"\n", "\"\"\"\n",
" Generalized Linear Model Regression Results \n", " Generalized Linear Model Regression Results \n",
"==============================================================================\n", "==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 7\n", "Dep. Variable: Frequency No. Observations: 23\n",
"Model: GLM Df Residuals: 5\n", "Model: GLM Df Residuals: 21\n",
"Model Family: Binomial Df Model: 1\n", "Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n", "Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -2.5250\n", "Method: IRLS Log-Likelihood: -10.158\n",
"Date: Sat, 13 Apr 2019 Deviance: 0.22231\n", "Date: Mon, 10 May 2021 Deviance: 20.315\n",
"Time: 19:11:24 Pearson chi2: 0.236\n", "Time: 12:48:12 Pearson chi2: 23.2\n",
"No. Iterations: 4 Covariance Type: nonrobust\n", "No. Iterations: 5 Covariance Type: nonrobust\n",
"===============================================================================\n", "===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n", " coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n", "-------------------------------------------------------------------------------\n",
"Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953\n", "Intercept 15.0429 7.379 2.039 0.041 0.581 29.505\n",
"Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n", "Temperature -0.2322 0.108 -2.145 0.032 -0.444 -0.020\n",
"===============================================================================\n", "===============================================================================\n",
"\"\"\"" "\"\"\""
] ]
...@@ -610,7 +754,7 @@ ...@@ -610,7 +754,7 @@
"outputs": [ "outputs": [
{ {
"data": { "data": {
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\n", 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -705,7 +849,7 @@ ...@@ -705,7 +849,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
module3/exo2/exercice.ipynb
View file @ 905f21cb
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