3. 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? Dep. Variable: mpg OLS Regression Results ============ R-squared: 0.844 Model: OLS Adj. R-squared: 0.833 Method: Least Squares F-statistic: 73.18 Date: Fri, 07 Apr 2023 Prob (F-statistic): 1.25e-11 Time: No. Observations: 00:12:17 Log-Likelihood: -68.713 30 AIC: 143.4 Df Residuals: 27 BIC: 147.6 Df Model: Covariance Type: 2 nonrobust coef std err t p>|t| [0.025 0.975] Intercept 37.5278 1.573 23.856 0.000 34.300 40.756 wt -3.8739 0.617 -6.283 0.000 -5.139 -2.609 hp -0.0324 0.009 -3.693 0.001 -0.050 -0.014 ==== Omnibus: 5.587 Durbin-Watson: 2.694 Prob(Omnibus): 0.061 Jarque-Bera (JB): 4.265 Skew: 0.908 Prob(JB): 0.119 Kurtosis: 3.339 Cond. No. 582. ===== ===