Given the highly competitive nature of the restaurant industry, individual companies cautiously guard operating information for individual outlets. As a result, there is not any publicly available data that can be used to estimate important operating relationships. To see the process that might be undertaken to develop a better understanding of store location decisions, consider the hypothetical example of The San Francisco Bread Co., a San Francisco-based chain of bakery-cafes. San Francisco has initiated an empirical estimation of customer traffic at 30 regional locations to help the firm formulate pricing and promotional plans for the coming year. Annual operating data for the 30 outlets appear in Table 3.5.

The following regression equation was fit to these data:

                                            Q_i = b₀ + b₁P_i + b₂P_xi + b₃Ad_i + b₄I_i + u_it.

Q is the number of meals served, P is the average price per meal (customer ticket amount, in dollars), P_x is the average price charged by competitors (in dollars), Ad is the local advertising budget for each outlet (in dollars), I is the average income per household in each outlet’s immediate service area, and u_i is a residual (or disturbance) term. The subscript i indicates the regional market from which the observation was taken. Least squares estimation of the regression equation on the basis of the 30 data observations resulted in the estimated regression coefficients and other statistics shown in Table 3.6.

A. Describe the economic meaning and statistical significance of each individual independent variable included in the San Francisco demand equation.

B. Interpret the coefficient of determination (R²) for the San Francisco demand equation.

C. What are expected unit sales and sales revenue in a typical market?

D. To illustrate use of the standard error of the estimate statistic, derive the 95 percent confidence interval for expected unit sales and total sales revenue in a typical market.