1. 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?
2. How do the plots and correlation coefficients of your peers compare with yours?
3. Would you recommend this regression model to the car rental company? Why or why not?

21/le/1192258/discussions/threads/22845763/View Hello Class. After utilizing python to assist in creating a multiple regression linear we can attest that both independent variables weight (wt) and horsepower(hp) once compared to miles per gallon (mpg) are linear and with a negative direction. In other words as either the weight increases or the horsepower, the fuel efficiency decreased as expected. MPG against Weight 10 15 20 25 30 35 10 45 50 MPG against Horsep 25 MPG 150 250 Another aspect we can conclude from the statistical model is the coefficient of correlation of the independent variables (wt and hp) mpg wt hp mpg 1.000000 -0.858263 -0.806390 wt -0858263 1.000000 0.711008 ap -0.806390 0.711008 1.000000\fy = 37.5006 - 3 6818x1 -0.0390x2. x1 = weight x2 = horsepower Based on the above equation, the rental company would be able to utilize a multiple linear regression to predict their car's fuel efficiency miles per gallon, based on the car's weight and horsepower Moreover, would allow analyzing their inventory and how those variables can influence their sales rents. -Luiz Walters