Consider the regression below (below) that was estimated on weekly data over a 2-year period on a sample of Kroger stores for Pepsi carbonated soft drinks. The dependent variable is the log of Pepsi volume per MM ACV. There are 53 stores in the dataset (data were missing for some stores in some weeks). Please answer the following questions about the regression output. Model Summary (b) Model 1 Model R 1 .869(a) R Square .754 Sum of Squares Regression 2881.089 10 Total a Predictors: (Constant), Mass stores in trade area, Labor Day dummy, Pepsi advertising days, Store traffic, Memorial Day dummy, Pepsi display days, Coke advertising days, Log of Pepsi price, Coke display days, Log of Coke price b Dependent Variable: Log of Pepsi volume/MM ACV ANOVA(b) df Residual 937.695 5524 Adjusted R Square 3818.784 5534 .754 Mean Square Std. Error of the Estimate .4120 .170 F Sig. 288.109 1697.262 .000(a) a Predictors: (Constant), Mass stores in trade area, Labor Day dummy, Pepsi advertising days, Store traffic, Memorial Day dummy, Pepsi display days, Coke advertising days. Log of Pepsi price, Coke display days, Log of Coke price b Dependent Variable: Log of Pepsi volume/MM ACV Coefficients(a) (Constant) Model Log of Pepsi price Log of Coke price Pepsi advertising days Coke advertising days Pepsi display days Coke display days Labor Day dummy Memorial Day dummy Store traffic Mass stores in trade area Unstandardized Coefficients B 7.79429 -3.34665 .65877 .00173 -00009 ,00011 -00299 .27190 .21295 .00000 -00910 a Dependent Variable: Log of Pepsi volume/MM ACV Std. Error .06249 .03483 .03170 .00020 .00018 .00021 .00020 .04167 .04269 .00000 .00026 Standardized Coefficients Betal -.739 .181 .065 -.004 .004 -123 .045 .036 .023 -238 t 124.721 -96.091 20.784 8.644 -502 .546 -14.766 6.525 4.988 3.367 -35.161 Sig. .000 .000 .000 .000 .616 .585 .000 .000 .000 .001 .000 Co linearity Statistics Tolerance .751 .587 .784 .689 .656 .646 .923 .834 .961 .968 VIF 1.332 1.703 1.275 1.450 1.525 1.549 1.083 1.199 1.040 1.033 Questions (a) Comment on the goodness of fit and significance of the regression and of individual variables. What does the ANOVA table reveal? (b) Write out the equation and interpret the meaning of each of the parameters. (c) What is the price elasticity? The cross-price elasticity with respect to Coke price? Are these results reasonable? Explain. (d) What do the results tell you about the effectiveness of Pepsi and Coke display and advertising? (e) What are the 3 most important variables? Explain how you arrived at this conclusion. (f) What is collinearity? Is collinearity a problem for this regression? Explain. If it is a problem, what action would you take to deal with it? (g) What changes to this regression equation, if any, would you recommend? Explain