Below is the multiple regression model: OLS Regression Results Dep. Variable: mpg R- squared: 0.835 Model : OLS Adj . R-squared: 0. 823 Method : Least Squares F-statistic: 68.28 Date : Mon, 04 Oct 2021 Prob (F-statistic) : 2.75e-11 Time : 16:03:36 Log-Likelihood: -66.988 No. Observations : 30 AIC : 140.0 Df Residuals: 27 BIC: 144.2 Df Model : 2 Covariance Type: nonrobust 2222222222229282828282828: coef std err t P> | t| [0. 025 0.975] Intercept 36.0778 1.535 23.500 0.000 32.928 39.228 wit -3.5870 0. 591 -6.071 0.000 -4.799 -2.375 hp -0. 0312 0. 008 -3.735 0. 001 -0.048 -0. 014 Omnibus : 5.485 Durbin-Watson : 1.183 Prob (Omnibus ) : 0. 064 Jarque-Bera (JB) : 3.851 Skew: 0. 816 Prob ( JB) : 0. 146 Kurtosis : 3.648 Cond. No. 607. Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. 1) The least squares regression line is: Y = 36. 0778 - 3. 5870X1 - 0. 0312X21. Review your peer's multiple regression model (#3 in their initial post). What is the predicted value of miles per gallon for a car that has 2.78 (2,780 lbs) weight and 225 horsepower? Suppose that this car achieves 18 miles per gallon, what is the residual based on this actual value and the value that is predicted using the regression equation