HW2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c1e4ceff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from scipy.stats import t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "58ab7dc9",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minutes</th>\n",
       "      <th>copiers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>20</td>\n",
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       "      <th>2</th>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
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       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
       "      <td>137</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>68</td>\n",
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       "      <th>7</th>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>144</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>156</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>72</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>105</td>\n",
       "      <td>7</td>\n",
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       "      <th>17</th>\n",
       "      <td>131</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>127</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>101</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>109</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>74</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>134</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>112</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>111</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>96</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>123</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <th>35</th>\n",
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       "      <td>4</td>\n",
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       "      <th>36</th>\n",
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       "      <td>5</td>\n",
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       "      <th>37</th>\n",
       "      <td>132</td>\n",
       "      <td>9</td>\n",
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       "      <th>38</th>\n",
       "      <td>112</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>131</td>\n",
       "      <td>9</td>\n",
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       "      <th>41</th>\n",
       "      <td>34</td>\n",
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       "      <th>42</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>77</td>\n",
       "      <td>5</td>\n",
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      ],
      "text/plain": [
       "    minutes  copiers\n",
       "0        20        2\n",
       "1        60        4\n",
       "2        46        3\n",
       "3        41        2\n",
       "4        12        1\n",
       "5       137       10\n",
       "6        68        5\n",
       "7        89        5\n",
       "8         4        1\n",
       "9        32        2\n",
       "10      144        9\n",
       "11      156       10\n",
       "12       93        6\n",
       "13       36        3\n",
       "14       72        4\n",
       "15      100        8\n",
       "16      105        7\n",
       "17      131        8\n",
       "18      127       10\n",
       "19       57        4\n",
       "20       66        5\n",
       "21      101        7\n",
       "22      109        7\n",
       "23       74        5\n",
       "24      134        9\n",
       "25      112        7\n",
       "26       18        2\n",
       "27       73        5\n",
       "28      111        7\n",
       "29       96        6\n",
       "30      123        8\n",
       "31       90        5\n",
       "32       20        2\n",
       "33       28        2\n",
       "34        3        1\n",
       "35       57        4\n",
       "36       86        5\n",
       "37      132        9\n",
       "38      112        7\n",
       "39       27        1\n",
       "40      131        9\n",
       "41       34        2\n",
       "42       27        2\n",
       "43       61        4\n",
       "44       77        5"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q2\n",
    "#Import data\n",
    "df=pd.read_csv(r'C:\\Users\\Chenyi\\Desktop\\LR\\hw1\\copier_maintenance.txt',delim_whitespace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "eb3ad776",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.loc[:,'minutes'].values #transform series into array\n",
    "x=df.loc[:,'copiers'].values\n",
    "\n",
    "X=np.ones([len(x),2])\n",
    "X[:,1]=x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bc3216c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.957\n",
      "Model:                            OLS   Adj. R-squared:                  0.957\n",
      "Method:                 Least Squares   F-statistic:                     968.7\n",
      "Date:                Thu, 13 Oct 2022   Prob (F-statistic):           4.01e-31\n",
      "Time:                        18:19:30   Log-Likelihood:                -161.27\n",
      "No. Observations:                  45   AIC:                             326.5\n",
      "Df Residuals:                      43   BIC:                             330.2\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.5802      2.804     -0.207      0.837      -6.235       5.075\n",
      "x1            15.0352      0.483     31.123      0.000      14.061      16.009\n",
      "==============================================================================\n",
      "Omnibus:                        1.399   Durbin-Watson:                   2.402\n",
      "Prob(Omnibus):                  0.497   Jarque-Bera (JB):                1.167\n",
