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‎07_Simple_and_Multiple_Regression.ipynb

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"name": "07 Simple and Multiple Regression.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"authorship_tag": "ABX9TyNhGP0GF9c1lVdgMRwuSiRi",
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"authorship_tag": "ABX9TyM4ImDR63Bml//QYzj8HH0p",
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"include_colab_link": true
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},
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"kernelspec": {
@@ -2881,11 +2881,259 @@
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "NVhGdOXIsOGa",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 89
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},
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"outputId": "b0996ed2-74da-4cf0-ca23-6ea1a0920589"
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},
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"source": [
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"base_model_properties_2"
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],
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"execution_count": 51,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"[('Jarque-Bera', 472.99120337658803),\n",
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" ('Chi^2 two-tail prob.', 1.9555331946581146e-103),\n",
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" ('Skew', 1.6859804491070154),\n",
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" ('Kurtosis', 7.5494043489051235)]"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 51
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Hb228jU0nmJE",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"outputId": "13f57e62-5df8-4630-edb2-855fdae0977e"
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},
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"source": [
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"# Overall Comparision\n",
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"print(\"ALL MODEL COMPARISION\")\n",
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"\n",
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"# Normality of the residuals\n",
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"# Jarque-Bera test:\n",
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"print(\"Normality of the residuals\")\n",
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"print(\"Jarque-Bera test : \")\n",
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"print(\"JB, ideal is close to 0\")\n",
2937+
"print(\"Probability(JB), less than 0.05\")\n",
2938+
"print(\"Base Model\")\n",
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"print(base_model_properties_1[0])\n",
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"print(base_model_properties_1[1])\n",
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"print(\"Model 1\")\n",
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"print(model_properties_1[0])\n",
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"print(model_properties_1[1])\n",
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"print(\"Model 2\")\n",
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"print(base_model_properties_2[0])\n",
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"print(base_model_properties_2[1])\n",
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"print(\"Model 3\")\n",
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"print(base_model_properties_3[0])\n",
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"print(base_model_properties_3[1])\n",
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"\n",
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"# Skewness and Kurtosis\n",
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"print(\"\\n\")\n",
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"print(\"Skewness and Kurtosis of Residual\")\n",
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"print(\"Skewness, ideal is -1 to 1\")\n",
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"print(\"Kurtosis, ideal value is 3\")\n",
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"\n",
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"# Omni test:\n",
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"print(\"\\n\")\n",
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"print(\"Normality of the Residual also\")\n",
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"print(\"Omni Test : \")\n",
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"print(\"Omni, ideal is close to 0\")\n",
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"print(\"Probability(Omni), less than 0.05\")\n",
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"print(\"Base Model\")\n",
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"name = ['Chi^2', 'Two-tail probability']\n",
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"test = sms.omni_normtest(base_model.resid)\n",
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"print(pd.DataFrame(lzip(name, test),columns = ['Test','Values']).round(2))\n",
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"print(\"Model 1\")\n",
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"name = ['Chi^2', 'Two-tail probability']\n",
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"test = sms.omni_normtest(Model_1.resid)\n",
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"print(pd.DataFrame(lzip(name, test),columns = ['Test','Values']).round(2))\n",
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"print(\"Model 2\")\n",
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"name = ['Chi^2', 'Two-tail probability']\n",
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"test = sms.omni_normtest(Model_2.resid)\n",
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"print(pd.DataFrame(lzip(name, test),columns = ['Test','Values']).round(2))\n",
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"print(\"Model 3\")\n",
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"name = ['Chi^2', 'Two-tail probability']\n",
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"test = sms.omni_normtest(Model_3.resid)\n",
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"print(pd.DataFrame(lzip(name, test),columns = ['Test','Values']).round(2))\n",
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"\n",
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"# Multicollinearity\n",
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"print(\"\\n\")\n",
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"print(\"Multicollinearity\")\n",
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"print(\"Ideal Condition Number will be less than 30\")\n",
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"print(\"Base Model, Condition number:\",np.linalg.cond(base_model.model.exog).round(2))\n",
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"print(\"Model 1, Condition number:\",np.linalg.cond(Model_1.model.exog).round(2))\n",
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"print(\"Model 2, Condition number:\",np.linalg.cond(Model_2.model.exog).round(2))\n",
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"print(\"Model 3, Condition number:\",np.linalg.cond(Model_3.model.exog).round(2))\n",
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"\n",
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"# Adjusted R Square\n",
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"print(\"\\n\")\n",
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"print(\"Model Performance\")\n",
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"print(\"Adjusted R Square, ideal is close to 1\")\n",
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"print(\"Base Model, Adjusted R Square : \",base_model.rsquared_adj.round(2))\n",
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"print(\"Model 1, Adjusted R Square : \",Model_1.rsquared_adj.round(2))\n",
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"print(\"Model 2, Adjusted R Square : \",Model_2.rsquared_adj.round(2))\n",
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"print(\"Model 3, Adjusted R Square : \",Model_3.rsquared_adj.round(2))\n",
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"\n",
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"# F Statistics\n",
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"print(\"\\n\")\n",
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"print(\"Significance of Independent Variables\")\n",
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"print(\"Probability(F Stat), less than 0.05 suggests independent variables are important\")\n",
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"print(\"Base Model, Probability of F Statistics : \",base_model.f_pvalue.round(2))\n",
