# Applied Data Analysis

#2 (Pg 545) The owner of the Original Italian Pizza restaurant chain would like to predict the sales of his specialty, deep-dish pizza. He has gathered data on the monthly sales of deep-dish pizzas at his restaurants and observations on other potentially relevant variables for each of his 15 outlets in central Indiana. These data are provided in the file P10_04.xlsx.

a. Estimate a multiple regression model between the quantity sold (Y ) and the explanatory variables in columns C–E.
b. Is there evidence of any violations of the key assumptions of regression analysis?
c. Which of the variables in this equation have regression coefficients that are statistically different from zero at the 5% significance level?

#18 (Pg 555) The Undergraduate Data sheet of the file P10_21.xlsx contains information on 101 undergraduate business programs in the U.S., including various rankings by Business Week. Use multiple regression to explore the relationship between the median starting salary and the following set of potential explanatory variables: annual cost, full-time enrollment, faculty-student ratio, average SAT score, and average ACT score. Which explanatory variables should be included in a final version of this regression equations? Justify your choices. Is multicollinearity a problem? Why or Why not?

#23 (Pg 560) The Undergraduate Data sheet of the file P10_21.xlsx contains information on 101 undergraduate business programs in the U.S., including various rankings by Business Week. Use forward, backward, and stepwise regression analysis to explore the relationship between median starting salary and the following set of potential explanatory variables: annual cost, full-time enrollment, faculty-student ratio, average SAT score, and average ACT score. Do these three methods all lead to the same regression equation? If not, do you think any of the final equations are substantially better than any of the others?

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