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40b4b9646ff373fd7a7b82d082e9f49d
mooc-rr
Commits
30e70174
Commit
30e70174
authored
Jan 28, 2024
by
40b4b9646ff373fd7a7b82d082e9f49d
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calcul challenger mis à jour
parent
1e3f2d58
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exo5_fr.ipynb
module2/exo5/exo5_fr.ipynb
+89
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module2/exo5/exo5_fr.ipynb
View file @
30e70174
...
@@ -40,7 +40,7 @@
...
@@ -40,7 +40,7 @@
},
},
{
{
"cell_type": "code",
"cell_type": "code",
"execution_count": 1,
"execution_count": 1
2
,
"metadata": {},
"metadata": {},
"outputs": [
"outputs": [
{
{
...
@@ -261,33 +261,33 @@
...
@@ -261,33 +261,33 @@
"</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
2
,
"metadata": {},
"metadata": {},
"output_type": "execute_result"
"output_type": "execute_result"
}
}
...
@@ -314,15 +314,15 @@
...
@@ -314,15 +314,15 @@
"metadata": {},
"metadata": {},
"source": [
"source": [
"## Inspection graphique des données\n",
"## Inspection graphique des données\n",
"
Les vols où aucun incident n'est relevé n'apporta
nt aucun information\n",
"
On pourrait penser que les vols où aucun incident n'est relevé n'apporte
nt aucun information\n",
"sur l'influence de la température ou de la pression sur les\n",
"sur l'influence de la température ou de la pression sur les\n",
"dysfonctionnements,
nous nous concentrons
sur les expériences où au\n",
"dysfonctionnements,
et se concentrer
sur les expériences où au\n",
"moins un joint a été défectueux.\n"
"moins un joint a été défectueux.
C'est ce qui est montré dans le tableau ci-dessous.
\n"
]
]
},
},
{
{
"cell_type": "code",
"cell_type": "code",
"execution_count":
2
,
"execution_count":
13
,
"metadata": {},
"metadata": {},
"outputs": [
"outputs": [
{
{
...
@@ -425,35 +425,49 @@
...
@@ -425,35 +425,49 @@
"22 1/12/86 6 58 200 1"
"22 1/12/86 6 58 200 1"
]
]
},
},
"execution_count":
2
,
"execution_count":
13
,
"metadata": {},
"metadata": {},
"output_type": "execute_result"
"output_type": "execute_result"
}
}
],
],
"source": [
"source": [
"data = data[data.Malfunction>0]\n",
"data
1
= data[data.Malfunction>0]\n",
"data"
"data
1
"
]
]
},
},
{
{
"cell_type": "markdown",
"cell_type": "markdown",
"metadata": {},
"metadata": {},
"source": [
"source": [
"Très bien, nous avons une variabilité de température importante mais\n",
"Le tableau indique une variabilité de température importante et\n",
"la pression est quasiment toujours égale à 200, ce qui devrait\n",
"une pression quasiment toujours égale à 200, ce qui devrait\n",
"simplifier l'analyse.\n",
"simplifier l'analyse. La température serait-elle plus impactante \n",
"sur les dysfonctionnements ? \n",
"\n",
"\n",
"En réalité, il est nécessaire, si l'on veut obtenir la dépendance\n",
"réelle des dysfonctionnements sur les paramètres environementaux,\n",
"de prendre en compte tous les cas expérimentaux, notamment ceux\n",
"pour lesquels aucun dysfonctionnement n'apparaît. \n",
"C'est d'ailleurs absolument nécessaire si l'on veut pouvoir dire \n",
"quelque chose sur la probabilité qu'un dysfonctionnement \n",
"apparaisse : sans prendre en compte ces cas expérimentaux, on\n",
"quantifie simplement l'influence de la température sur la\n",
"probabilité conditionnelle qu'un dysfonctionnement arrive, sachant\n",
"qu'au moins un dysfonctionnement est survenu.\n",
"\n",
"Conservons cependant l'approche en fonction de la température,\n",
"mais sur l'ensemble des données expérimentales. \n",
"Comment la fréquence d'échecs varie-t-elle avec la température ?\n"
"Comment la fréquence d'échecs varie-t-elle avec la température ?\n"
]
]
},
},
{
{
"cell_type": "code",
"cell_type": "code",
"execution_count":
3
,
"execution_count":
14
,
"metadata": {},
"metadata": {},
"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>"
]
]
...
@@ -500,7 +514,7 @@
...
@@ -500,7 +514,7 @@
},
},
{
{
"cell_type": "code",
"cell_type": "code",
"execution_count":
4
,
"execution_count":
15
,
"metadata": {},
"metadata": {},
"outputs": [
"outputs": [
{
{
...
@@ -509,10 +523,10 @@
...
@@ -509,10 +523,10 @@
"<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 +535,16 @@
...
@@ -521,16 +535,16 @@
" <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> -
2.525
0</td> \n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -
3.921
0</td> \n",
"</tr>\n",
"</tr>\n",
"<tr>\n",
"<tr>\n",
" <th>Date:</th> <td>S
at, 13 Apr 2019</td> <th> Deviance: </th> <td> 0.22231
</td> \n",
" <th>Date:</th> <td>S
un, 28 Jan 2024</td> <th> Deviance: </th> <td> 3.0144
</td> \n",
"</tr>\n",
"</tr>\n",
"<tr>\n",
"<tr>\n",
" <th>Time:</th> <td>1
9:11:24</td> <th> Pearson chi2: </th> <td> 0.236
</td> \n",
" <th>Time:</th> <td>1
7:21:21</td> <th> Pearson chi2: </th> <td> 5.00
</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>
6
</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 +552,10 @@
...
@@ -538,10 +552,10 @@
" <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>
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",
"<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> 0.24
0</td>\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> 0.11
0</td>\n",
"</tr>\n",
"</tr>\n",
"</table>"
"</table>"
],
],
...
@@ -550,24 +564,24 @@
...
@@ -550,24 +564,24 @@
"\"\"\"\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: -
2.525
0\n",
"Method: IRLS Log-Likelihood: -
3.921
0\n",
"Date: S
at, 13 Apr 2019 Deviance: 0.22231
\n",
"Date: S
un, 28 Jan 2024 Deviance: 3.0144
\n",
"Time: 1
9:11:24 Pearson chi2: 0.236
\n",
"Time: 1
7:21:21 Pearson chi2: 5.00
\n",
"No. Iterations:
4
Covariance Type: nonrobust\n",
"No. Iterations:
6
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
5.0850 7.477 0.680 0.496 -9.570 19.740
\n",
"Temperature
0.0014 0.122 0.012 0.991 -0.238 0.24
0\n",
"Temperature
-0.1156 0.115 -1.004 0.316 -0.341 0.11
0\n",
"===============================================================================\n",
"===============================================================================\n",
"\"\"\""
"\"\"\""
]
]
},
},
"execution_count":
4
,
"execution_count":
15
,
"metadata": {},
"metadata": {},
"output_type": "execute_result"
"output_type": "execute_result"
}
}
...
@@ -587,10 +601,9 @@
...
