diff --git a/module4/MOOC_challenger.ipynb b/module4/MOOC_challenger.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c5ef22fcc55d3c316f5ed3280545d1423eb48723
--- /dev/null
+++ b/module4/MOOC_challenger.ipynb
@@ -0,0 +1,754 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "613c606b-78ea-4262-9700-057e46b188cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]\n",
+      "uname_result(system='Windows', node='mobilis', release='10', version='10.0.14393', machine='AMD64')\n",
+      "IPython 8.18.1\n",
+      "IPython.core.release 8.18.1\n",
+      "PIL 10.3.0\n",
+      "PIL.Image 10.3.0\n",
+      "PIL._deprecate 10.3.0\n",
+      "PIL._version 10.3.0\n",
+      "_csv 1.0\n",
+      "_ctypes 1.1.0\n",
+      "decimal 1.70\n",
+      "_pydev_bundle.fsnotify 0.1.5\n",
+      "_pydevd_frame_eval.vendored.bytecode 0.13.0.dev\n",
+      "argparse 1.1\n",
+      "bottleneck 1.3.7\n",
+      "cffi 1.16.0\n",
+      "colorama 0.4.6\n",
+      "comm 0.1.4\n",
+      "csv 1.0\n",
+      "ctypes 1.1.0\n",
+      "cycler 0.12.1\n",
+      "dateutil 2.8.2\n",
+      "debugpy 1.8.0\n",
+      "debugpy.public_api 1.8.0\n",
+      "decimal 1.70\n",
+      "decorator 5.1.1\n",
+      "defusedxml 0.7.1\n",
+      "exceptiongroup 1.2.0\n",
+      "exceptiongroup._version 1.2.0\n",
+      "executing 2.0.1\n",
+      "executing.version 2.0.1\n",
+      "http.server 0.6\n",
+      "ipykernel 6.26.0\n",
+      "ipykernel._version 6.26.0\n",
+      "ipywidgets 8.1.1\n",
+      "ipywidgets._version 8.1.1\n",
+      "jedi 0.19.1\n",
+      "joblib 1.3.2\n",
+      "joblib.externals.cloudpickle 2.2.0\n",
+      "joblib.externals.loky 3.4.1\n",
+      "json 2.0.9\n",
+      "jupyter_client 8.6.0\n",
+      "jupyter_client._version 8.6.0\n",
+      "jupyter_core 5.5.0\n",
+      "jupyter_core.version 5.5.0\n",
+      "kiwisolver 1.4.5\n",
+      "kiwisolver._cext 1.4.5\n",
+      "logging 0.5.1.2\n",
+      "matplotlib 3.8.2\n",
+      "matplotlib._version 3.8.2\n",
+      "mkl 2.4.1\n",
+      "numexpr 2.9.0\n",
+      "numpy 1.26.4\n",
+      "numpy.core 1.26.4\n",
+      "numpy.core._multiarray_umath 3.1\n",
+      "numpy.linalg._umath_linalg 0.1.5\n",
+      "numpy.version 1.26.4\n",
+      "packaging 23.2\n",
+      "pandas 2.1.3\n",
+      "pandas._version_meson 2.1.3\n",
+      "parso 0.8.3\n",
+      "patsy 0.5.3\n",
+      "patsy.version 0.5.3\n",
+      "pickleshare 0.7.5\n",
+      "platform 1.0.8\n",
+      "platformdirs 4.0.0\n",
+      "platformdirs.version 4.0.0\n",
+      "prompt_toolkit 3.0.41\n",
+      "psutil 5.9.5\n",
+      "pure_eval 0.2.2\n",
+      "pure_eval.version 0.2.2\n",
+      "pydevd 2.9.5\n",
+      "pygments 2.17.2\n",
+      "pyparsing 3.1.1\n",
+      "pytz 2023.3.post1\n",
+      "re 2.2.1\n",
+      "scipy 1.11.4\n",
+      "scipy._lib._uarray 0.8.8.dev0+aa94c5a4.scipy\n",
+      "scipy._lib.decorator 4.0.5\n",
+      "scipy.integrate._dop 1.22.4\n",
+      "scipy.integrate._lsoda 1.22.4\n",
+      "scipy.integrate._vode 1.22.4\n",
+      "scipy.interpolate.dfitpack 1.22.4\n",
+      "scipy.linalg._fblas 1.22.4\n",
+      "scipy.linalg._flapack 1.22.4\n",
+      "scipy.linalg._flinalg 1.22.4\n",
+      "scipy.linalg._interpolative 1.22.4\n",
+      "scipy.optimize.__nnls 1.22.4\n",
+      "scipy.optimize._cobyla 1.22.4\n",
