Forecasting monthly car and truck sales. Forecasts of automotive vehicle sales in the United States provide the basis for financial and strategic planning at large automotive corporations. The following forecasting model was developed for Yt, total monthly passenger car and light truck sales (in thousands): E1Yt2 = b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 where x1 = average monthly retail price of regular gasoline, x2 = annual percentage change in GDP per quarter, x3 = monthly consumer confidence index, x4 = total number of vehicles scrapped (millions) per month, and x5 = vehicle seasonality. The model was fit to monthly data collected over a 12-year period (i.e., n = 144 months), with the following results: R2 = .856, Durbin-Watson d = 1.01.

a. Is there sufficient evidence to indicate that the overall model contributes information for the prediction of monthly passenger car and light truck sales? Test using a = .05.

b. Is there sufficient evidence to indicate that the regression errors are positively correlated? Test using a = .05.

c. Comment on the validity of the inference concerning model adequacy in light of the result of part b.