1. Write the multiple regression equation for miles per gallon as the response variable. Use weight and horsepower as predictor variables.

OLS Regression Results ============================================================================== Dep. Variable: mpg R-squared: 0.828 Model: OLS Adj. R-squared: 0.815 Method: Least Squares F-statistic: 64.88 Date: Tue, 11 Feb 2020 Prob (F-statistic): 4.88e-11 Time: 21:23:39 Log-Likelihood: -69.581 No. Observations: 30 AIC: 145.2 Df Residuals: 27 BIC: 149.4 Df Model: 2 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ Intercept 37.1687 1.602 23.204 0.000 33.882 40.455 wt -3.8486 0.636 -6.051 0.000 -5.154 -2.544 hp -0.0310 0.009 -3.319 0.003 -0.050 -0.012 ============================================================================== Omnibus: 4.982 Durbin-Watson: 2.110 Prob(Omnibus): 0.083 Jarque-Bera (JB): 3.688 Skew: 0.845 Prob(JB): 0.158 Kurtosis: 3.312 Cond. No. 559. ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.