Go to the tab labeled Torque Height. This tab shows the results of the model using Torque and Height to predict AvgMPG that was built in Assignment 3. a. - Copy the Residuals to a new sheet. - From the Assignment 3 Data tab, copy the numeric variables that were not used (all variables from Price to Wheelbase, excluding Torque and Height). - Construct a correlation table (matrix). Paste a copy into your assignment. b. You should find that Cylinders has the highest correlation with residuals. - Copy Torque, Height, Cylinders and AvgMPG into a new sheet. - Use Regression to fit the model AvgMPG = intercept + coeff1*Torque + coeff2*Height + coeff3*Cylinders. - Request Residuals and Residual Plot for this model. - Copy the Regression Summary Output into your assignment. Do not copy the Residual Output or the Residual Chart. c. Write out the estimated regression formula. d. In Assignment \#3, it was suggested that the strong correlation among Power, Torque, Displacement and Cylinders would give rise to issues of multicollinearity, if more than one was included in the model. Comment on the degree to which the coefficient for Torque has changed after Cylinders was added. Did it shift by much more than would have been expected based upon the standard error of the estimate in Assignment 3? The standard error reflects of how much error there might have been in the estimate. e. Has including Cylinders improved the "performance" of the model predictions? This is not simply an opinion. Back up your conclusion using model performance measures