@@ -587,10 +601,9 @@
"cell_type": "markdown",
"cell_type": "markdown",
"metadata": {},
"metadata": {},
"source": [
"source": [
"L'estimateur le plus probable du paramètre de température est 0.0014\n",
"L'estimateur le plus probable du paramètre de température est -0.1156\n",
"et l'erreur standard de cet estimateur est de 0.122, autrement dit on\n",
"et l'erreur standard de cet estimateur est de 0.115. La température a\n",
"ne peut pas distinguer d'impact particulier et il faut prendre nos\n",
"un impact conséquent sur l'apparition de dysfonctionnements.\n"
"estimations avec des pincettes.\n"
]
]
},
},
{
{
...
@@ -605,12 +618,12 @@
...
@@ -605,12 +618,12 @@
},
},
{
{
"cell_type": "code",
"cell_type": "code",
"execution_count":
5
,
"execution_count":
16
,
"metadata": {},
"metadata": {},
"outputs": [
"outputs": [
{
{
"data": {
"data": {
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awbHy57KYS1ErUSNTUQI1ErYQENTUdt0WNoLZmb7u9j8nuqylaRo0K5teI2hoxvOPxUr4sAFFbky1b+bq7PK5omWv68HSUFfSSLgC+B9QCd0fEXxbd/3VgPtAKvAH8SUT8qg/19Nk+r/qljgAKjhpKvcruEzzFO0vnEUL2uP0dtaz/TSvvvbRjnzCIotvdBl3Rq3rbfu7b35HBPvOLQ6rLkcq+Ry7dHYE1N3/AyFVPdd7X2ZcSwVNYb9fneyD/Qnpp3UsH9PDC8FBhQOThURwEtTXZziyU7/D7Pr62RojC8MkeO6y2hkOG7Q2cjmDqWEbn8vL17vztbzn6qCP3CaWOdvvczutUPr+wPqlrqGX9UNewLFFDTan11AhREKI1RcuVgL3PRZdw7QjfGvHsM88we/YZBbUL1RQ9FwXPrfLlVotb+vCYHoNeUi1wB3Ae0ASskrQsIjYWNFsDnBoRH0j6r8BfAVfsb7nbm9s596//uXQQlArtff716Ro8Q9Lavrz2ltbxh93lFb9oR+g86ug4AqnpLmz2hkoWGnuPHrJ1lA6O2jwUJHizpoWjfv/wLjt58Tpqa9Sl9pJHPwV9KWyX9TN7LHmfawtrrum6jNqa7LkR+4ZNjYAuobdvUBTWvnrV85x+2qx9gqXrkV/R9ig6EhzK4dHY2EhDw8mDXUa/+djIGj4+ZuRglzGklHNEPxPYGhGvAkh6ELgY6Az6iHiqoP2zwBd6WujwGlH/e2M6d8CSQdBNKOzdWfNQoGvoFR/xFP6bpOLbRUc1xUddxQGyN/T2hlvx0UiNxIurVzNr1oyCYN7fkR37HAWpKJiGmiwspg12Gf2iaXQNnxg3erDLMKuYcoJ+AvB6wXQTMGs/7b8E/GOpOyQtABYAjB8/nssmvFdmmX0Q+U8FteU/5RirD2ja+EJlCxhCmpubaWxsHOwy+kXKfQP372BUTtCXOpwsGaGSvgCcCvxhqfsjYjGwGKC+vj4aGhrKq7IKZUe8DYNdRr9JuX8p9w3cv4NROUHfBBxTMD0R2FHcSNK5wP8E/jAiPqxMeWZmdqDK+cDUKmCKpMmSRgBXAssKG0iaBtwFzI2InZUv08zM+qrHoI+IVuBaYAWwCVgSERsk3Sxpbt5sEXAo8BNJayUt62ZxZmY2wMq6jj4ilgPLi+Z9q+D2uRWuy6zXlq7ZzqIVm9nxTgtHH17Hwjn1APvMu2TahAGtoT/X1xvfXLqeB557na+dsIcv3bCcebOO4ZZLThzssmwA+JOxloSla7ZzwyPradmTXRe1/Z0WFv7kJRDsaYvOeTc8sh6gX8K3VA39ub7e+ObS9fzw2dc6p9siOqcd9unzoGaWhEUrNncGbIc97dEZ8h1a9rSxaMXmAauhP9fXGw8893qv5ltaHPSWhB3vtPRL20rU0F/r6422bsab6G6+pcVBb0k4+vC6fmlbiRr6a329UdvNp6u7m29pcdBbEhbOqadueG2XecNrxPDarkFWN7y2803agaihP9fXG/NmHdOr+ZYWvxlrSeh4s3Mwr7rprobBfiMW9r7h2nFOvlbyVTcHEQe9JeOSaRNKhupABm13NQwFt1xyIrdcciKNjY3821UNg12ODSCfujEzS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBJXVtBLukDSZklbJX2jxP2HSHoov/85SZMqXaiZmfVNj0EvqRa4A7gQOB6YJ+n4omZfAt6OiH8HfBf4X5Uu1MzM+qacI/qZwNaIeDUiPgIeBC4uanMx8A/57YeBz0hS5co0M7O+GlZGmwnA6wXTTcCs7tpERKukd4FxwJuFjSQtABbkkx9KerkvRVeJIynqf2JS7l/KfQP3r9rV9/YB5QR9qSPz6EMbImIxsBhA0uqIOLWM9Vcl9696pdw3cP+qnaTVvX1MOadumoBjCqYnAju6ayNpGDAW+F1vizEzs8orJ+hXAVMkTZY0ArgSWFbUZhnwx/ntS4F/ioh9jujNzGzg9XjqJj/nfi2wAqgF7o2IDZJuBlZHxDLgHuB+SVvJjuSvLGPdiw+g7mrg/lWvlPsG7l+163X/5ANvM7O0+ZOxZmaJc9CbmSVuQIJe0khJz0t6SdIGSd/O50/Oh0zYkg+hMGIg6ukPkmolrZH0s3w6pb5tk7Re0tqOS7skHSHp8bx/j0v62GDX2VeSDpf0sKT/J2mTpNNT6Z+k+ny7dfy8J+lrCfXvv+WZ8rKkB/KsSWnf+/O8bxskfS2f1+ttN1BH9B8C50TEycBU4AJJp5ENlfDdiJgCvE02lEK1+nNgU8F0Sn0DODsiphZcn/wN4Mm8f0/m09Xqe8DPI+I44GSy7ZhE/yJic77dpgKnAB8Aj5JA/yRNAK4DTo2IE8guFrmSRPY9SScAXyYbneBk4HOSptCXbRcRA/oDjAJeJPt07ZvAsHz+6cCKga6nQn2amD/h5wA/I/sAWRJ9y+vfBhxZNG8zcFR++yhg82DX2ce+HQb8kvzChNT6V9Sn84GnU+kfez+RfwTZFYQ/A+aksu8BlwF3F0z/BfA/+rLtBuwcfX5qYy2wE3gc+DfgnYhozZs0kW24anQ72QZoz6fHkU7fIPuU80pJL+TDWAD8XkT8GiD//fFBq+7AfBJ4A/j7/NTb3ZJGk07/Cl0JPJDfrvr+RcR24DbgNeDXwLvAC6Sz770MnCVpnKRRwGfJPpja6203YEEfEW2R/fs4kexfkU+XajZQ9VSKpM8BOyPihcLZJZpWXd8KzI6I6WQjmP6ZpLMGu6AKGgZMB+6MiGnA+1ThaYye5Oep5wI/GexaKiU/N30xMBk4GhhN9jdarCr3vYjYRHYa6nHg58BLQOt+H9SNAb/qJiLeARqB04DD8yEToPTQCtVgNjBX0jaykT3PITvCT6FvAETEjvz3TrLzuzOB30o6CiD/vXPwKjwgTUBTRDyXTz9MFvyp9K/DhcCLEfHbfDqF/p0L/DIi3oiIPcAjwBmkte/dExHTI+Issg+jbqEP226grroZL+nw/HYd2QbaBDxFNmQCZEMoPDYQ9VRSRNwQERMjYhLZv8b/FBFXkUDfACSNljSm4zbZed6X6TrsRdX2LyJ+A7wuqWNEwM8AG0mkfwXmsfe0DaTRv9eA0ySNyodF79h2Sex7AJI+nv8+FviPZNuw19tuQD4ZK+kksvHqa8leXJZExM2SPkl2FHwEsAb4QkR82O8F9RNJDcD1EfG5VPqW9+PRfHIY8OOIuFXSOGAJcCzZDndZRFTlQHaSpgJ3AyOAV4Evkv+dkkb/RpG9afnJiHg3n5fE9ssv1b6C7JTGGmA+2Tn5qt/3ACT9C9l7fnuAr0fEk33Zdh4Cwcwscf5krJlZ4hz0ZmaJc9CbmSXOQW9mljgHvZlZ4sr5cnCzAZVfPvZkPvn7QBvZMAUAMyPio0EpbD8k/QmwPL8u32xI8eWVNqRJuglojojbhkAttRHR1s19/wpcGxFre7G8YQVjspj1G5+6saoi6Y+VfbfBWkl/I6lG0jBJ70haJOlFSSskzZL0z5JelfTZ/LHzJT2a379Z0jfLXO4tkp4HZkr6tqRV+Rjhf6vMFWTDbz+UP36EpKaCT4OfJumJ/PYtku6S9DjZQGrDJP11vu51kuYP/LNqqXPQW9XIx+f+PHBGPkDeMPZ+Ef1YYGU++NpHwE1kH4m/DLi5YDEz88dMB/6zpKllLPfFiJgZEc8A34uIGcCJ+X0XRMRDwFrgisjGfu/p1NI04KKI+CNgAdmgeDOBGWSDxh3bl+fHrDs+R2/V5FyyMFydDW1CHdlH+wFaIuLx/PZ64N2IaJW0HphUsIwVEfE2gKSlwH8g2w+6W+5H7B0CAuAzkhYCI4EjyYbF/cde9uOxiNid3z4f+LSkwheWKWQfbTerCAe9VRMB90bEX3SZmY1UWHgU3U72rWYdtwv/zovflIoeltsS+RtZ+Zgx/xeYHhHbJd1CFviltLL3P+biNu8X9elPI+JJzPqJT91YNXkCuFzSkZBdndOH0xznK/uO2FFkY5k/3Yvl1pG9cLyZj+j5nwru2wWMKZjeRvbVfRS1K7YC+NOOYXWVfcdrXS/7ZLZfPqK3qhER6/PRCp+QVEM2ot9/oXfjjf8r8GPgD4D7O66SKWe5EfGWpH8gG6b5V8BzBXf/PXC3pBay9wFuAv5O0m+A5/dTz11koxCuzU8b7SR7ATKrGF9eaQeN/IqWEyLia4Ndi9lA8qkbM7PE+YjezCxxPqI3M0ucg97MLHEOejOzxDnozcwS56A3M0vc/wcowwoTqhaBU
gAAAABJRU5ErkJggg==\n",
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX
cAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xl4VOXd//H3dyb7QmLYISA7yA5hEXEBrYK2KiriinVBpHWp7SNVn199tE+16oNt1VZxQ3GpgisupYJa44JbQBBkX8UEkJ0kkD33748ZMGAgQzLJLPm8rivXzDlzn3O+dwY+c3LmnPuYcw4REYkunlAXICIiwadwFxGJQgp3EZEopHAXEYlCCncRkSikcBcRiUI1hruZPW1mW83s28O8bmb2sJmtMbPFZjYw+GWKiMjRCGTPfTow+givnwl09f9MBKbWvSwREamLGsPdOfcxsPMITc4FnnM+XwDpZtY6WAWKiMjRiwnCOtoC31eZzvXP23xoQzObiG/vnsTExKx27drVaoOVlZV4PNHxdYH6Ep6ipS/R0g9QX/ZbtWrVdudc85raBSPcrZp51Y5p4Jx7AngCYNCgQW7+/Pm12mB2djYjRoyo1bLhRn0JT9HSl2jpB6gv+5nZd4G0C8bHYC5QdRc8E9gUhPWKiEgtBSPc3wKu8J81czywxzn3k0MyIiLScGo8LGNmLwEjgGZmlgvcCcQCOOceA2YDZwFrgH3AVfVVrIiIBKbGcHfOXVLD6w64PmgViUhEKCsrIzc3l+Li4gbZXlpaGsuXL2+QbdW3QPqSkJBAZmYmsbGxtdpGML5QFZFGKDc3l9TUVDp06IBZdedVBFdBQQGpqan1vp2GUFNfnHPs2LGD3NxcOnbsWKttRMd5RSLS4IqLi2natGmDBHtjY2Y0bdq0Tn8VKdxFpNYU7PWnrr9bhbuISBTSMXcRiVher5c+ffocmJ41axYdOnQIXUFhROEuIhErMTGRRYsWHfb18vJyYmIaZ8zpsIyIRJXp06dz4YUXcvbZZ3PGGWcAMGXKFAYPHkzfvn258847D7S955576N69Oz/72c+45JJLeOCBBwAYMWIE+4dH2b59+4G/BioqKpg8efKBdT3++OPAj8MJjB07lh49enDZZZfhO0sccnJyOOGEE+jXrx9DhgyhoKCAUaNGHfShNHz4cBYvXhzU30Pj/EgTkaD649tLWbYpP6jr7NmmCXee3euIbYqKiujfvz8AHTt25I033gDg888/Z/HixWRkZDB37lxWr17NV199hXOOc845h48//pjk5GRmzJjBwoULKS8vZ+DAgWRlZR1xe9OmTSMtLY2cnBxKSkoYPnz4gQ+QhQsXsnTpUtq0acPw4cOZN28eQ4YM4aKLLmLmzJkMHjyY/Px8EhMTueKKK5g+fToPPvggq1atoqSkhL59+wbht/YjhbuIRKzDHZY5/fTTycjIAGDu3LnMnTuXAQMGAFBYWMjq1aspKCjgvPPOIykpCYBzzjmnxu3NnTuXxYsX8+qrrwKwZ88eVq9eTVxcHEOGDCEzMxOA/v37s2HDBtLS0mjdujWDBw8GoEmTJgCcd955DB8+nClTpvD0009z5ZVX1u0XUQ2Fu4jUWU172A0tOTn5wHPnHLfffjvXXXfdQW0efPDBw55uGBMTQ2VlJcBB55o75/j73//OqFGjDmqfnZ1NfHz8gWmv10t5eTnOuWq3kZSUxOmnn86bb77Jyy+/TG1HyD0SHXMXkag2atQonn76aQoLCwHIy8tj69atnHzyybzxxhsUFRVRUFDA22+/fWCZDh06sGDBAoADe+n71zV16lTKysoAWLVqFXv37j3stnv06MGmTZvIyckBfFemlpeXAzBhwgRuuukmBg8efOCvjGDSnruIRLUzzjiD5cuXM2zYMABSUlJ44YUXGDhwIBdddBH9+/fn2GOP5aSTTjqwzC233MK4ceN4/vnnOfXUUw/MnzBhAhs2bGDgwIE452jevDmzZs067Lbj4uKYOXMmN954I0VFRSQmJvL+++8DkJWVRZMmTbjqqnoaa9E5F5KfrKwsV1sffvhhrZcNN+pLeIqWvtRnP5YtW1Zv665Ofn5+va7/zjvvdFOmTKnXbeyXn5/v8vLyXNeuXV1FRcVh21X3OwbmuwAyVodlREQa2IsvvsjQoUO555576u3WgTosIyIC3HXXXQ22rUsvvfQnX/AGm/bcRaTWnKv2dskSBHX93SrcRaRWEhIS2LFjhwK+Hjj/eO4JCQm1XocOy4hIrWRmZpKbm8u2bdsaZHvFxcV1CrtwEkhf9t+JqbYU7iJSK7GxsbW+S1BtZGdnH7jKNNI1RF90WEZEJAop3EVEopDCXUQkCincRUSikMJdRCQKKdxFRKKQwl1EJAop3EVEopDCXUQkCincRUSiUMSF+77Sct7bUEZ5RWWoSxERCVsRF+7vLN7MP1eUMu7xz/lux+HvXSgi0phFXLiPG9SOSX3jWbO1kDMf+oSZORs15KiIyCEiLtwBjm8Tw7s3n0z/dunc+toSbnhxIXuKykJdlohI2IjIcAdok57IC9cM5dbRPZizdAtnPfQJX2/cFeqyRETCQsSGO4DHY/xqRGde/dUJeDww7rHPefLjdTpMIyKNXkDhbmajzWylma0xs9uqeT3NzN42s2/MbKmZXRX8Ug+vf7t03rnxJE47rgX3zF7OxOcX6DCNiDRqNYa7mXmBR4AzgZ7AJWbW85Bm1wPLnHP9gBHAX8wsLsi1HlFaYiyPXZ7F//yiJx+u2Mq5//iUFVvyG7IEEZGwEcie+xBgjXNunXOuFJgBnHtIGwekmpkBKcBOoDyolQbAzLj6xI7MmHg8+0orGPPIPN76ZlNDlyEiEnJW0/FpMxsLjHbOTfBPjweGOuduqNImFXgL6AGkAhc55/5VzbomAhMBWrZsmTVjxoxaFV1YWEhKSsoR2+wuqeTRRSWs2lXJWR1jGdstFo9ZrbZXnwLpS6RQX8JPtPQD1Jf9Ro4cucA5N6jGhs65I/4AFwJPVZkeD/z9kDZjgb8BBnQB1gNNjrTerKwsV1sffvhhQO1Kyirc/3tjsTv21nfcFdO+dHuKSmu9zfoSaF8igfoSfqKlH86pL/sB810Nue2cC+iwTC7Qrsp0JnDosY6rgNf9217jD/ceAay7XsXFeLh7TB/uPb8P89Zs5/xHP9NVrSLSKAQS7jlAVzPr6P+S9GJ8h2Cq2gicBmBmLYHuwLpgFloXlwxpz/PXDGV7YQnnPjKPL9btCHVJIiL1qsZwd86VAzcAc4DlwMvOuaVmNsnMJvmb/Qk4wcyWAB8AtzrnttdX0bUxrHNT3rx+OE2T4xg/7UveWJgb6pJEROpNTCCNnHOzgdmHzHusyvNNwBnBLS34jm2azOu/Gs6kFxbw25nfsHFHETed1gULwy9aRUTqIqKvUK2NtKRYnr16COcPbMvf3l/Fba8t0fDBIhJ1AtpzjzZxMR7+cmE/MtMTefg/a9hWWMI/Lh1AUlyj/HWISBRqdHvu+5kZvzujO/ec15vslVu59Mkv2bW3NNRliYgERaMN9/0uG3osUy/PYtnmfC58/HM27S4KdUkiInXW6MMdYFSvVjx71RC27Clm7NTPWLetMNQliYjUicLdb1jnpsyYeDzF5ZWMe/xzlm/WoGMiErkU7lX0bpvGy9cNI8bj4aLHP2ehbv4hIhFK4X6ILi1SeGXSMNKT4hg/7StyNuwMdUkiIkdN4V6NdhlJvHzdMFo0ieeKaV/x2ZqwuthWRKRGCvfDaJWWwMyJw2ifkcRV03P4eNW2UJckIhIwhfsRNE+N56WJx9OxWTITnpuvgBeRiKFwr0FGchwvXns8nZunMOG5+XykgBeRCKBwD0BGchwvThhK5+YpTHxuPvN0DF5EwpzCPUDHJMfxzwlD6dA0mWuezdGY8CIS1hTuRyEjOY5/XjuUzGOSuHp6Dgu+02mSIhKeFO5HqVlKPC9eO5SWTRK48ukcvs3bE+qSRER+QuFeCy1SE/jnhKE0SYxl/LQvWbmlINQliYgcROFeS23SE3nx2qHExXi4fNqXuvG2iIQVhXsdHNs0mReuGUp5RSWXPfUlW/YUh7okERFA4V5nXVum8uzVQ9i9r4zLp+mGHyISHhTuQdA3M50nrxjExp37uHJ6DntLykNdkog0cgr3IBnWuSn/uGQAS3J3M+mFBZSW66bbIhI6CvcgOqNXK+47vy+frN7Of73yDZWVLtQliUgjFRPqAqLNuMHt2LG3lPvfXUHzlHju+MVxmFmoyxKRRkbhXg8mndKJrQXFPD1vPS2bxHPdKZ1DXZKINDIK93pgZtzx855sKyjh3n+voEWTeM4bkBnqskSkEVG41xOPx/jLuH7sKCzl968upkVqAsO7NAt1WSLSSOgL1XoUH+PlsfFZdGqWwqTnF7B8c36oSxKRRkLhXs/SEmN55qrBJMfHcNUzOWzeUxTqkkSkEVC4N4A26Yk8c9VgCkvKueqZHAqKy0JdkohEOYV7AzmudRMevWwgq7cWcv2LCymr0EVOIlJ/FO4N6ORuzfnzeb35eNU2/ufNpTini5xEpH7obJkGdtHg9ny3Yx+PZq+lY7MkuoW6IBGJStpzD4FbzujOz/u05t5/r2D+Fg0yJiLBF1C4m9loM1tpZmvM7LbDtBlhZovMbKmZfRTcMqPL/nPg+7dL54nFJSzO3R3qkkQkytQY7mbmBR4BzgR6ApeYWc9D2qQDjwLnOOd6ARfWQ61RJSHWyxPjB5EaZ0x4dr5OkRSRoApkz30IsMY5t845VwrMAM49pM2lwOvOuY0AzrmtwS0zOjVPjee3WQnsK63gmunzNQ68iASN1XTGhpmNBUY75yb4p8cDQ51zN1Rp8yAQC/QCUoGHnHPPVbOuicBEgJYtW2bNmDGjVkUXFhaSkpJSq2XDTWFhIeuKEvjbghL6t/By44B4PBE6imS0vS/R0Jdo6QeoL/uNHDlygXNuUE3tAjlbprqkOfQTIQbIAk4DEoHPzewL59yqgxZy7gngCYBBgwa5ESNGBLD5n8rOzqa2y4ab7OxsbvrFCJq0Wc9dby8jp6Q1t47uEeqyaiXa3pdo6Eu09APUl6MVSLjnAu2qTGcCm6pps905txfYa2YfA/2AVUhAfnlCB1ZvLWRq9lq6NE/hgiyNIikitRfIMfccoKuZdTSzOOBi4K1D2rwJnGRmMWaWBAwFlge31OhmZtx1Ti9O6NyU219fwoLvdoa6JBGJYDWGu3OuHLgBmIMvsF92zi01s0lmNsnfZjnwLrAY+Ap4yjn3bf2VHZ1ivR4evWwgrdMTuO75BeTt1hk0IlI7AZ3n7pyb7Zzr5pzr7Jy7xz/vMefcY1XaTHHO9XTO9XbOPVhfBUe79KQ4pv1yECVllUx4VmfQiEjt6ArVMNSlRSoPXzqAlVvy+a+XdaNtETl6CvcwNbJ7C/77rON4d+kWHv7P6lCXIyIRRgOHhbFrTuzIii0FPPj+arq3TOXMPq1DXZKIRAjtuYcxM+Oe83ozsH06v3v5G5Zt0m36RCQwCvcwt/8+rGmJsVz73Hx2FJaEuiQRiQAK9wjQIjWBJ67IYnthCb/+59e6i5OI1EjhHiH6ZqZz/wV9+XL9Tv749tJQlyMiYU5fqEaQMQPasnxzPo9/vI6erdO4dGj7UJckImFKe+4R5veje3BKt+bc+da3zN+gIQpEpHoK9wjj9RgPXzyAzGOSmPTCAjZpiAIRqYbCPQKlJcXy5BVZFJdVct3zCyguqwh1SSISZhTuEapLi1T+dlF/luTt4fbXl1DTTVdEpHFRuEew03u25Hend+ONhXlM+3R9qMsRkTCicI9wN4zswuherfjz7OV8snpbqMsRkTChcI9wHo/xwLh+dGmRwg0vLmTjjn2hLklEwoDCPQqkxMfw5BW+++Ve+5zGgBcRhXvUOLZpMv+4dACrtxZwyyvf6AtWkUZO4R5FTuranNvO7MG/v93CIx+uCXU5IhJCCvcoc+1JnRjTvw1/eW8V7y/7IdTliEiIKNyjjJlx3wV96dWmCTfPXMSarYWhLklEQkDhHoUSYr08Pn4Q8TEeJj43nz1FZaEuSUQamMI9SrVNT2Tq5Vls3LmP38xYSIVusi3SqCjco9iQjhnceU4vsldu44G5K0Ndjog0II3nHuUuH9qeZZvymZq9lp6tm3B2vzahLklEGoD23KOcmfHHc3ox6NhjmPzqN3ybtyfUJYlIA1C4NwJxMR6mXp7FMUlxTHxuPtt1k22RqKdwbySap8bz5BWD2LG3lF+9sIDSct1kWySaKdwbkd5t0/i/sX3J2bCLO99aqiEKRKKYvlBtZM7t35blmwt47KO19GydyvhhHUJdkojUA+25N0KTR3Xn1B4t+OPby/hs7fZQlyMi9UDh3gh5PcZDF/enQ7Nkrv/n1xoDXiQKKdwbqdSEWJ66YhCVDiY8l0NBsYYoEIkmCvdGrEOzZB69bCBrt+3l5hmLNESBSBRRuDdyw7s0486ze/LBiq1MmaMhCkSihc6WEcYffywrt/jOoOnWMoXzB2aGuiQRqaOA9tzNbLSZrTSzNWZ22xHaDTazCjMbG7wSpb6ZGXed04thnZpy22tLWPDdzlCXJCJ1VGO4m5kXeAQ4E+gJXGJmPQ/T7n5gTrCLlPoX6/Uw9fKBtElPYOJzC8jdpTNoRCJZIHvuQ4A1zrl1zrlSYAZwbjXtbgReA7YGsT5pQOlJcUy7cjClFZVMeHa+zqARiWBW0yXo/kMso51zE/zT44GhzrkbqrRpC7wInApMA95xzr1azbomAhMBWrZsmTVjxoxaFV1YWEhKSkqtlg034diXpdsr+MuCYno383LzwHg8ZgEtF459qa1o6Uu09APUl/1Gjhy5wDk3qKZ2gXyhWt3/7EM/ER4EbnXOVdgRgsA59wTwBMCgQYPciBEjAtj8T2VnZ1PbZcNNOPZlBJCW+R1/mPUtnxS24M6zewW0XDj2pbaipS/R0g9QX45WIOGeC7SrMp0JbDqkzSBghj/YmwFnmVm5c25WUKqUBnf58ceybttenp63nk7NkjUGjUiECSTcc4CuZtYRyAMuBi6t2sA513H/czObju+wjII9wv2/nx/Hdzv2cudbS8nMSGJk9xahLklEAlTjF6rOuXLgBnxnwSwHXnbOLTWzSWY2qb4LlNDxeoyHLxnAca2bcMM/v2bZpvxQlyQiAQroPHfn3GznXDfnXGfn3D3+eY855x6rpu2V1X2ZKpEpOT6Gab8cTGpCLNc8m8OWPcWhLklEAqDhB6RGrdISmHblIPKLyrh6eg6FJeWhLklEaqBwl4D0apPGI5cNZOUPBdzw4teUV+g2fSLhTOEuARvRvQV/Orc32Su3cceb3+o2fSJhTAOHyVG5dGh7cnft49HstbRNT+SGU7uGuiQRqYbCXY7a5FHd2bKnmAfmrqJVWiJjszSKpEi4UbjLUTMz7rugL1sLSrjttcU0T43nlG7Na7WuWQvzmDJnJZt2F9EmPZHJo7ozZkDbIFcs9UXvX/jSMXeplbgY3yiS3Vqm8qsXFvDN97uPeh2zFuZx++tLyNtdhAPydhdx++tLmLUwL/gFS9Dp/QtvCneptdSEWKZfPZimKXFcPT2HLXuP7gyaKXNWUlRWcdC8orIK3REqQuj9C28Kd6mTFqkJPHvVEBzwwPxifsgP/CKnTbuLjmq+hBe9f+FN4S511ql5CtOvGkxhqeOKaV+xZ19g48C3SU88qvkSXvT+hTeFuwRF38x0bhqYwPrte7n62Rz2ldZ8FevkUd1JjPUeNC8x1svkUd3rq0wJIr1/4U3hLkHTs6mXBy/uz8KNu5j0wteUlh/5GPyYAW259/w+tE1PxIC26Ynce34fnW0RIfT+hTedCilBdVaf1tx3fl9+/9pifjtzEQ9fMgCv5/A3cBkzoK3CIILp/QtfCncJunGD25FfXMbd/1pOcryX+87vi+cIAS8iwadwl3ox4aROFBSX89AHq0mM9XLXOb040i0YRSS4FO5Sb27+WVf2lZbz5CfrSYjzctvoHgp4kQaicJd6Y2b891nHsa+0gsc/Wkd8jJffnd4t1GWJNAoKd6lXZsafzu1NaXklD3+wmliPceNpGklSpL4p3KXeeTy+gcYqKh1/eW8VMV4PvxrROdRliUQ1hbs0CK/HmHJhP8orHfe/uwKH49cjuoS6LJGopXCXBuP1GH8d1w8z+L93V+IcXD9SAS9SHxTu0qBivB7+Oq4/hm9UwfIKx02nddFZNCJBpnCXBuf1GH8Z1x+vx8Pf3l9FSXkFk0d1V8CLBJHCXULC6zGmjO1LXIyHR7PXUlxWyR2/OE4BLxIkCncJGY/H+PN5vYmP8fD0vPXsKy3nnvP6HHEsGhEJjMJdQsrMuPPsnqQmxPD3/6yhsKScv47rT1yMBiwVqQuFu4ScmfFfZ3QnJT6Ge/+9goLicqZePpCkOP3zFKkt7R5