+      "scipy.optimize._lbfgsb 1.22.4\n",
+      "scipy.optimize._minpack2 1.22.4\n",
+      "scipy.optimize._slsqp 1.22.4\n",
+      "scipy.sparse.linalg._eigen.arpack._arpack 1.22.4\n",
+      "scipy.sparse.linalg._isolve._iterative 1.22.4\n",
+      "scipy.special._specfun 1.22.4\n",
+      "scipy.stats._mvn 1.22.4\n",
+      "scipy.stats._statlib 1.22.4\n",
+      "seaborn 0.13.0\n",
+      "seaborn.external.appdirs 1.4.4\n",
+      "seaborn.external.husl 2.1.0\n",
+      "six 1.16.0\n",
+      "socketserver 0.4\n",
+      "stack_data 0.6.2\n",
+      "stack_data.version 0.6.2\n",
+      "statsmodels 0.14.0\n",
+      "statsmodels.__init__ 0.14.0\n",
+      "statsmodels._version 0.14.0\n",
+      "statsmodels.api 0.14.0\n",
+      "statsmodels.tools.web 0.14.0\n",
+      "traitlets 5.14.0\n",
+      "traitlets._version 5.14.0\n",
+      "urllib.request 3.9\n",
+      "wcwidth 0.2.12\n",
+      "xmlrpc.client 3.9\n",
+      "zlib 1.0\n",
+      "zmq 25.1.1\n",
+      "zmq.sugar 25.1.1\n",
+      "zmq.sugar.version 25.1.1\n"
+     ]
+    }
+   ],
+   "source": [
+    " def print_imported_modules():\n",
+    "    import sys\n",
+    "    for name, val in sorted(sys.modules.items()):\n",
+    "        if(hasattr(val, '__version__')): \n",
+    "            print(val.__name__, val.__version__)\n",
+    "#        else:\n",
+    "#            print(val.__name__, \"(unknown version)\")\n",
+    "def print_sys_info():\n",
+    "    import sys\n",
+    "    import platform\n",
+    "    print(sys.version)\n",
+    "    print(platform.uname())\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns\n",
+    "\n",
+    "print_sys_info()\n",
+    "print_imported_modules()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ec5f8965-490f-4db3-b033-12abac36703e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>Malfunction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\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",
+       "      <td>11/12/81</td>\n",
+       "      <td>6</td>\n",
+       "      <td>70</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\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",
+       "      <td>2/03/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>57</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4/06/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>63</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>8/30/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>70</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\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",
+       "      <td>1/24/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>53</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2</td>\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",
+       "      <td>10/30/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>75</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2</td>\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",
+       "      <td>1/12/86</td>\n",
+       "      <td>6</td>\n",
+       "      <td>58</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</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"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"module2_exo5_shuttle.csv\")\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e092983b-fa43-485c-bcd2-870f8f4cc5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oUFJSEvfcAAAAn/Io3AQHB2v16tV65JFHtHPnTjVs2FBdunRxfZs3AACAr3gUbs7q0KGDOnTo4K1aAAAAbPMo3JSXlysjI0MfffSRCgoKVFFR4bb+448/9kpxAAAAteVRuJk4caIyMjI0aNAgde7cWQ6Hw9t1AQAAeMSjcPM///M/ev311zVw4EBv1wMAAGCLR28FDw4OVrt27bxdCwAAgG0ehZspU6bo6aeflmVZ3q4HAADAFo8uS3366afasGGD3n//fV111VUKCgpyW79mzRqvFAcAAFBbHoWbSy65RHfccYe3awEAALDNo3CzfPlyb9cBAADgFR7dcyNJp0+f1ocffqglS5aouLhYknTkyBGdOHHCa8UBAADUlkdnbg4ePKg//OEPys3NVWlpqfr166fGjRvrySef1KlTp7R48WJv1wkAAFAjHp25mThxorp166bjx4+rYcOGrvE77rhDH330kdeKAwAAqC2P3y2VnZ2t4OBgt/HY2FgdPnzYK4UBAAB4wqMzNxUVFSovL680/sMPP6hx48a2iwIAAPCUR+GmX79+WrBggWvZ4XDoxIkTmjlzJl/JAAAAfMqjy1L//Oc/1bdvX1155ZU6deqUhg8frj179qhp06ZatWqVt2sEAACoMY/CTcuWLZWTk6NVq1bpiy++UEVFhZKTk/XnP//Z7QZjAACAuuZRuJGkhg0basyYMRozZow36wEAALDFo3CzYsWKC65PSkryqBgAAAC7PAo3EydOdFt2Op0qKSlRcHCwwsLCCDcAAMBnPHq31PHjx91+Tpw4od27d6tnz57cUAwAAHzK4++WOlf79u31+OOPVzqrAwAAUJe8Fm4kKSAgQEeOHPHmQwIAANSKR/fcrF271m3Zsizl5eXpueeeU48ePbxSGAAAgCc8CjeDBw92W3Y4HGrWrJluuukmzZ8/3xt1AQAAeMSjcFNRUeHtOgAAALzCq/fcAAAA+JpHZ27S0tJqPDc9Pd2TpwAAAPCIR+Fmx44d+uKLL3T69GldccUVkqTvvvtOAQEB6tq1q2uew+HwTpUAAAA15FG4ufXWW9W4cWO9/PLLuvTSSyWd+WC/0aNHq1evXpoyZYpXiwQAAKgpj+65mT9/vubNm+cKNpJ06aWXau7cubxbCgAA+JRH4aaoqEj/+c9/Ko0XFBSouLjYdlEAAACe8ijc3HHHHRo9erTefPNN/fDDD/rhhx/05ptvKjk5WUOGDPF2jQAAADXm0T03ixcv1tSpUzVixAg5nc4zDxQYqOTkZD311FNeLRAAAKA2PAo3YWFhWrhwoZ566int3btXlmWpXbt2atSokbfrAwAAqBVbH+KXl5envLw8dejQQY0aNZJlWd6qCwAAwCMehZvCwkLdfPPN6tChgwYOHKi8vDxJ0tixY3kbOAAA8CmPws3kyZMVFBSk3NxchYWFucaHDh2qDz74wGvFAQAA1JZH99ysX79e69atU6tWrdzG27dvr4MHD3qlMAAAAE94dObm5MmTbmdszjp69KhCQkJsFwUAAOApj8JN7969tWLFCteyw+FQRUWFnnrqKfXt29drxQEAANSWR5elnnrqKfXp00fbtm1TWVmZHnzwQX3zzTc6duyYsrOzvV0jAABAjXl05ubKK6/Ul19+qeuuu079+vXTyZMnNWTIEO3YsUOXX365t2sEAACosVqfuXE6nUpMTNSSJUs0e/bsi1ETAACAx2p95iYoKEhff/21HA7HxagHAADAFo8uSyUlJWnp0qXergUAAMA2j24oLisr00svvaSsrCx169at0ndKpaene6U4AACA2qpVuNm3b5/atGmjr7/+Wl27dpUkfffdd25zuFwFAAB8qVbhpn379srLy9OGDRsknfm6hWeeeUZRUVEXpTgAAIDaqtU9N+d+6/f777+vkydPerUgAAAAOzy6ofisc8OOJxYuXKi4uDiFhoYqPj5emzdvrtF22dnZCgwM1DXXXGO7BgAAYI5ahRuHw1Hpnho799isXr1akyZN0owZM7Rjxw716tVLAwYMUG5u7gW3+/nnn5WUlKSbb77Z4+cGAABmqtU9N5ZladSoUa4vxzx16pRSUlIqvVtqzZo1NXq89PR0JScna+zYsZKkBQsWaN26dVq0aJHmzZt33u3uu+8+DR8+XAEBAXrnnXdqswsAAMBwtQo3I0eOdFseMWKEx09cVlam7du3a9q0aW7jiYmJ2rJly3m3W758ufbu3auVK1dq7ty51T5PaWmpSktLXctFRUWSznzSstPp9LB6/3R2f0zbr7pED+2hf/bRQ3von33+2sPa1FOrcLN8+fJaF3M+R48eVXl5eaV3WkVFRSk/P7/Kbfbs2aNp06Zp8+bNCgysWenz5s2r8msi1q9fr7CwsNoXXg9kZWX5uoR6jx7aQ//so4f20D/7/K2HJSUlNZ7r0Yf4edO59+xYllXlfTzl5eUaPny4Zs+erQ4dOtT48adPn660tDTXclFRkWJiYpSYmKjw8HDPC/dDTqdTWVlZ6tevn4KCgnxdTr1ED+2hf/bRQ3von33+2sOzV15qwmfhpmnTpgoICKh0lqagoKDKz80pLi7Wtm3btGPHDt1///2SpIqKClmWpcDAQK1fv1433XRTpe1CQkJc9wj9UlBQkF/90rzJ5H2rK/TQHvpnHz20h/7Z5289rE0ttt4KbkdwcLDi4+MrnfbKyspS9+7dK80PDw/XV199pZycHNdPSkqKrrjiCuXk5Oj666+vq9IBAIAf8+llqbS0NN1zzz3q1q2bEhIS9MILLyg3N1cpKSmSzlxSOnz4sFasWKEGDRqoc+fObts3b95coaGhlcYBAMCvl0/DzdChQ1VYWKg5c+YoLy9PnTt3VmZmpmJjYyVJeXl51X7mDQAAwC/5/IbicePGady4cVWuy8jIuOC2s2bN0qxZs7xfFAAAqLd8ds8NAADAxUC4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwo0X7fvxhDbsLtD+oyd9XQoA/Cod+O/r78HCEh9XAl8K9HUBJvippEypq3K0ac+PrrHe7Zvp2WHXKiIsyIeVAcCvw9nX4c/2FejJ66RBz27W9W2b8zr8K8WZGy9IXZWj7O+Puo1lf39UE1bt8FFFAPDrwuswfolwY9O+H09o054fVW5ZbuPllqVNe37kEhUAXGS8DuNchBubDh678HXdA4X8oQKAi4nXYZyLcGNTbJOwC65vE9mojioBgF8nXodxLsKNTW2b/Ua92zdTgMPhNh7gcKh3+2aKa8ofKgC4mHgdxrkIN17w7LBr1aNdU7exHu2a6tlh1/qoIgD4deF1GL/EW8G9ICIsSCuSr9P+oyd1oPCk2kQ24l8KAFCHzr4Of5//s7757BP9fxN6qV2LCF+XBR8h3HhRXFNCDQD4UmxkmL7573/x68VlKQAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRfB5uFi5cqLi4OIWGhio+Pl6bN28+79w1a9aoX79+atasmcLDw5WQkKB169bVYbUAAMDf+TTcrF69WpMmTdKMGTO0Y8cO9erVSwMGDFBubm6V8zdt2qR+/fopMzNT27dvV9++fXXrrbdqx44ddVw5AADwVz4NN+np6UpOTtbYsWPVqVMnLViwQDExMVq0aFGV8xcsWKAHH3xQv/vd79S+fXs99thjat++vd599906rhwAAPirQF89cVlZmbZv365p06a5jScmJmrLli01eoyKigoVFxerSZMm551TWlqq0tJS13JRUZEkyel0yul0elC5/zq7P6btV12ih/bQP/vooT30zz5/7WFt6vFZuDl69KjKy8sVFRXlNh4VFaX8/PwaPcb8+fN18uRJ3XXXXeedM2/ePM2ePbvS+Pr16xUWFla7ouuJrKwsX5dQ79FDe+ifffTQHvpnn7/1sKSkpMZzfRZuznI4HG7LlmVVGqvKqlWrNGvWLP3v//6vmjdvft5506dPV1pammu5qKhIMTExSkxMVHh4uOeF+yGn06msrCz169dPQUFBvi6nXqKH9tA/++ihPfTPPn/t4dkrLzXhs3DTtGlTBQQEVDpLU1BQUOlszrlWr16t5ORkvfHGG/r9739/wbkhISEKCQmpNB4UFORXvzRvMnnf6go9tIf+2UcP7aF/9vlbD2tTi89uKA4ODlZ8fHyl015ZWVnq3r37ebdbtWqVRo0apddee02DBg262GUCAIB6xqeXpdLS0nTPPfeoW7duSkhI0AsvvKDc3FylpKRIOnNJ6fDhw1qxYoWkM8EmKSlJTz/9tG644QbXWZ+GDRsqIiLCZ/sBAAD8h0/DzdChQ1VYWKg5c+YoLy9PnTt3VmZmpmJjYyVJeXl5bp95s2TJEp0+fVrjx4/X+PHjXeMjR45URkZGXZcPAAD8kM9vKB43bpzGjRtX5bpzA8snn3xy8QsCAAD1ms+/fgEAAMCbCDfwmn0/ntCG3QXaf/SkT7YHPTRB9p4fJUlb9x71cSVA/eXzy1Ko/34qKVPqqhxt+u+LsiT1bt9Mzw67VhFh1b91z+72oIcmOFh4UoOfz1ZJaZmevE6695XtCgsJ1trxPRUTaeYHjgIXC2duYFvqqhxlf+/+r8zs749qwqqafaGp3e1BD00w+PlsHS9x/3j54yVO3fb8pz6qCKi/CDewZd+PJ7Rpz48qtyy38XLL0qY9P1Z7ecTu9qCHJti4u6BSsDnreIlTm39xRg5A9Qg3sOXgsQt/18eBwgv/xWp3e9BDE+T88NMF13+Re7xuCgEMQbiBLbFNLnwvQJvIRhd1e9BDE1zT6pILru/a+tK6KQQwBOEGtrRt9hv1bt9MAed82WmAw6He7ZsprumF/2K1uz3ooQluvKK5Lj3Pjd+XhgWpV/tmdVwRUL8RbmDbs8OuVY92Td3GerRrqmeHXVsn24MemmDt+J6VAs6lYUFaO76njyoC6i/eCg7bIsKCtCL5Ou0/elIHCk+qTWSjWp0tsLs96KEJYiLDtOPhRG36//N0fPe/9eI98erdMdrXZQH1EuEGXhPX1N5fqHa3Bz00QcLlTZW5+8x/AXiGy1IAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABjF5+Fm4cKFiouLU2hoqOLj47V58+YLzt+4caPi4+MVGhqqtm3bavHixXVUKYCLbd+PJ7Rhd4H2Hz3p0far/52rSat36I1th3xWg93ts/f8KEnauveoR9vb5ev9t+vAf5/3YGGJR9tv3F2gpz/6Tpv/+3vwhfr+O/D180tSoM+eWdLq1as1adIkLVy4UD169NCSJUs0YMAAffvtt2rdunWl+fv379fAgQN17733auXKlcrOzta4cePUrFkz/fGPf/TBHgDwhp9KypS6KkebfvEXSu/2zfTssGsVERZU7fZf/fCT7li4RacrLEnSOzuOaPqar7R2fA9deVlEndRgd/uDhSc1+PlslZSW6cnrpHtf2a6wkGCtHd9TMZFhNdoHO3y9/3adff7P9hXoyeukQc9u1vVtm9e6/8dLnK6xS8OC6qz/kjm/A189/y/59MxNenq6kpOTNXbsWHXq1EkLFixQTEyMFi1aVOX8xYsXq3Xr1lqwYIE6deqksWPHasyYMfrHP/5Rx5UD8KbUVTnK/t79TEX290c1YdWOGm3/y2Bz1ukKS7c9n11nNdjd/ty/WCXpeIlTtz3/aY22t8vX+29Xfe+/xO/Am3x25qasrEzbt2/XtGnT3MYTExO1ZcuWKrfZunWrEhMT3cb69++vpUuXyul0KiiocjIsLS1VaWmpa/nnn3+WJB07dkxOp7PS/PrM6XSqpKREhYWFVfYC1aOH9njSv9zCk9qy64AcqvyCtGXXCe3YE63WTc7/L+e1OYelshPnfTF7+eOvdMvVLS9qDXa3/9feoyou+kmBkgIrLJWUVCjQ2UDlFQ4VF0nvb/tO18VFXnAf7PD1/tvl9vwN3PtX2/6fqy76L/nX78AXf45rori4WJJkWVY1M89M8onDhw9bkqzs7Gy38UcffdTq0KFDldu0b9/eevTRR93GsrOzLUnWkSNHqtxm5syZliR++OGHH3744ceAn0OHDlWbMXx6z40kORwOt2XLsiqNVTe/qvGzpk+frrS0NNdyRUWFjh07psjIyAs+T31UVFSkmJgYHTp0SOHh4b4up16ih/bQP/vooT30zz5/7aFlWSouLlbLlhc+Eyv58LJU06ZNFRAQoPz8fLfxgoICRUVFVblNixYtqpwfGBioyMiqTxmGhIQoJCTEbeySSy7xvPB6IDw83K8OyPqIHtpD/+yjh/bQP/v8sYcRERE1muezG4qDg4MVHx+vrKwst/GsrCx17969ym0SEhIqzV+/fr26devG/REAAECSj98tlZaWppdeeknLli