Goodbuy's management is interested in the variation in the Sales for each supermarket for the financial year (Sales $m). Of all the potential factors, the following variables have been chosen as the factors most likely to influence the sales for each supermarket. Sales Sm: Age (Yrs): Adv.$'000: Car Spaces: HomeDel: Below is the computer output showing the result of a regression analysis between sales and Age of Store, Advertising, Car Spaces and Home Delivery as the independent variables (Note: Some output has been omitted). SUMMARY OUTPUT Regression Statistics ANOVA Sales for each supermarket for the financial year ($ million) The number of full years of operation of the store Advertising and promotional expenses for the financial year (in thousand dollars) Multiple R R Square Adjusted R Square Standard Error Observations Regression Residual Total The number of parking spaces available to the supermarket Whether or not the supermarket home delivers for checkout customers. 0:No 1:Yes Intercept Age (Yrs) Adv.$'000 Car Spaces HomeDel Residuals 5.000 0.879406531 0.773355847 0.767103595 1.723335567 0.000 df -5.000 150 4 145 149 SS F MS 1469.409 367.352 123.692 430.633 2.970 1900.042 Coefficients Standard Error t Stat 0.480 0.026 0.002 0.008 0.329 4.322 -0.049 0.038 0.039 1.541 Age (Yrs) Residual Plot P-value 9.005 0.000 -1.881 0.062 15.252 0.000 4.845 0.000 4.676 0.000 (a) How well does the model explain sales? < (b) Is the model overall significant? < (c) Which of the four independent variables are significant in this relationship? Justify briefly (d) Based on the residual plot below, does there appear to be any problems with the regression model? < Age (Yrs) 30 Significance F 0.000 ( Lower 95% 3.374 -0.101 0.033 0.023 0.889 Upper 95% 5.271 0.003 0.043 0.055 2.192 (e) Estimate the sales of a store that is 10 years old, spends $75,000 on advertising, has 89 car spaces and does not offer home delivery.