Suppose that you work for a company that claims to base the pay raises of its managers on performance level rather on simply the years of experience at the company. The company measures “performance” by using a rating by the manager’s supervisor on a 7-point scale that ranges from 1¼Unsatisfactory to 5¼Outstanding. The company measures “experience” by the total number of years of relevant business experience of the manager, both at this company and also at former companies. The educational level of the manager is measured as the total number of undergraduate or graduate degrees obtained by the manager.

Let’s find out what happens when you use the hypothetical data that is presented in Fig. 7.11.

(a) Create an Excel spreadsheet using PERCENT RAISE as the criterion (Y), and the other variables as the three predictors of this criterion.

(b) Use Excel’s multiple regression function to find the relationship between these variables and place it below the table.

(c) Use number format (two decimal places) for the multiple correlation on the Summary Output, number format (four decimal places) for the coefficients, and two decimal places for all other decimal figures in the SUMMARY OUTPUT.

(d) Print the table and regression results below the table so that they fit onto one page.

(e) By hand on this printout, circle and label:
(1a) multiple correlation Rxy (2b) coefficients for the y-intercept, PERFORMANCE RATING, YEARS OF EXPERIENCE, AND NO. DEGREES

(f ) Save this file as: Raise2 (g) Now, go back to your Excel file and create a correlation matrix for these four variables, and place it underneath the SUMMARY OUTPUT. Change each correlation to just two decimals. Save this file again as: Raise3 (h) Now, print out just this correlation matrix in portrait mode on a separate sheet of paper.