The file 350_Case_2.xlsx contains starting salaries for 40 MBA students directly after graduation. These students were randomly selected from different universities. For each student, the file also lists the number of years of experience prior to entering the MBA program and his/her overall GPA in the MBA program. Besides, the file contains the overall ranking of the college/school on a 100-point scale (1=lowest, 100=highest). Questions: 2. Present the simple regression equation, identify and analyze 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 an example of how this regression equation may be used to project 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.

Show an example of how this improved multiple regression equation may be used to project student starting salaries. Student School_Ranking GPA ExperienceSalary 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 3.01 272,120 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