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A. Coefficient estimates for the P, Px, Ad, and I independent X variables are statistically significant at the 99% confidence level. Price of the product itself (P) has the predictably negative influence on the quantity demanded, whereas the effects of competitor price (Px), advertising (Ad) and household disposable income (I) are positive as expected. The chance of finding such large t statistics is less than 1% if, in fact, there were no relation between each variable and quantity.

B. The R2 = 90.4% obtained by the model means that 90.4% of demand variation is explained by the underlying variation in all four independent variables. This is a relatively high level of explained variation and implies an attractive level of explanatory power. Moreover, as shown in the graph of actual and fitted (estimated) demand, the multiple regression model closely tracks week-by-week changes in demand with no worrisome divergences between actual and estimated demand over time. This means that this regression model can be used to forecast demand in similar markets under similar conditions.

C. Notice that each prospective market displays characteristics similar to those of markets used to estimate the regression model described here. Thus, the regression model estimated previously can be used to forecast demand in each regional market. Forecast results are as follows:

Regional Forecast Price per Competitor Advertising, Household Forecast Market Case, P Price, Px Ad Income, I Demand, Q

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