First, read the case and develop a regression model (I attached Excel file) for how to improve MBA grad salaries. Secondly, give recommendations to the CLIENT (in memo form) on what to do based on your analysis

For the exclusive use of M. Wang, 2017. S w W12307 MBA STARTING SALARIES Professor Chris Higgins wrote this case solely to provide material for class discussion. The author does not intend to illustrate either effective or ineffective handling of a managerial situation. The author may have disguised certain names and other identifying information to protect confidentiality. Richard Ivey School of Business Foundation prohibits any form of reproduction, storage or transmission without its written permission. Reproduction of this material is not covered under authorization by any reproduction rights organization. To order copies or request permission to reproduce materials, contact Ivey Publishing, Richard Ivey School of Business Foundation, The University of Western Ontario, London, Ontario, Canada, N6A 3K7; phone (519) 661-3208; fax (519) 661-3882; e-mail cases@ivey.uwo.ca. Copyright 2012, Richard Ivey School of Business Foundation Version: 2012-11-09 Every year, MBA programs across the country advertise for prospective students, promoting the academic excellence of their programs, the uniqueness of their offerings, the quality of their faculty and a variety of other factors. In a competitive market, the goal is to attract the brightest and the best. Many schools claim that graduates of their programs will earn large salaries upon graduation, a point that clearly ranks high on the list of many students' decision-making criteria. In fact, the Financial Times' rating of MBA programs uses graduates' salaries as a large component of its rating system. Marie Daer, an aspiring MBA applicant, was very interested in the starting salaries of graduating students. Surprisingly, she was able to track down a dataset from a prominent - but anonymous - MBA school. Daer was able to learn the following about the data. Three months after graduation, the students in the class of 2012 were sent a survey. The survey asked about their satisfaction with the MBA program as well as their starting salary. The survey was not anonymous, and the responses of these students were added to the information already on file about them. These data included the graduates' age, sex, years of work experience, GMAT information, fall and spring MBA average, quartile ranking, and their native language. Daer was pleased to have located the data. She wondered whether it could answer some important questions that would help her decide whether to enroll in the MBA program at this particular school. In particular, she wondered about starting salaries, whether gender and/or age made a difference, and whether students liked this particular program. She also wondered whether her GMAT score made a difference in marks. Since her native language was not English, Daer had a relatively low GMAT. This document is authorized for use only by Meixian Wang in Accounting Analytics taught by Gregory Dawson, Arizona State University from August 2017 to February 2018. For the exclusive use of M. Wang, 2017. Page 2 9B12E013 Appendix 1 Description of MBA_SALARIES.SAV This data set contains the reported starting salaries of MBA's graduating in 2012. It also contains their GMAT scores and some information about how they did in the MBA program. Field Description age age - in years sex 1=Male; 2=Female gmat_tot total GMAT score gmat_qpc quantitative GMAT percentile gmat_vpc verbal GMAT percentile qmat_tpc overall GMAT percentile s_avg spring MBA average f_avg fall MBA average quarter quartile ranking (1 work_yrs years of work experience frstlang first language (1=English; 2=other) salary starting salary satis degree of satisfaction with MBA program (1= low, 7 = high satisfaction) st is top, 4th is bottom) Missing salary and data are coded as