3Trl27NHnyZOXm5iolJUXSmUtKSUlJrvkpKSk6ePCg0tLStGvXLi1btkxLly7V1KlTfbULAADAz/j0npuhQ4eqsLBQc+bMUV5enjp37qzMzEzFxsZKkvLy8pSbm+uaHxcXp8zMTE2ePFnPP/+8WrZsqWeeeYbPuPmvkJAQzZw5s9JlONQcPbSH/tlHD+2hf/aZ0EOHZdXkPVUAAAD1g8+/fgEAAMCbCDcAAMAohBsAAGAUwg0AADAK4aaemTVrlhwOh9tPixYtXOtHjRpVaf0NN9zgw4r90+HDhzVixAhFRkYqLCxM11xzjbZv3+5ab1mWZs2apZYtW6phw4bq06ePvvnmGx9W7F+q6x/H4YW1adOmUn8cDofGjx8vieOvOtX1j+OveqdPn9bf/vY3xcXFqWHDhmrbtq3mzJmjiooK15z6fBz6/OsXUHtXXXWVPvzwQ9dyQECA2/o//OEPWr58uWs5ODi4zmqrD44fP64ePXqob9++ev/999W8eXPt3bvX7ZOrn3zySaWnpysjI0MdOnTQ3Llz1a9fP+3evVuNGzf2XfF+oCb9kzgOL+Tzzz9XeXm5a/nrr79Wv379dOedd0ri+KtOdf2TOP6q88QTT2jx4sV6+eWXddVVV2nbtm0aPXq0IiIiNHHiREn1/Dis9tun4FdmzpxpXX311eddP3LkSOv222+vs3rqo4ceesjq2bPneddXVFRYLVq0sB5//HHX2KlTp6yIiAhr8eLFdVGiX6uuf5bFcVhbEydOtC6//HKroqKC488Dv+yfZXH81cSgQYOsMWPGuI0NGTLEGjFihGVZ9f91kMtS9dCePXvUsmVLxcXF6e6779a+ffvc1n/yySdq3ry5OnTooHvvvVcFBQU+qtQ/rV27Vt26ddOdd96p5s2b69prr9WLL77oWr9//37l5+crMTHRNRYSEqIbb7xRW7Zs8UXJfqW6/p3FcVgzZWVlWrlypcaMGSOHw8HxV0vn9u8sjr8L69mzpz766CN99913kqSdO3fq008/1cCBAyXV/9dBwk09c/3112vFihVat26dXnzxReXn56t79+4qLCyUJA0YMECvvvqqPv74Y82fP1+ff/65brrpJpWWlvq4cv+xb98+LVq0SO3bt9e6deuUkpKi1NRUrVixQpJcX8567he4RkVFVfri1l+j6voncRzWxjvvvKOffvpJo0aNksTxV1vn9k/i+KuJhx56SMOGDVPHjh0VFBSka6+9VpMmTdKwYcMkGXAc+vrUEew5ceKEFRUVZc2fP7/K9UeOHLGCgoKst956q44r819BQUFWQkKC29iECROsG264wbIsy8rOzrYkWUeOHHGbM3bsWKt///51Vqe/qq5/VeE4PL/ExETrlltucS1z/NXOuf2rCsdfZatWrbJatWplrVq1yvryyy+tFStWWE2aNLEyMjIsy6r/xyFnbuq5Ro0aqUuXLtqzZ0+V66OjoxUbG3ve9b9G0dHRuvLKK93GOnXq5Poes7PvPjv3XycFBQWV/hXza1Rd/863DcdhZQcPHtSHH36osWPHusY4/mquqv5VheOvsgceeEDTpk3T3XffrS5duuiee+7R5MmTNW/ePEn1/zgk3NRzpaWl2rVrl6Kjo6tcX1hYqEOHDp13/a9Rjx49tHv3brex7777zvWFrXFxcWrRooWysrJc68vKyrRx40Z17969Tmv1R9X1ryoch1Vbvny5mjdvrkGDBrnGOP5qrqr+VYXjr7KSkhI1aOAeAQICAlxvBa/3x6GvTx2hdqZMmWJ98skn1r59+6x//etf1i233GI1btzYOnDggFVcXGxNmTLF2rJli7V//35rw4YNVkJCgnXZZZdZRUVFvi7db/z73/+2AgMDrUcffdTas2eP9eqrr1phYWHWypUrXXMef/xxKyIiwlqzZo311VdfWcOGDbOio6Ppo1V9/zgOa6a8vNxq3bq19dBDD1Vax/FXvfP1j+OvZkaOHGlddtll1nvvvWft37/fWrNmjdW0aVPrwQcfdM2pz8ch4aaeGTp0qBUdHW0FBQVZLVu2tIYMGWJ98803lmVZVklJiZWYmGg1a9bMCgoKslq3bm2NHDnSys3N9XHV/ufdd9+1OnfubIWEhFgdO3a0XnjhBbf1FRUV1syZM60WLVpYISEhVu/eva2vvvrKR9X6nwv1j+OwZtatW2dJsnbv3l1pHcdf9c7XP46/mikqKrImTpxotW7d2goNDbXatm1rzZgxwyotLXXNqc/HocOyLMvXZ48AAAC8hXtuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AXJDD4bjgz6hRo3xdotf16dNHkyZN8nUZADwU6OsCAPi3vLw81/+vXr1aDz/8sNsXZzZs2NAXZXnE6XQqKCjI2OcDcAZnbgBcUIsWLVw/ERERcjgcbmObNm1SfHy8QkND1bZtW82ePVunT592be9wOLRkyRLdcsstCgsLU6dOnbR161Z9//336tOnjxo1aqSEhATt3bvXtc2sWbN0zTXXaMmSJYqJiVFYWJjuvPNO/fTTT261LV++XJ06dVJoaKg6duyohQsXutYdOHBADodDr7/+uvr06aPQ0FCtXLlShYWFGjZsmFq1aqWwsDB16dJFq1atcm03atQobdy4UU8//bTr7NSBAweUkZGhSy65xO3533nnHTkcjkp1L1u2TG3btlVISIgsy9LPP/+sv/zlL2revLnCw8N10003aefOnV76DQE4F+EGgMfWrVunESNGKDU1Vd9++62WLFmijIwMPfroo27zHnnkESUlJSknJ0cdO3bU8OHDdd9992n69Onatm2bJOn+++932+b777/X66+/rnfffVcffPCBcnJyNH78eNf6F198UTNmzNCjjz6qXbt26bHHHtPf//53vfzyy26P89BDDyk1NVW7du1S//79derUKcXHx+u9997T119/rb/85S+655579Nlnn0mSnn76aSUkJOjee+9VXl6e8vLyFBMTU+OenK37rbfeUk5OjiRp0KBBys/PV2ZmprZv366uXbvq5ptv1rFjx2r8uABqwcdf3AmgHlm+fLkVERHhWu7Vq5f12GOPuc155ZVXrOjoaNeyJOtvf/uba3nr1q2WJGvp0qWusVWrVlmhoaGu5ZkzZ1oBAQHWoUOHXGPvv/++1aBBAysvL8+yLMuKiYmxXnvtNbfnfuSRR6yEhATLsixr//79liRrwYIF1e7XwIEDrSlTpriWb7zxRmvixIkX3HfLsqy3337b+uXL6MyZM62goCCroKDANfbRRx9Z4eHh1qlTp9y2vfzyy60lS5ZUWxuA2uOeGwAe2759uz7//HO3MzXl5eU6deqUSkpKFBYWJkn67W9/61ofFRUlSerSpYvb2KlTp1RUVKTw8HBJUuvWrdWqVSvXnISEBFVUVGj37t0KCAjQoUOHlJycrHvvvdc15/Tp04qIiHCrsVu3bm7L5eXlevzxx7V69WodPnxYpaWlKi0tVaNGjey2Q5IUGxurZs2auZa3b9+uEydOKDIy0m3e//3f/7ldigPgPYQbAB6rqKjQ7NmzNWTIkErrQkNDXf//y5tqz96jUtVYRUXFeZ/r7ByHw+Ga9+KLL+r66693mxcQEOC2fG5omT9/vv75z39qwYIF6tKlixo1aqRJkyaprKzs/DsqqUGDBrIsy23M6XRWmnfu81VUVCg6OlqffPJJpbnn3sMDwDsINwA81rVrV+3evVvt2rXz+mPn5ubqyJEjatmypSRp69atatCggTp06KCoqChddtll2rdvn/785z/X6nE3b96s22+/XSNGjJB0Jnzs2bNHnTp1cs0JDg5WeXm523bNmjVTcXGxTp486QowZ++puZCuXbsqPz9fgYGBatOmTa1qBeAZwg0Ajz388MO65ZZbFBMTozvvvFMNGjTQl19+qa+++kpz58619dihoaEaOXKk/vGPf6ioqEipqam666671KJFC0ln3pmUmpqq8PBwDRgwQKWlpdq2bZuOHz+utLS08z5uu3bt9NZbb2nLli269NJLlZ6ervz8fLdw06ZNG3322Wc6cOCAfvOb36hJkya6/vrrFRYWpr/+9a+aMGGC/v3vfysjI6Pa/fj973+vhIQEDR48WE888YSuuOIKHTlyRJmZmRo8eHCly2YA7OPdUgA81r9/f7333nvKysrS7373O91www1KT09XbGys7cdu166dhgwZooEDByoxMVGdO3d2e6v32LFj9dJLLykjI0NdunTRjTfeqIyMDMXFxV3wcf/+97+ra9eu6t+/v/r06aMWLVpo8ODBbnOmTp2qgIAAXXnllWrWrJlyc3PVpEkTrVy5UpmZma63j8+aNava/XA4HMrMzFTv3r01ZswYdejQQXfffbcOHDjguv8IgHc5rHMvIgOAj82aNUvvvPNOjS77AMC5OHMDAACMQrgBAABG4bIUAAAwCmduAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBR/h9DigUvvZMUYwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "data[\"Frequency\"]=data.Malfunction/data.Count\n",
+    "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5ae87d39-9f53-493e-a64a-ce5d0420ab9e",
+   "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>    21</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>     1</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> -3.9210</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>            <td>Mon, 16 Sep 2024</td> <th>  Deviance:          </th> <td>  3.0144</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                <td>11:15:44</td>     <th>  Pearson chi2:      </th>  <td>  5.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Iterations:</th>          <td>6</td>        <th>  Pseudo R-squ. (CS):</th>  <td>0.04355</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </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>    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",
+       "  <th>Temperature</th> <td>   -0.1156</td> <td>    0.115</td> <td>   -1.004</td> <td> 0.316</td> <td>   -0.341</td> <td>    0.110</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}   &    Frequency     & \\textbf{  No. Observations:  } &       23    \\\\\n",
+       "\\textbf{Model:}           &       GLM        & \\textbf{  Df Residuals:      } &       21    \\\\\n",
+       "\\textbf{Model Family:}    &     Binomial     & \\textbf{  Df Model:          } &        1    \\\\\n",
+       "\\textbf{Link Function:}   &      Logit       & \\textbf{  Scale:             } &    1.0000   \\\\\n",
+       "\\textbf{Method:}          &       IRLS       & \\textbf{  Log-Likelihood:    } &   -3.9210   \\\\\n",
+       "\\textbf{Date:}            & Mon, 16 Sep 2024 & \\textbf{  Deviance:          } &    3.0144   \\\\\n",
+       "\\textbf{Time:}            &     11:15:44     & \\textbf{  Pearson chi2:      } &     5.00    \\\\\n",
+       "\\textbf{No. Iterations:}  &        6         & \\textbf{  Pseudo R-squ. (CS):} &  0.04355    \\\\\n",