3003+
"print(\"Model 1, Probability of F Statistics : \",Model_1.f_pvalue.round(2))\n",
3004+
"print(\"Model 2, Probability of F Statistics : \",Model_2.f_pvalue.round(2))\n",
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"print(\"Model 3, Probability of F Statistics : \",Model_3.f_pvalue.round(2))\n",
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"\n",
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"# Durbin Watson Test\n",
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"print(\"\\n\")\n",
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"print(\"Homoscedasticity of Error\")\n",
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"print(\"Durbin Watson, ideal is between 1 to 2\")\n",
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"print(\"Base Model, Durbin Watson Value : 0.78\")\n",
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"print(\"Model 1, Durbin Watson Value : 0.78\")\n",
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"print(\"Model 2, Durbin Watson Value : 2.1\")\n",
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"print(\"Model 3, Durbin Watson Value : 2.07\")"
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],
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"execution_count": 56,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"ALL MODEL COMPARISION\n",
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"Normality of the residuals\n",
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"Jarque-Bera test : \n",
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"JB, ideal is close to 0\n",
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"Probability(JB), less than 0.05\n",
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"Base Model\n",
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"('Jarque-Bera', 3084.310178901069)\n",
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"('Chi^2 two-tail prob.', 0.0)\n",
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"Model 1\n",
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"('Jarque-Bera', 2125.548489196517)\n",
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"('Chi^2 two-tail prob.', 0.0)\n",
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"Model 2\n",
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"('Jarque-Bera', 472.99120337658803)\n",
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"('Chi^2 two-tail prob.', 1.9555331946581146e-103)\n",
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"Model 3\n",
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"('Jarque-Bera', 452.9473484300142)\n",
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"('Chi^2 two-tail prob.', 4.402840898839639e-99)\n",
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"\n",
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"\n",
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"Skewness and Kurtosis of Residual\n",
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"Skewness, ideal is -1 to 1\n",
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"Kurtosis, ideal value is 3\n",
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"\n",
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"\n",
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"Normality of the Residual also\n",
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"Omni Test : \n",
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"Omni, ideal is close to 0\n",
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"Probability(Omni), less than 0.05\n",
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"Base Model\n",
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" Test Values\n",
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"0 Chi^2 277.01\n",
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"1 Two-tail probability 0.00\n",
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"Model 1\n",
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" Test Values\n",
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"0 Chi^2 195.55\n",
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"1 Two-tail probability 0.00\n",
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"Model 2\n",
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" Test Values\n",
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"0 Chi^2 134.24\n",
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"1 Two-tail probability 0.00\n",
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"Model 3\n",
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" Test Values\n",
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"0 Chi^2 128.77\n",
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"1 Two-tail probability 0.00\n",
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"\n",
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"\n",
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"Multicollinearity\n",
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"Ideal Condition Number will be less than 30\n",
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"Base Model, Condition number: 8125.62\n",
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"Model 1, Condition number: 8118.28\n",
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"Model 2, Condition number: 103.55\n",
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"Model 3, Condition number: 89.45\n",
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"\n",
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"\n",
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"Model Performance\n",
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"Adjusted R Square, ideal is close to 1\n",
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"Base Model, Adjusted R Square : 0.95\n",
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"Model 1, Adjusted R Square : 0.95\n",
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"Model 2, Adjusted R Square : 0.91\n",
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"Model 3, Adjusted R Square : 0.91\n",
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"\n",
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"\n",
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"Significance of Independent Variables\n",
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"Probability(F Stat), less than 0.05 suggests independent variables are important\n",
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"Base Model, Probability of F Statistics : 0.0\n",
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"Model 1, Probability of F Statistics : 0.0\n",
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"Model 2, Probability of F Statistics : 0.0\n",
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"Model 3, Probability of F Statistics : 0.0\n",
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"\n",
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"\n",
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"Homoscedasticity of Error\n",
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"Durbin Watson, ideal is between 1 to 2\n",
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"Base Model, Durbin Watson Value : 0.78\n",
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"Model 1, Durbin Watson Value : 0.78\n",
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"Model 2, Durbin Watson Value : 2.1\n",
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"Model 3, Durbin Watson Value : 2.07\n"
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],
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"name": "stdout"
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}
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]
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},
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"cell_type": "code",
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"id": "_cnBy2F8rhjS",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 35
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"outputId": "93662ec1-8635-4a90-b678-1c075b6af894"
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},
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"source": [
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"base_model_properties_1[0]"
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],
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"execution_count": 50,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"('Jarque-Bera', 3084.310178901069)"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 50
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"id": "6UmVmM89r7Ro",
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"colab_type": "code",
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"colab": {}
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"source": [

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