      "Skew:                          -0.388   Prob(JB):                        0.558\n",
      "Kurtosis:                       2.853   Cond. No.                         12.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(y, X).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "00bc4de4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0188954308469501\n"
     ]
    }
   ],
   "source": [
    "#2C\n",
    "test=(15.0352-14)/(0.483)\n",
    "p_value=t.sf(test, 43)\n",
    "print(p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3504229d",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Stay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Cult</th>\n",
       "      <th>Xray</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7.13</td>\n",
       "      <td>55.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>39.6</td>\n",
       "      <td>279</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>207</td>\n",
       "      <td>241</td>\n",
       "      <td>60.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8.82</td>\n",
       "      <td>58.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>51.7</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8.34</td>\n",
       "      <td>56.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>8.1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>107</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>54</td>\n",
       "      <td>20.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8.95</td>\n",
       "      <td>53.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>18.9</td>\n",
       "      <td>122.8</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>148</td>\n",
       "      <td>40.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>11.20</td>\n",
       "      <td>56.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>34.5</td>\n",
       "      <td>88.9</td>\n",
       "      <td>180</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>134</td>\n",
       "      <td>151</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>109</td>\n",
       "      <td>11.80</td>\n",
       "      <td>53.8</td>\n",
       "      <td>5.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>116.9</td>\n",
       "      <td>571</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>441</td>\n",
       "      <td>469</td>\n",
       "      <td>62.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>110</td>\n",
       "      <td>9.50</td>\n",
       "      <td>49.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>70.9</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>111</td>\n",
       "      <td>7.70</td>\n",
       "      <td>56.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12.2</td>\n",
       "      <td>67.9</td>\n",
       "      <td>129</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>136</td>\n",
       "      <td>62.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>112</td>\n",
       "      <td>17.94</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>26.4</td>\n",
       "      <td>91.8</td>\n",
       "      <td>835</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>791</td>\n",
       "      <td>407</td>\n",
       "      <td>62.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>113</td>\n",
       "      <td>9.41</td>\n",
       "      <td>59.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>20.6</td>\n",
       "      <td>91.7</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID   Stay   Age  Risk  Cult   Xray  Beds  MS  Reg  Cen  Nurses   AFS\n",
       "0      1   7.13  55.7   4.1   9.0   39.6   279   2    4  207     241  60.0\n",
       "1      2   8.82  58.2   1.6   3.8   51.7    80   2    2   51      52  40.0\n",
       "2      3   8.34  56.9   2.7   8.1   74.0   107   2    3   82      54  20.0\n",
       "3      4   8.95  53.7   5.6  18.9  122.8   147   2    4   53     148  40.0\n",
       "4      5  11.20  56.5   5.7  34.5   88.9   180   2    1  134     151  40.0\n",
       "..   ...    ...   ...   ...   ...    ...   ...  ..  ...  ...     ...   ...\n",
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       "\n",
       "[113 rows x 12 columns]"
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     },
     "execution_count": 40,
     "metadata": {},
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   "source": [
    "#Q3\n",
    "df2=pd.read_csv(r'C:\\Users\\Chenyi\\Desktop\\LR\\hw1\\SENIC.txt',delim_whitespace = True)\n",
    "df2"
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  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cb6b84ac",
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       "      ID   Stay   Age  Risk  Cult   Xray  Beds  MS  Reg  Cen  Nurses   AFS\n",
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       "1      2   8.82  58.2   1.6   3.8   51.7    80   0    2   51      52  40.0\n",
       "2      3   8.34  56.9   2.7   8.1   74.0   107   0    3   82      54  20.0\n",
       "3      4   8.95  53.7   5.6  18.9  122.8   147   0    4   53     148  40.0\n",
       "4      5  11.20  56.5   5.7  34.5   88.9   180   0    1  134     151  40.0\n",
       "..   ...    ...   ...   ...   ...    ...   ...  ..  ...  ...     ...   ...\n",
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       "109  110   9.50  49.3   5.8  42.0   70.9    98   0    3   68      46  22.9\n",
       "110  111   7.70  56.9   4.4  12.2   67.9   129   0    4   85     136  62.9\n",
       "111  112  17.94  56.2   5.9  26.4   91.8   835   1    1  791     407  62.9\n",
       "112  113   9.41  59.5   3.1  20.6   91.7    29   0    3   20      22  22.9\n",
       "\n",
       "[113 rows x 12 columns]"
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   "source": [
    "#3a\n",
    "def change(x):\n",
    "    if x==2:\n",
    "        return 0\n",
    "    else:\n",
    "        return x\n",
    "    \n",
    "df2['MS']=df2['MS'].apply(change)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
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   "id": "f995226d",
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       "                            \n",
       "var gd = document.getElementById('55c82862-c075-4c16-aa87-a60200414410');\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3b\n",
    "import plotly.express as px\n",
    "\n",
    "matrix = df2[['Risk','Stay','Age','Xray']]\n",
    "fig = px.scatter_matrix(matrix)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c82f06fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x=['Stay','Age','Xray']\n",
    "for i in x:\n",
    "    sns.scatterplot(x=i, y='Risk',data=df2,hue='MS')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d4487dd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.368\n",
      "Model:                            OLS   Adj. R-squared:                  0.345\n",
      "Method:                 Least Squares   F-statistic:                     15.73\n",
      "Date:                Thu, 13 Oct 2022   Prob (F-statistic):           3.57e-10\n",
      "Time:                        18:19:31   Log-Likelihood:                -167.05\n",
      "No. Observations:                 113   AIC:                             344.1\n",
      "Df Residuals:                     108   BIC:                             357.7\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.8574      1.324      0.647      0.519      -1.768       3.482\n",
      "x1             0.2888      0.063      4.591      0.000       0.164       0.414\n",
      "x2            -0.0181      0.024     -0.749      0.456      -0.066       0.030\n",
      "x3             0.0200      0.006      3.458      0.001       0.009       0.031\n",
      "x4             0.2878      0.307      0.938      0.350      -0.320       0.896\n",
      "==============================================================================\n",
      "Omnibus:                        0.712   Durbin-Watson:                   1.864\n",
      "Prob(Omnibus):                  0.700   Jarque-Bera (JB):                0.800\n",
      "Skew:                           0.068   Prob(JB):                        0.670\n",
      "Kurtosis:                       2.611   Cond. No.                     1.29e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.29e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.399\n",
      "Model:                            OLS   Adj. R-squared:                  0.359\n",
      "Method:                 Least Squares   F-statistic:                     9.952\n",
      "Date:                Thu, 13 Oct 2022   Prob (F-statistic):           1.75e-09\n",
      "Time:                        18:19:31   Log-Likelihood:                -164.23\n",
      "No. Observations:                 113   AIC:                             344.5\n",
      "Df Residuals:                     105   BIC:                             366.3\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.9720      1.396      0.696      0.488      -1.795       3.739\n",
      "x1             0.2933      0.071      4.142      0.000       0.153       0.434\n",
      "x2            -0.0257      0.025     -1.031      0.305      -0.075       0.024\n",
      "x3             0.0231      0.007      3.483      0.001       0.010       0.036\n",
      "x4            -5.9751      4.614     -1.295      0.198     -15.124       3.174\n",
      "x5            -0.1473      0.159     -0.926      0.357      -0.463       0.168\n",
      "x6             0.1891      0.099      1.901      0.060      -0.008       0.386\n",
      "x7            -0.0226      0.014     -1.612      0.110      -0.050       0.005\n",
      "==============================================================================\n",
      "Omnibus:                        0.878   Durbin-Watson:                   1.811\n",
      "Prob(Omnibus):                  0.645   Jarque-Bera (JB):                0.980\n",
      "Skew:                           0.144   Prob(JB):                        0.613\n",
      "Kurtosis:                       2.647   Cond. No.                     4.62e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.62e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "#3C\n",
    "#The first model\n",
    "y=df2['Risk'].values\n",
    "x1=df2['Stay'].tolist()\n",
    "x2=df2['Age'].tolist()\n",
    "x3=df2['Xray'].tolist()\n",
    "x4=df2['MS'].tolist()\n",
    "X1=np.array([[1]*len(y),x1,x2,x3,x4]).T\n",
    "X1\n",
    "\n",
    "model_1 = sm.OLS(y, X1).fit()\n",
    "print(model_1.summary())\n",
    "\n",
    "#The second model\n",
    "def cross_variable(x):\n",
    "    cross=[]\n",
    "    for i in range(len(y)):\n",
    "        cross.append(x[i]*x4[i])\n",
    "    return cross\n",
    "\n",
    "x5=cross_variable(x1)\n",
    "x6=cross_variable(x2)\n",
    "x7=cross_variable(x3)\n",
    "X2=np.array([[1]*len(y),x1,x2,x3,x4,x5,x6,x7]).T\n",
    "model_2= sm.OLS(y, X2).fit()\n",
    "print(model_2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8ba8108c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid         ssr  df_diff   ss_diff         F   Pr(>F)\n",
      "0     108.0  127.243169      0.0       NaN       NaN      NaN\n",
      "1     105.0  121.058094      3.0  6.185075  1.788213  0.15393\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.api import anova_lm\n",
    "print(anova_lm(model_1,model_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c055bade",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0923134",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cf5f91b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
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}