+       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                     & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{Intercept}   &       5.0850  &        7.477     &     0.680  &         0.496        &       -9.570    &       19.740     \\\\\n",
+       "\\textbf{Temperature} &      -0.1156  &        0.115     &    -1.004  &         0.316        &       -0.341    &        0.110     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{Generalized Linear Model Regression Results}\n",
+       "\\end{center}"
+      ],
+      "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:                       21\n",
+       "Model Family:                Binomial   Df Model:                            1\n",
+       "Link Function:                  Logit   Scale:                          1.0000\n",
+       "Method:                          IRLS   Log-Likelihood:                -3.9210\n",
+       "Date:                Mon, 16 Sep 2024   Deviance:                       3.0144\n",
+       "Time:                        11:15:44   Pearson chi2:                     5.00\n",
+       "No. Iterations:                     6   Pseudo R-squ. (CS):            0.04355\n",
+       "Covariance Type:            nonrobust                                         \n",
+       "===============================================================================\n",
+       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "-------------------------------------------------------------------------------\n",
+       "Intercept       5.0850      7.477      0.680      0.496      -9.570      19.740\n",
+       "Temperature    -0.1156      0.115     -1.004      0.316      -0.341       0.110\n",
+       "===============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 7,
+     "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",
+    "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n",
+    "                family=sm.families.Binomial()).fit()\n",
+    "\n",
+    "logmodel.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "29bfd3cd-ffcd-4411-841c-f89c69e167e1",
+   "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>    21</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>     1</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> -23.526</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>            <td>Mon, 16 Sep 2024</td> <th>  Deviance:          </th> <td>  18.086</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                <td>11:16:03</td>     <th>  Pearson chi2:      </th>  <td>  30.0</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Iterations:</th>          <td>6</td>        <th>  Pseudo R-squ. (CS):</th>  <td>0.2344</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </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>    5.0850</td> <td>    3.052</td> <td>    1.666</td> <td> 0.096</td> <td>   -0.898</td> <td>   11.068</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Temperature</th> <td>   -0.1156</td> <td>    0.047</td> <td>   -2.458</td> <td> 0.014</td> <td>   -0.208</td> <td>   -0.023</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}   &    Frequency     & \\textbf{  No. Observations:  } &       23    \\\\\n",
+       "\\textbf{Model:}           &       GLM        & \\textbf{  Df Residuals:      } &       21    \\\\\n",
