Updated Answer

parent 416e0738
...@@ -40,7 +40,7 @@ ...@@ -40,7 +40,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -287,7 +287,7 @@ ...@@ -287,7 +287,7 @@
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 7, "execution_count": 1,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -322,7 +322,7 @@ ...@@ -322,7 +322,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -425,7 +425,7 @@ ...@@ -425,7 +425,7 @@
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 8, "execution_count": 2,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -448,7 +448,7 @@ ...@@ -448,7 +448,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -501,7 +501,7 @@ ...@@ -501,7 +501,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -528,7 +528,7 @@ ...@@ -528,7 +528,7 @@
" <th>Date:</th> <td>Thu, 26 Oct 2023</td> <th> Deviance: </th> <td> 0.22231</td> \n", " <th>Date:</th> <td>Thu, 26 Oct 2023</td> <th> Deviance: </th> <td> 0.22231</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Time:</th> <td>10:52:06</td> <th> Pearson chi2: </th> <td> 0.236</td> \n", " <th>Time:</th> <td>11:08:51</td> <th> Pearson chi2: </th> <td> 0.236</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>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
...@@ -557,7 +557,7 @@ ...@@ -557,7 +557,7 @@
"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: -2.5250\n",
"Date: Thu, 26 Oct 2023 Deviance: 0.22231\n", "Date: Thu, 26 Oct 2023 Deviance: 0.22231\n",
"Time: 10:52:06 Pearson chi2: 0.236\n", "Time: 11:08:51 Pearson chi2: 0.236\n",
"No. Iterations: 4 Covariance Type: nonrobust\n", "No. Iterations: 4 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",
...@@ -568,7 +568,7 @@ ...@@ -568,7 +568,7 @@
"\"\"\"" "\"\"\""
] ]
}, },
"execution_count": 10, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -606,7 +606,7 @@ ...@@ -606,7 +606,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -649,7 +649,7 @@ ...@@ -649,7 +649,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -706,7 +706,7 @@ ...@@ -706,7 +706,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -739,7 +739,7 @@ ...@@ -739,7 +739,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 8,
"metadata": { "metadata": {
"scrolled": true "scrolled": true
}, },
...@@ -768,7 +768,7 @@ ...@@ -768,7 +768,7 @@
" <th>Date:</th> <td>Thu, 26 Oct 2023</td> <th> Deviance: </th> <td> 3.0144</td> \n", " <th>Date:</th> <td>Thu, 26 Oct 2023</td> <th> Deviance: </th> <td> 3.0144</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Time:</th> <td>10:54:46</td> <th> Pearson chi2: </th> <td> 5.00</td> \n", " <th>Time:</th> <td>11:08:51</td> <th> Pearson chi2: </th> <td> 5.00</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n", " <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
...@@ -797,7 +797,7 @@ ...@@ -797,7 +797,7 @@
"Link Function: logit Scale: 1.0000\n", "Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -3.9210\n", "Method: IRLS Log-Likelihood: -3.9210\n",
"Date: Thu, 26 Oct 2023 Deviance: 3.0144\n", "Date: Thu, 26 Oct 2023 Deviance: 3.0144\n",
"Time: 10:54:46 Pearson chi2: 5.00\n", "Time: 11:08:51 Pearson chi2: 5.00\n",
"No. Iterations: 6 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",
...@@ -808,7 +808,7 @@ ...@@ -808,7 +808,7 @@
"\"\"\"" "\"\"\""
] ]
}, },
"execution_count": 15, "execution_count": 8,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -832,7 +832,7 @@ ...@@ -832,7 +832,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -863,7 +863,7 @@ ...@@ -863,7 +863,7 @@
"metadata": {}, "metadata": {},
"source": [ "source": [
"It is increadible to see directly how the risk for low temperature can possibly rise if we include all the non-defect values.\n", "It is increadible to see directly how the risk for low temperature can possibly rise if we include all the non-defect values.\n",
"This stays as an estimation but shows that whatever happen not considering tyhe non-defect values has been biaising the prediction." "This stays as an estimation but shows that whatever happen not considering the non-defect values has been biaising the prediction."
] ]
} }
], ],
......
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