OLS Regression Results Dep. Variable: mpg R-squared: 0.817 Model : OLS Adj. R-squared: 0.804 Method : Least Squares F-statistic: 60.33 Date: Mon, 28 Mar 2022 Prob (F-statistic): 1. 09e-10 Time: 10:31:50 Log-Likelihood: -70.106 No. Observations: 30 AIC : 146.2 Df Residuals: 27 BIC : 150.4 Df Model : 2 Covariance Type : nonrobust coef std err t P> t [0. 025 0.975] Intercept 37.4992 1.739 21.560 0.600 33.930 41.068 wt -3.9248 0. 695 -5.645 0.600 -5.351 -2.498 hp -0. 0320 0. 009 -3.452 0.002 -0. 051 -0.013 Omnibus : 4.357 Durbin-Watson: 1.958 Prob (Omnibus ) : 0. 113 Jarque-Bera (JB) : 3.245 Skew: 0.800 Prob(JB) : 0.197 Kurtosis : 3. 181 Cond. No. 610