J2LjulM7cd34fPlm9jUuf/JJde0tDXZJIxFK4S1i5eEh7Hr0si2Wb87nw8c/J0/04RWpF4S5hZ3TvVjx71RB+yC/mvEfmsWxTfqhLEok4CncJS8M6N+WVScPwmDHu8c/5ZPW2UJckElEU7hK2erRqwhvXn0DmMYlc9UwOM3M2hrokkYihcJew1jotkVcmDWNY56bc+toS7n93BZWVLtRliYQ9hbuEvdSEWJ6+cjCXDGnP1Oy1/OqfC9hXWh7qskTCWkDhbmajzWylma0xs9uqef0yM1vs//nMzPoFv1RpzGK9Hv58Xm/u+EVP3lv2A2Onfs4mnUkjclg1hruZeYFHgDOBnsAlZtbzkGbrgVOcc32BPwFPBLtQETPjmhM7Mu3KwXy/cx9n//1Tvlq/M9RliYSlQPbchwBrnHPrnHOlwAzg3KoNnHOfOed2+Se/ADKDW6bIj0Z2b8Eb1w8nLTGWS5/8guc/34BzOg4vUpXV9J/CzMYCo51zE/zT44GhzrkbDtP+FqDH/vaHvDYRmAjQsmXLrBkzZtSq6MLCQlJSUmq1bLhRX2pvX5nj8cUlfLOtguFtYriiVxzx3uAMOhYt70u09APUl/1Gjhy5wDk3qMaGzrkj/gAXAk9VmR4P/P0wbUcCy4GmNa03KyvL1daHH35Y62XDjfpSNxUVle5v7610HW57x43620du/bbCoKw3Wt6XaOmHc+rLfsB8V0O+OucCOiyTC7SrMp0JbDq0kZn1BZ4CznXO7QhgvSJ15vEYN/+sG89cOZjNe4o5+++f8q/Fm0NdlkjIBRLuOUBXM+toZnHAxcBbVRuYWXvgdWC8c25V8MsUObIR3Vvwr5tOpHOLFK5/8WvumPUtxWUVoS5LJGRqDHfnXDlwAzAH3yGXl51zS81skplN8jf7H6Ap8KiZLTKz+fVWschhZB6TxMvXDePakzry/BffMeaReaz+oSDUZYmEREADZjvnZgOzD5n3WJXnE4CffIEq0tBmL9nM7CVbAFj1QwFnPfwJY/q3Zd6a7WzeU0yb9EQmj+rOmAFtg77tWQvzmDJnJZt2F9XrdgLxh1lLeOnL77m5dxnX3D6bS4a24+4xfUJSi4SG7oYgUWPWwjxuf30JRf7DMZUOXIXjlQW5B9rk7S7i9teXAAQ1eA/ddn1tJxB/mLWEF774cRyeCucOTCvgGw8NPyBRY8qclQfCdb/qTvQtKqtgypyV9b7t+thOIF768vujmi/RSeEuUeNohiMI9k1ADrftUAyRUHGYa1cON1+ik8Jdokab9MSA23oM3lyUF7QrWw+37aOpKVi8Vv2FXIebL9FJ4S5RY/Ko7iTGeg+aF+sxYg+5ajU+xkPmMUn8ZsYiJjw7n9xd++pl24mxXiaP6l7ndR+tS4a2O6r5Ep0U7hI1xgxoy73n96FteiIGtE1PZMqF/Zgytt9B8+6/oC8f3jKCP/z8OD5bu4PT//oxT3y8lrKKyqBu+97z+4TkbJm7x/Th8uPbH9hT95px+fHt9WVqI6OzZSSqjBnQttpArW7ehJM6Mbp3K+56ayl/nr2C1xbk8cdze3F8p6ZB3XYo3D2mD3eP6UN2djZrLxsR6nIkBLTnLo1a5jFJPPXLwTwxPovCknIufuILbnppITuLa78XLxIOtOcuApzRqxUndW3O1I/W8thHa3m3spLvvKu47pROJMXpv4lEHu25i/glxnn53end+OB3p9C/hZeHPljNyAeymZmzkQrdt1UijMJd5BDtMpL4df8EXpk0jNZpidz62hLOfOhj3lv2g24KIhFD4S5yGIM7ZPDGr0/g0csGUlbhuPa5+Yx59DM+Xb1dIS9hT+EucgRmxll9WvPeb0/m/gv6sC2/mMunfcm4xz9n3hqFvIQvhbtIAGK8Hi4a3J7/3DKC/z23F9/vLOKyp77kgqmf8Z8VOlwj4UfhLnIUEmK9XDGsA9mTR/Cnc3vxQ34JV0+fz1kPf8obC3PrdCGUSDAp3EVqISHWy3h/yD9wYT/KKir57cxvOPn/PuTxj9ayZ19ZqEuURk4n8IrUQazXw9isTM4f0JbsVVt54uN13PvvFTz4/mouyGrLFcM60K1laqjLlEZI4S4SBB6PcWqPlpzaoyVLN+1h+rwNvDw/lxe+2MjQjhlcfvyxnNGrJfEx3ppXJhIECneRIOvVJo0pF/bj9rOO4+X53/PCF99x40sLyUiO44KBbRk3qB1dtTcv9UzhLlJPMpLjmHRKZyae1IlP12znpa828sy8DTz5yXr6ZaYxNiuTn/dtQ0ZyXKhLlSikcBepZx6PcXK35pzcrTnbC0uYtTCPVxfkcsebS/nj28sY0b05Z/drw8+Oa0lyvP5LSnDoX5JIA2qWEs+Ekzox4aROLN+cz6yFeby5aBPvL99KQqyHU3u04MzerRnZowUpCnqpA/3rEQmR41o34bjWTbh1dA/mf7eLt7/ZxL+/3cLsJVuIi/FwUpdmnN6zJacd15LmqfGhLlcijMJdJMQ8HmNIxwyGdMzgrnN6seC7Xcxespn3lv3AByu2YraEvpnpnNajBSO6N6d3mzQ8Ht0PVY5M4S4SRrxVgv7Os3uyfHMBHyz3hfzf3l/FX99bRUZyHCd2acaJXZtxYpc9/m12AAANA0lEQVRmIbkJt4Q/hbtImDIzerZpQs82TbjxtK5sLyzh09XbyV65lU/XbOetbzYB0LFZMsd3yuD4Tk0Z0jGD1mkKe1G4i0SMZinxB+7T6pxj5Q8FfLp6O5+v3cE732zmpa++B6BdRiKDj81g4LHH4Aoqqah0eHUYp9FRuItEIDOjR6sm9GjVhAkndaK8opLlmwv4asNOvlq/g49Xb+P1hXkA3Jczh76Z6fRrl06/zDT6tkunTVoCZgr8aKZwF4kCMV4PfTLT6JOZxjUndsQ5x8ad+3jh3c8oSWnNwo27mfbpOsoqfEMTH5MUS++2afRs7Tvsc1zrJnRslkysV2MJRguFu0gUMjOObZrM8LaxjBjRG4CS8gpWbC5gce5ulm7K59tNe3hm3gZK/cMUx3k9dGqeTPdWqXRtkULXlql0aZFC+4wkhX4EUriLNBLxMV7foZl26QfmlVVUsm7bXpZvzmfFlgJWbMln/oZdvLlo04E2sV6jfUYSHZul0Kl5Msc2TaJj02TaN02idVqijueHKYW7SCMW6/XQvVUq3VsdPJBZYUk5a7YWsnZrIWu2FbJ+217Wb9/Lx6u3UVpeWWV5o216Iu0yksg8JonMYxJpm55I22MSaZOeSMvUeGK01x8SCncR+YmU+Bj6t0unf5W9fIDKSseW/GI2bN/Lhh37+H7XPjbu2Efurn3M3bSFHXtLD2rvMWiRmkCrtARaNfE9tmgST4vUBFqkxtOiSTzNUuLJSIrThVlBpnAXkYB5PEabdN9e+Qldfvr6vtJyNu0uIndXEZv3FLN5dxGb9hTzQ34xa7YVMm/tdgqKy3+ynNdjZCTH0TQ5jmYp8TRNiSMjOY6MpDgyUuI4JimO73ZU0HJzPsckxZGWGEtCrEdn/ByBwl1EgiYpLoYuLVLp0uLw49XvKy1na34JWwtK2FpQzPaCErYXlrKtoIQde0vZXljCxp372LW3lIKSgz8I7s/55MDzOK+HtKRYmiTEkJYYS5PEWJokxJKaEEPqgccYUuJ//En2//iee0mKi4na7wwCCnczGw08BHiBp5xz9x3yuvlfPwvYB1zpnPs6yLWKRK1ZC/OYMmclm3YX0SY9kcmjuvPK/I3MW7vzQJvhnTO4cFD7n7QDfjJv/nc7eenL77m5dxnX3D6bS4a24+4xfQLa7pgBbQ87P5Dl92+7wjm8Zj/ZdlJcDB2axbDo+9019uXOs3tycrfm7NxXyofzcujQrSe79pXx2drtZK/cxraCEgqKy/B6jLIKx4bte8kvLqeguOzAaZ81iY/xkBTnC/qkOC+JcV4SY70kxXlJiPU9T4jzkhDjJSHWQ0Lsj4/xMR7iY/yPsT8+j9v/4/3xebzXS2yMEev14FxgtdVFjeFuZl7gEeB0IBfIMbO3nHPLqjQ7E+jq/xkKTPU/ikgNZi3M4/bXl1BUVgFA3u4ibp656Cft5q3deVDY5+0uYvKr34CDskp3YN7vZi6isspyFc7xwhcbAQ4K2eq2e/vrS5j/3U5eW5D3k/nAQQFf3fJ12fbkV74B40Ao5+0u4o43l3Lv+X0YM6AtW5p6GdGnNbMW5vHB8q0Hli0uq+T7nUUH2u1XXFZBYUk5hcXlFBSXU1hSzt6ScvaWlrO3pIK9JeXsK61gb2k5+0p9z4tKKw487thbemC6pLyC4rJKisoqqKisezCf2TGWkSPrvJojCmTPfQiwxjm3DsDMZgDnAlXD/VzgOef7OPrCzNLNrLVzbnPQKxaJMlPmrDwQVEerur3TymraAbz05fcHBWx12y0qqziw133o/ClzVh4UntUtX5dtl1UTmoFut7p2vj1sL81SgjtccllFJcVlFZSUV1JS7nte6n/ue6ygpKyS0grf9IHH8krKKnw/nl0bg1pTdaymPw/MbCww2jk3wT89HhjqnLuhSpt3gPucc5/6pz8AbnXOzT9kXROBif7J7sDKWtbdDNhey2XDjfoSnhqsL3GtumTV17or9u3Bm5R2YLp0y5oFddluXZYPwrLNgO1HWrbqNsJcXf59Heuca15To0D23Kv7tuHQT4RA2uCcewJ4IoBtHrkgs/nOuUF1XU84UF/CU7T0xczml+/ZGvH9gOh5T6Bh+hLI1QW5QLsq05nAplq0ERGRBhJIuOcAXc2so5nFARcDbx3S5i3gCvM5Htij4+0iIqFT42EZ51y5md0AzMF3KuTTzrmlZjbJ//pjwGx8p0GuwXcq5FX1VzIQhEM7YUR9CU/R0pdo6QeoL0elxi9URUQk8mhEHxGRKKRwFxGJQmEf7maWYGZfmdk3ZrbUzP7on59hZu+Z2Wr/4zGhrjUQZuY1s4X+awMiuR8bzGyJmS0ys/n+eZHal3Qze9XMVpjZcjMbFol9MbPu/vdj/0++md0coX35rf//+7dm9pI/ByKuHwBm9ht/P5aa2c3+efXel7APd6AEONU51w/oD4z2n5FzG/CBc64r8IF/OhL8BlheZTpS+wEw0jnXv8r5upHal4eAd51zPYB++N6fiOuLc26l//3oD2ThO7nhDSKsL2bWFrgJGOSc643vRI6LibB+AJhZb+BafFf69wN+YWZdaYi+OOci5gdIAr7GN27NSqC1f35rYGWo6wug/kz/G3kq8I5/XsT1w1/rBqDZIfMiri9AE2A9/pMLIrkvh9R/BjAvEvsCtAW+BzLwndH3jr8/EdUPf50X4htscf/0HcDvG6IvkbDnvv9QxiJgK/Cec+5LoKXzn0vvf2wRyhoD9CC+N7bqEByR2A/wXYE818wW+IeVgMjsSydgG/CM/3DZU2aWTGT2paqLgZf8zyOqL865POABYCOwGd91M3OJsH74fQucbGZNzSwJ3ynj7WiAvkREuDvnKpzvT81MYIj/T52IYma/ALY65yJl7IuaDHfODcQ3Iuj1ZnZyqAuqpRhgIDDVOTcA2EsE/Ll/JP6LDc8BXgl1LbXhP/58LtARaAMkm9nloa2qdpxzy4H7gfeAd4FvgJ/eraQeRES47+ec2w1kA6OBH8ysNYD/cWsISwvEcOAcM9sAzABONbMXiLx+AOCc2+R/3IrvuO4QIrMvuUCu/69BgFfxhX0k9mW/M4GvnXM/+KcjrS8/A9Y757Y558qA14ETiLx+AOCcm+acG+icOxnYCaymAfoS9uFuZs3NLN3/PBHfG78C35AHv/Q3+yXwZmgqDIxz7nbnXKZzrgO+P5n/45y7nAjrB4CZJZtZ6v7n+I6HfksE9sU5twX43sy6+2edhm8464jrSxWX8OMhGYi8vmwEjjezJDMzfO/JciKvHwCYWQv/Y3vgfHzvTb33JeyvUDWzvsCz+L4x9wAvO+f+18yaAi8D7fH9Y7jQObfz8GsKH2Y2ArjFOfeLSOyHmXXCt7cOvsMaLzrn7onEvgCYWX/gKSAOWIdv+AwPkdmXJHxfRnZyzu3xz4u498V/yvNF+A5hLAQmAClEWD8AzOwToClQBvzOOfdBQ7wnYR/uIiJy9ML+sIyIiBw9hbuISBRSuIuIRCGFu4hIFFK4i4hEoUBukC3SoPyniX3gn2wFVOAbIgBgiHOuNCSFHYGZXQ3M9p83LxJyOhVSwpqZ3QUUOuceCINavM65isO89ilwg3Nu0VGsL8Y51yCXokvjo8MyElHM7JfmG99/kZk9amYeM4sxs91mNsXMvjazOWY21Mw+MrN1ZnaWf9kJZvaG//WVZvaHANd7t5l9hW9coz+aWY5/fO7HzOcifMNRz/QvH2dmuVWurD7ezN73P7/bzB43s/fwDVYWY2Z/9W97sZlNaPjfqkQjhbtEDP+AcecBJ/gHkovBN5QDQBow1z+YWSlwF77L1i8E/rfKaob4lxkIXGpm/QNY79fOuSHOuc+Bh5xzg4E+/tdGO+dmAouAi5xvPPWaDhsNAM52zo0HJuIbUG4IMBjfIGzta/P7EalKx9wlkvwMXwDO9w05QiK+S+0Bipxz7/mfL8E3TGy5mS0BOlRZxxzn3C4AM5sFnIjv/8Hh1lvKj0MtAJxmZpOBBKAZsAD491H2403nXLH/+RnAcWZW9cOkK75L0kVqTeEukcSAp51zdxw00ywGXwjvV4nvDl77n1f9d37ol0yuhvUWOf8XU/5xW/4BDHTO5ZnZ3fhCvjrl/PiX8aFt9h7Sp1875z5AJIh0WEYiyfvAODNrBr6zampxCOMM890zNQnfmOHzjmK9ifg+LLb7R8W8oMprBUBqlekN+G51xyHtDjUH+LX/g2T/fVATj7JPIj+hPXeJGM65Jf7RAt83Mw++UfYmAZuOYjWfAi8CnYHn95/dEsh6nXM7zOxZfMMbfwd8WeXlZ4CnzKwI33H9u4AnzWwL8NUR6nkc38iAi/yHhLbi+9ARqROdCimNhv9MlN7OuZtDXYtIfdNhGRGRKKQ9dxGRKKQ9dxGRKKRwFxGJQgp3EZEopHAXEYlCCncRkSj0/wHRUJwHFwSFe
