OLS Regression Results Dep. Variable: mpg R- squared: 0.816 Model: OLS Adj. R-squared: 0. 803 Method : Least Squares F-statistic: 59.92 Date : Mon, 01 Aug 2022 Prob (F-statistic) : 1. 18e-10 Time : 21:36:01 Log-Likelihood : -69.989 No. Observations : 30 AIC: 146.0 Of Residuals : 27 BIC: 150.2 Of Model : 2 Covariance Type: nonrobust coef std err t P>It/ [0. 025 0.975] Intercept 36.5459 1.736 21.053 0.000 32.984 40. 108 wt -3. 6186 0. 684 -5.293 0.000 -5.021 -2. 216 hp -0.0336 0. 009 -3.608 0. 001 -0.053 -0. 014 Omnibus : 6.789 Durbin-Watson: 2. 161 Prob (Omnibus ) : 0. 034 Jarque-Bera (JB) : 5. 248 Skew: 0.983 Prob( JB) : 0. 0725 Kurtosis : 3.577 Cond. No. 615. Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified