Consider the hypothetical example of the San Francisco Bread Company, a San Francisco-based chain of bakery/cafes. San Francisco Bread Company 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 the attached Table 1.
The following regression equation was fit to these data:
Qi = b0 + b1Pi + b2Pxi + b3Adi + b4Ii + uit.
Where: Q is the number of meals served,
P is the average price per meal (customer ticket amount, in dollars),
Px 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 service area,
ui is a residual (or disturbance) term.
The subscript indicates the regional market (i = 1,…, 30) from which the observation was taken. Least squares estimation of the regression equation on the basis of the 30 data cross sectional observations resulted in the estimated regression coefficients and other statistics as shown in Table 2.
A. Describe the economic meaning for each individual independent variable included in the San Francisco demand equation.
B. Interpret the coefficient of determination (R2) for the San Francisco Bread Company demand equation.
C. What are expected (average) unit sales and sales revenue in a typical market?
D. Describe the level statistical significance for each individual independent variable included in the San Francisco Bread Company demand equation.
E. Interpret each coefficient and its impact on the dependent variable.
F. Conduct an F-test for the set of coefficients in the equation to determine if they are significant at the 95 and 99 percent levels.