The fileP10_14.xlsxincludes data on the 50 top graduate programs in the United States, according to a recentU.S. News & World Reportsurvey. Columns B, C, and D contain the average starting salary of the students of each university, a ranking score of each university (highest is 100), the average GMAT scores and the average GPA after graduation.

Run three separate regressions, each using the Average Salary as the dependent variable and university Score, GMAT, and GPA as single independent variables. Interpret these equations.

• Regression Equation with University score as independent variable:
• [ Select ]
• ["
• Y = 28,227 + 1,148 * X1
• ", "
• Y = 38,525 + 752 * X1
• ", "
• Y = 26,901 + 2,208 * X1
• ", "
• Y = 61,306+ 1,726 * X1
• "]
• Regression Equation with GMAT as independent variable:
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• ["
• Y = -242,073 + 517 * X2
• ", "
• Y = 184,648 - 527 * X2
• ", "
• Y = -104,743 + 621 * X2
• ", "
• Y = -445,902 - 785 * X2
• "]
• Regression Equation with GPA as independent variable:
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• ["
• Y = 238,744 - 38,444 * X3
• ", "
• Y = 194,821 - 94,177 * X3
• ", "
• Y = 224,952 + 10,854 * X3
• ", "
• Y = -388,637 - 25,891 * X3
• "]
• The only variables that is NOT a good predictor of the starting salary at a significance level of 0.05 is
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• ["
• average GPA after graduation
• ", "
• ranking score of each university
• ", "
• GMAT
• "] . the p-value of the regression coeficient (Beta1) of this valriable is
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• ["
• 0.079
• ", "
• 1.325
• ", "
• 0.006
• ", "
• 0.050
• "]
• How much more money can you expect to earn after graduation if your GMAT score is 1 point higher?
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• ["
• $517
• ", "
• -$231
• ", "
• $6,142
• ", "
• $997
• "]

A college bookstore must order books two months before each semester starts. They believe that the number of books that will ultimately be sold for any particular course is related to the number of students registered for the course when the books are ordered. They would like to develop a linear regression equation to help plan how many books to order. From past records, the bookstore obtains the number of students registered, X, and the number of books actually sold for a course, Y, for 12 different semesters. These data are below.

Semesterstudentsbooks136322282833535439345303063029731318383893633103834112930122625

1. Conduct a simple linear regression to find the slope and intercept. The regression equation that you calculated isY =
2. [ Select ]
3. ["
4. 8.33
5. ", "
6. 10.40
7. ", "
8. 7.38
9. ", "
10. 15.18
11. "] +
12. [ Select ]
13. ["
14. 0.70
15. ", "
16. 1.21
17. ", "
18. -1.14
19. ", "
20. -1.67
21. "] * X
22. What percentage of variation in the data can be explained by the linear model?
23. [ Select ]
24. ["
25. 0.81
26. ", "
27. 1.64
28. ", "
29. 0.21
30. ", "
31. 0.64
32. "]
33. Conduct hypothesis test to verify if the slope can be considered equal to 0.The t-statistic is
34. [ Select ]
35. ["
36. 6.52
37. ", "
38. 0.63
39. ", "
40. 1.96
41. ", "
42. 2.17
43. "] . The p-value is
44. [ Select ]
45. ["
46. 0.0000.. (very small number)
47. ", "
48. 1.3
49. ", "
50. 0.99999... (close to 1)
51. ", "
52. 0.67
53. "] . thus we can conclude that
54. [ Select ]
55. ["
56. The number of students in a class is a good predictor of the number of books sold
57. ", "
58. there is not sufficient evidence to make any claim about the relationship between number of students and books sold
59. ", "
60. The number of students in a class is NOT a good predictor of the number of books sold
61. "]
62. Based on the regression model, how many books can be expected to be sold if the class size is 30 students? Answer:
63. [ Select ]
64. ["
65. About 31 books
66. ", "
67. about 21 books
68. ", "
69. about 35 books
70. ", "
71. about 33 books
72. "]