The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables.

The data are listed in the file P16_19.xlsx.

a. Estimate a simple regression model involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression.

b. Add another explanatory variable—annual advertising expenditures—to the regression model in part

a. Estimate and interpret this expanded model.

How does the R-square value for this multiple regression model compare to that of the simple regression model estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two models?

c. Add one more explanatory variable to the multiple regression model estimated in part

b. In particular, estimate and interpret the coefficients of a multiple regression model that includes the previous year’s advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the model of part b? Explain any changes in this value.

What does the adjusted R-square for the new model tell you?