+       "\\textbf{Model Family:}    &     Binomial     & \\textbf{  Df Model:          } &        1    \\\\\n",
+       "\\textbf{Link Function:}   &      Logit       & \\textbf{  Scale:             } &    1.0000   \\\\\n",
+       "\\textbf{Method:}          &       IRLS       & \\textbf{  Log-Likelihood:    } &   -23.526   \\\\\n",
+       "\\textbf{Date:}            & Mon, 16 Sep 2024 & \\textbf{  Deviance:          } &    18.086   \\\\\n",
+       "\\textbf{Time:}            &     11:16:03     & \\textbf{  Pearson chi2:      } &     30.0    \\\\\n",
+       "\\textbf{No. Iterations:}  &        6         & \\textbf{  Pseudo R-squ. (CS):} &   0.2344    \\\\\n",
+       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                     & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{Intercept}   &       5.0850  &        3.052     &     1.666  &         0.096        &       -0.898    &       11.068     \\\\\n",
+       "\\textbf{Temperature} &      -0.1156  &        0.047     &    -2.458  &         0.014        &       -0.208    &       -0.023     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{Generalized Linear Model Regression Results}\n",
+       "\\end{center}"
+      ],
+      "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:                       21\n",
+       "Model Family:                Binomial   Df Model:                            1\n",
+       "Link Function:                  Logit   Scale:                          1.0000\n",
+       "Method:                          IRLS   Log-Likelihood:                -23.526\n",
+       "Date:                Mon, 16 Sep 2024   Deviance:                       18.086\n",
+       "Time:                        11:16:03   Pearson chi2:                     30.0\n",
+       "No. Iterations:                     6   Pseudo R-squ. (CS):             0.2344\n",
+       "Covariance Type:            nonrobust                                         \n",
+       "===============================================================================\n",
+       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "-------------------------------------------------------------------------------\n",
+       "Intercept       5.0850      3.052      1.666      0.096      -0.898      11.068\n",
+       "Temperature    -0.1156      0.047     -2.458      0.014      -0.208      -0.023\n",
+       "===============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n",
+    "                family=sm.families.Binomial(),\n",
+    "                var_weights=data['Count']).fit()\n",
+    "\n",
+    "logmodel.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "776aaeb4-e1dd-4dd5-85ef-7db52cd93912",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
+    "data_pred['Frequency'] = logmodel.predict(data_pred)\n",
+    "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
+    "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7ddbe776-9979-4891-b615-8e5baaec5f90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(color_codes=True)\n",
+    "plt.xlim(30,90)\n",
+    "plt.ylim(0,1)\n",
+    "sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf250191-a2b3-4148-adde-3442cdc86e4b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}