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haridutt12/automobile_price_insight.ipython

Created May 30, 2020 17:10
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automobile_price_insight.ipython
In [99]: import pandas as pd
In [100]: data = pd.read_csv('/home/haridutt/Downloads/automobile.csv')
In [101]: X = data.loc[:, ['type', 'engine_s', 'horsepow', 'mpg']]
In [102]: y = data.iloc[:, 1 ]
In [103]: X_opt = X
In [104]: regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
In [105]: regressor_OLS.summary()
Out[105]:
<class 'statsmodels.iolib.summary.Summary'>
"""
OLS Regression Results
=======================================================================================
Dep. Variable: price R-squared (uncentered): 0.943
Model: OLS Adj. R-squared (uncentered): 0.941
Method: Least Squares F-statistic: 623.1
Date: Sat, 30 May 2020 Prob (F-statistic): 3.83e-93
Time: 22:37:31 Log-Likelihood: -533.55
No. Observations: 156 AIC: 1075.
Df Residuals: 152 BIC: 1087.
Df Model: 4
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
type -2.1457 1.443 -1.487 0.139 -4.996 0.705
engine_s -3.6465 1.076 -3.389 0.001 -5.772 -1.521
horsepow 0.2535 0.019 13.571 0.000 0.217 0.290
mpg -0.3319 0.060 -5.506 0.000 -0.451 -0.213
==============================================================================
Omnibus: 63.094 Durbin-Watson: 1.309
Prob(Omnibus): 0.000 Jarque-Bera (JB): 214.094
Skew: 1.554 Prob(JB): 3.24e-47
Kurtosis: 7.825 Cond. No. 500.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
"""
In [106]: X_opt = X.iloc[:, 1:4]
In [107]: X_opt
Out[107]:
engine_s horsepow mpg
0 1.8 140 25
1 3.2 225 25
2 3.2 225 22
3 3.5 210 22
4 1.8 150 24
.. ... ... ...
151 1.9 160 25
152 2.4 168 25
153 2.4 168 25
154 2.3 236 23
155 2.9 201 24
[156 rows x 3 columns]
In [108]: regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
In [109]: regressor_OLS.summary()
Out[109]:
<class 'statsmodels.iolib.summary.Summary'>
"""
OLS Regression Results
=======================================================================================
Dep. Variable: price R-squared (uncentered): 0.942
Model: OLS Adj. R-squared (uncentered): 0.941
Method: Least Squares F-statistic: 823.6
Date: Sat, 30 May 2020 Prob (F-statistic): 3.67e-94
Time: 22:39:33 Log-Likelihood: -534.68
No. Observations: 156 AIC: 1075.
Df Residuals: 153 BIC: 1085.
Df Model: 3
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
engine_s -4.2411 1.003 -4.229 0.000 -6.222 -2.260
horsepow 0.2597 0.018 14.200 0.000 0.224 0.296
mpg -0.3254 0.060 -5.390 0.000 -0.445 -0.206
==============================================================================
Omnibus: 67.561 Durbin-Watson: 1.326
Prob(Omnibus): 0.000 Jarque-Bera (JB): 242.693
Skew: 1.655 Prob(JB): 1.99e-53
Kurtosis: 8.137 Cond. No. 325.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
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