Student School_Ranking GPA Experience Salary
1 78 2.92 3 73,590
2 56 3.84 9 87,000
3 23 3.04 6 76,970
4 67 3.20 6 79,320
5 56 3.61 7 79,530
6 78 2.99 5 71,040
7 68 3.78 8 82,050
8 89 3.20 5 78,890
9 37 3.42 7 82,170
10 67 3.05 5 76,120
11 48 3.12 4 77,500
12 78 3.56 7 83,920
13 56 3.01 5 71,800
14 25 3.15 6 77,000
15 68 3.05 7 79,000
16 36 3.24 5 77,800
17 76 3.25 6 80,600
18 78 3.78 9 87,000
19 67 3.12 4 78,450
20 67 3.24 8 80,600
21 15 2.98 5 74,900
22 29 3.24 6 79,200
23 49 3.08 4 77,000
24 67 3.00 6 77,900
25 39 2.95 4 76,950
26 81 3.01 5 76,800
27 54 3.23 7 79,300
28 72 3.01 2 72,120
29 73 3.45 7 83,900
30 78 3.85 8 85,200
31 51 3.00 5 77,300
32 86 3.23 6 83,500
33 76 3.80 7 77,000
34 30 3.08 5 75,000
35 58 3.15 7 79,200
36 86 3.35 7 80,400
37 34 3.09 7 80,200
38 72 3.35 9 84,800
39 38 3.16 3 72,800
40 89 2.76 7 75,000
1. Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable. (5 + 5 = 10 points)
2. Present the simple linear regression equation, identify and explain the coefficient of determination, intercept and regression coefficient, and significance of F-test. Based on this analysis, is your regression equation “good for use”? Explain. (2+2+2+2+2 = 10 points)
3. Provide a numeric example of how this regression equation may be used to predict students’ starting salaries. (5 points)
4. Develop a multiple regression equation for starting salaries using School_Ranking, GPA, and Experience as independent variables. Is this regression equation “good for use”? Explain. (7 + 3 = 10 points)
5. If the multiple regression equation in the previous question is not “good for use”, how would you suggest improving this multiple regression equation? Present the improved multiple regression equation. Give a numeric example of how this improved multiple regression equation may be used to predict students’ starting salaries. (5 + 5 + 5 = 15 points)
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