The following multiple regression model was developedto predict job performance as measured by a company jobperformance evaluation index based on a preemployment testscore and college grade point average (GPA):Wherey = 35 + 20x1 + 50x2y = job performance evaluation indexx 1 =preemployment test scorex2 =college GPAa) Forecast the job performance index for an applicant who hada 3.0 GPA and scored 80 on the preemployment test. b) Forecast the job performance index for an applicant who hada 2.5 GPA and scored 70 on the preemployment test.

The following multiple regression model was developedto predict job performance as measured by a company jobperformance evaluation index based on a preemployment testscore and college grade point average (GPA):Wherey = 35 + 20x1 + 50x2y = job performance evaluation indexx 1 =preemployment test scorex2 =college GPAa) Forecast the job performance index for an applicant who hada 3.0 GPA and scored 80 on the preemployment test. b) Forecast the job performance index for an applicant who hada 2.5 GPA and scored 70 on the preemployment test.

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter14: Data Mining

Section14.2: Classification Methods

Problem 3P

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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 P13_17.xlsx. a. Estimate a simple regression equation 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 variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation 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 equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?
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