Questions: 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.

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.

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.

Experience 3 9 6 6 7 5 8 5 7 5 4 7 5 6 7 5 6 9 4 Student School Ranking 1 78 2 56 3 23 4 67 5 56 6 7 7 68 8 B9 9 37 10 67 11 48 12 78 13 56 14 25 15 68 16 36 17 76 18 78 19 67 20 67 21 15 22 29 23 49 24 67 25 39 26 81 27 54 28 72 29 73 30 78 31 51 32 86 33 76 34 30 35 58 36 86 37 34 38 72 39 38 40 GPA 2.92 3.84 3.04 3.20 3.61 2.99 3.78 3.20 3.42 3.05 3.12 3.56 3.01 3.15 3.05 3.24 3.25 3.78 3.12 3.24 2.98 3.24 3.08 3.00 2.95 3.01 3.23 3.01 3.45 3.85 3.00 3.23 3.80 3.08 3.15 3.35 3.09 3.35 3.16 2.76 Salary 73,590 87,000 76,970 79,320 79,530 71,040 82,050 78,890 82,170 76,120 77,500 83,920 71,800 77,000 79,000 77 800 80,600 87,000 78,450 80,600 74,900 79,200 77,000 77,900 76,950 76,800 79,300 72,120 83,900 85,200 77,300 83,500 77,000 75,000 79,200 80,400 80,200 84,800 72,800 75.000 5 6 4 6 4 5 7 2 7 8 5 6 7 5 7 7 7 9 3 7