gAAAABJRU5ErkJggg==\n",
"text/plain": [
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
"<Figure size 432x288 with 1 Axes>"
]
]
...
@@ -638,53 +651,22 @@
...
@@ -638,53 +651,22 @@
"scrolled": true
"scrolled": true
},
},
"source": [
"source": [
"Comme on pouvait s'attendre au vu des données initiales, la\n",
" Elle sera d'environ 0.8, ce qui est très important. Même \n",
"température n'a pas d'impact notable sur la probabilité d'échec des\n",
" en prenant en compte le fait que chaque joint est pairé \n",
"joints toriques. Elle sera d'environ 0.2, comme dans les essais\n",
" avec un autre, la probabilité de défaillance d'une paire \n",
"précédents où nous il y a eu défaillance d'au moins un joint. Revenons\n",
" est $p^2 = 0.64$. La probabilité de défaillance d'un des\n",
"à l'ensemble des données initiales pour estimer la probabilité de\n",
"lançeur est donc de $1-(1-p^2)^3 \\approx 0.95%$. La navette \n",
"défaillance d'un joint:\n"
"a toutes les chances d'exploser !!\n",
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.06521739130434782\n"
]
}
],
"source": [
"data = pd.read_csv(\"shuttle.csv\")\n",
"print(np.sum(data.Malfunction)/np.sum(data.Count))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cette probabilité est donc d'environ $p=0.065$, sachant qu'il existe\n",
"un joint primaire un joint secondaire sur chacune des trois parties du\n",
"lançeur, la probabilité de défaillance des deux joints d'un lançeur\n",
"est de $p^2 \\approx 0.00425$. La probabilité de défaillance d'un des\n",
"lançeur est donc de $1-(1-p^2)^3 \\approx 1.2%$. Ça serait vraiment\n",
"pas de chance... Tout est sous contrôle, le décollage peut donc avoir\n",
"lieu demain comme prévu.\n",
"\n",
"\n",
"
Seulement, l
e lendemain, la navette Challenger explosera et emportera\n",
"
L
e lendemain, la navette Challenger explosera et emportera\n",
"avec elle ses sept membres d'équipages. L'opinion publique est\n",
"avec elle ses sept membres d'équipages. L'opinion publique est\n",
"fortement touchée et lors de l'enquête qui suivra, la fiabilité des\n",
"fortement touchée et lors de l'enquête qui suivra, la fiabilité des\n",
"joints toriques sera directement mise en cause.
Au delà d
es problèmes\n",
"joints toriques sera directement mise en cause.
L
es problèmes\n",
"de communication interne à la NASA
qui
sont pour beaucoup dans ce\n",
"de communication interne à la NASA sont pour beaucoup dans ce\n",
"fiasco
, l'analyse précédente comporte (au moins) un petit
\n",
"fiasco
. Le calcul effectué ci-dessous montre que tout indiquait déjà
\n",
"
problème... Saurez-vous le trouver ? Vous êtes libre de modifier cette
\n",
"
une sombre fin à cette célèbre histoire. Cependant, est-il tout à
\n",
"
analyse et de regarder ce jeu de données sous tous les angles afin
\n",
"
fait exacte. Je laisse le lecteur attentif s'en assurer, car mes
\n",
"
d'expliquer ce qui ne va pas
."
"
souvenirs de probabilité ne sont plus tous jeunes..
."
]
]
}
}
],
],
...
@@ -705,7 +687,7 @@
...
@@ -705,7 +687,7 @@
"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,
...
...
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