Question: OLS Regression Results Dep. .Variable: mpg R-squared: 0. 769 Model: OLS Adj. R-squared: 0. 761 Method : Least Squares F-statistic: 93. 10 Date : Tue,

OLS Regression Results Dep. .Variable: mpg R-squared: 0. 769 Model: OLS Adj. R-squared: 0. 761 Method : Least Squares F-statistic: 93. 10 Date : Tue, 26 Jul 2022 Prob (F-statistic ) : 2.11e-10 Time : 11 : 51:45 Log-Likelihood: -74. 718 No. Observations : 30 AIC: 153.4 Of Residuals: 28 BIC: 156.2 Df Model : 1 Covariance Type: nonrobust coef std err t P> | t| [0. 025 0.975] Intercept 37.5328 1.879 19.976 0.000 33. 684 41.382 wt -5. 3648 0.556 -9. 649 0. 000 -6.504 -4. 226 ==: Omnibus : 2.828 Durbin-Watson: 2. 185 Prob (Omnibus ) : 0.243 Jarque-Bera (JB) : 2. 309 Skew : 0. 672 Prob ( JB ) : 0. 315 Kurtosis : 2. 795 Cond. No. 12.4 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

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