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
3. Provide a numeric example of how this regression equation may be used to predict students'
starting salaries.
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.
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.

Transcribed Image Text:

1 Student School_Ranking GPA Experience Salary 23456 2 1 78 2.92 3 73,590 2 56 3.84 4 3 23 3.04 06 9 87,000 6 76,970 4 67 3.20 6 7 56 56 3.61 78 2.99 8 7 68 3.78 9 8 89 3.20 10 9 37 3.42 11 10 67 3.05 12 11 48 3.12 13 12 78 3.56 14 13 56 3.01 67500 50 754 75 79,320 79,530 71,040 8 82,050 5 78,890 82,170 76,120 4 77,500 83,920 71,800 15 14 25 3.15 16 15 68 3.05 17 16 36 3.24 675 6 77,000 79,000 5 77,800 18 17 76 3.25 19 18 78 3.78 9 20 19 67 3.12 164 80,600 87,000 4 78,450 21 20 67 3.24 22 21 15 2.98 23 22 29 3.24 24 23 49 3.08 25 24 67 3.00 26 25 39 2.95 27 26 81 3.01 28 27 54 3.23 29 28 72 3.01 856464572 80,600 74,900 79,200 77,000 77,900 76,950 76,800 79,300 72,120 1 Student School_Ranking GPA Experience Salary 23456 2 1 78 2.92 3 73,590 2 56 3.84 4 3 23 3.04 06 9 87,000 6 76,970 4 67 3.20 6 7 56 56 3.61 78 2.99 8 7 68 3.78 9 8 89 3.20 10 9 37 3.42 11 10 67 3.05 12 11 48 3.12 13 12 78 3.56 14 13 56 3.01 67500 50 754 75 79,320 79,530 71,040 8 82,050 5 78,890 82,170 76,120 4 77,500 83,920 71,800 15 14 25 3.15 16 15 68 3.05 17 16 36 3.24 675 6 77,000 79,000 5 77,800 18 17 76 3.25 19 18 78 3.78 9 20 19 67 3.12 164 80,600 87,000 4 78,450 21 20 67 3.24 22 21 15 2.98 23 22 29 3.24 24 23 49 3.08 25 24 67 3.00 26 25 39 2.95 27 26 81 3.01 28 27 54 3.23 29 28 72 3.01 856464572 80,600 74,900 79,200 77,000 77,900 76,950 76,800 79,300 72,120