Question 12 (30 points) You are provided with the results of the linear regression model on the dataset on cars with the following variables: Data columns (total 12 columns) : make 74 non-null object price 74 non-null int32 mpg 74 non-null int32 rep78 74 non-null int32 headroom 74 non-null float32 trunk 74 non-null int32 weight 74 non-null int32 length 74 non-null int32 turn 74 non-null int32 displacement 74 non-null int32 gear ratio 74 non-null float32 foreign 74 non-null int16 dtypes: float32 (2), int16(1), int32(8), object(1)In your model you want to see how weight and brand nationality (domestic or foreign) significantly effects mile per galloon. Your OLS regression results look like this: Dep, Variable: mpg K-squared; 0/683 Model: OLS Adj. H-squared: 0.053 Method: Least Squares F-Mallvici 80.75 Date: Thu, 26 Dec 2018 Prob (F-statistics 1.760-17 Timer 13:31:18 Log-Lackhood -194. 18 No. Observations 74 ATC: 394.4 If Residuals 71 401.3 Of Model: Covariance Type: Inonrobust Phil 0.975 Intercept 41 6787 2.106 19.247 37.362 Color inNT.I] 1.4500 1.076 1.633 0.130 -2.796 0.0038 0.001 -10.340 -D.DOR Omnibus: 37 830 Durhin-Watson: 2.471 Probs Omnilans): 0.000 Jarque-Hera (JR): 82. 107 Skew 1.727 ProbJIG 9.080-21 7:236 Cond. Na. 1.810+04Provide a brief explanation of each model diagnostic metrics. Explain the results and evaluate the model