1. Write the multiple regression equation for miles per gallon as the response variable. Use weight and horsepower as predictor variables. See Step 5 in the Python script. How might the car rental company use this model?

 OLS Regression Results ============================================================================== Dep. Variable: mpg R-squared: 0.792 Model: OLS Adj. R-squared: 0.777 Method: Least Squares F-statistic: 51.40 Date: Tue, 15 Jun 2021 Prob (F-statistic): 6.22e-10 Time: 15:56:02 Log-Likelihood: -70.457 No. Observations: 30 AIC: 146.9 Df Residuals: 27 BIC: 151.1 Df Model: 2 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ Intercept 37.2458 1.876 19.851 0.000 33.396 41.096 wt -3.9414 0.724 -5.441 0.000 -5.428 -2.455 hp -0.0310 0.009 -3.299 0.003 -0.050 -0.012 ============================================================================== Omnibus: 5.729 Durbin-Watson: 1.727 Prob(Omnibus): 0.057 Jarque-Bera (JB): 4.443 Skew: 0.930 Prob(JB): 0.108 Kurtosis: 3.314 Cond. No. 651. ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.