Gibson Products is using multiple regression analysis to try to relate a set of independent variables to the number of daily customer inquiries the company receives on its website. Gibson wants to include “season of the year” as one of the variables in the model. Since “season of the year” has four categories— summer, fall, winter, and spring— Gibson has defined three dummy variables, x₅ , x₆ , and x₇ , and assigned values as follows:

Computer output for the analysis provides the following statistically significant coefficients for the three variables: b₅ = 217, b₆ = 335 and b₇ = 564. Using these coefficients and assuming that all the other variables in the model are held constant, 

a. What is the predicted difference in the number of inquiries would you expect for fall days versus summer days? 

b. What is the predicted difference in the number of inquiries would you expect for winter days versus summer days? 

c. What is the predicted difference in the number of inquiries would you expect between spring days and winter days?

Summer Fall Winter Spring