follows: 998 = did not answer the survey 999 = answered the survey but did not disclose salary data Size of data set: 274 records This document is authorized for use only by Meixian Wang in Accounting Analytics taught by Gregory Dawson, Arizona State University from August 2017 to February 2018. age sex 23 24 24 24 24 24 25 25 25 25 26 26 26 26 26 26 27 27 27 27 27 27 27 28 29 30 31 32 32 32 34 37 42 48 22 27 25 25 27 28 24 25 25 25 26 23 24 27 25 2 1 1 1 2 1 1 2 1 1 1 2 1 1 2 2 2 1 1 1 2 1 1 2 1 1 2 1 1 1 2 2 2 1 2 2 2 2 1 2 1 2 2 1 2 2 1 1 1 gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter 620 77 87 87 3.4 3 610 90 71 87 3.5 4 670 99 78 95 3.3 3.25 570 56 81 75 3.3 2.67 710 93 98 98 3.6 3.75 640 82 89 91 3.9 3.75 610 89 74 87 3.4 3.5 650 88 89 92 3.3 3.75 630 79 91 89 3.3 3.25 680 99 81 96 3.45 3.67 740 99 98 99 3.56 4 610 75 87 86 3.4 3.75 710 95 95 98 3.5 3.5 720 97 97 99 3.4 4 660 84 93 94 3.3 3.25 640 67 98 92 4 4 660 71 99 95 3.5 4 600 77 78 84 3.3 3.5 630 79 89 89 3.5 4 600 91 58 83 3.4 3.25 570 65 82 77 3.3 3.25 740 99 96 99 3.5 3.5 750 99 98 99 3.4 3.5 540 75 50 65 3.6 4 580 56 87 78 3.64 3.33 620 82 84 87 3.4 2.8 560 60 78 72 3.3 3.75 760 99 99 99 3.4 3 640 79 91 91 3.6 3.75 570 71 71 0 3.5 3.5 620 75 89 87 3.3 3 560 43 87 72 3.4 3.5 650 75 98 93 3.38 3 590 84 62 81 3.8 4 660 90 92 94 3.5 3.75 700 94 98 98 3.3 3.25 680 87 96 96 3.5 2.67 650 82 91 93 3.4 3.25 710 96 96 98 3.3 3.5 620 52 98 87 3.4 3.75 670 84 96 95 3.3 3.25 560 52 81 72 3.3 3.5 530 50 62 61 3.6 3.67 650 79 93 93 3.3 3.5 590 56 89 81 3.3 3.25 650 93 81 93 3.4 3 560 81 50 71 3.4 3.67 610 72 84 86 3.3 3.5 650 95 84 93 3.3 3 work_yrs 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 3 2 4 2 4 3 2 4 4 3 1 5 3 5 10 5 7 4 7 9 13 22 1 2 2 3 2 5 0 1 3 1 4 2 2 6 2 25 26 26 30 31 30 30 27 25 28 39 27 27 33 27 28 30 30 40 25 22 23 24 24 24 25 25 25 25 25 25 25 25 25 25 25 25 26 26 26 26 27 27 27 27 27 28 28 28 28 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 2 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 2 1 550 570 580 600 570 620 680 630 600 640 600 570 710 620 600 700 600 670 630 700 600 640 550 570 620 570 660 680 690 670 690 630 670 680 690 670 580 680 560 560 530 740 720 590 630 560 450 620 610 660 74 68 79 60 72 60 96 93 82 89 72 95 95 72 67 95 77 87 71 98 95 89 73 82 82 61 94 94 87 99 94 83 99 91 96 97 79 92 64 87 68 99 99 60 87 60 49 81 85 95 50 74 71 91 71 96 87 75 74 81 81 33 98 89 84 95 81 95 95 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1 1 1 1 1 2 2 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 2 1 1 660 580 510 640 610 590 580 680 670 610 540 630 530 650 630 670 620 540 670 610 560 500 590 570 570 580 580 560 620 620 560 680 620 550 600 670 620 630 650 620 720 640 710 670 710 650 600 640 600 630 94 91 57 90 91 68 79 97 83 64 43 66 48 87 82 87 89 60 95 87 52 78 72 82 93 83 72 68 89 97 75 84 81 72 84 91 84 72 89 88 95 94 96 94 97 89 89 96 89 79 87 50 50 84 62 84 67 87 98 89 78 95 71 89 87 95 74 62 89 71 81 30 81 58 37 58 71 67 74 63 58 96 87 58 67 93 81 95 87 74 98 78 97 89 97 84 62 71 62 91 94 80 55 92 86 81 78 96 96 86 65 90 62 93 89 95 87 65 95 86 72 52 81 75 75 79 78 72 87 88 72 96 89 69 83 95 87 89 93 87 99 92 99 96 99 93 83 91 83 89 3 3.1 3.27 3.2 3.1 3.1 3 3 3.2 3.25 3.2 3.08 3 3 3.1 3.1 3.1 3.1 3.2 3.27 3.2 3 3.2 3.2 3 3.1 3 3.09 3.1 3.2 3.2 3.2 3 3 3.09 3.1 3 3.2 3.2 3.1 2.8 2.9 2.8 2.7 2.8 2.7 2.9 2.7 2.7 2.7 3 2.67 3.4 3 3.67 3 3.25 3 3.4 0 3.25 3.25 2.5 3.2 3 3.33 3.5 3 3.5 3.25 3.25 2.75 3.25 3.25 2.75 3 3.25 3 3.5 3 3.25 3.25 3 3 3.5 3 3.25 3 3.25 3 2.5 3.25 2.75 3 3 3.25 3 2.5 3.25 2.75 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 1 4 5 3 7 6 4 4 4 11 8 12 7 18 16 1 2 8 2 3 2 2 2 2 3 5 2 4 2 3 4 3 3 16 2 6 1 4 4 2 1 2 2 2 2 1 1 2 4 2 25 25 25 25 25 26 26 26 26 26 26 26 26 26 27 27 27 27 27 27 27 27 27 27 27 27 28 28 28 28 29 29 32 34 34 43 23 27 25 25 25 29 27 28 24 25 25 27 28 29 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 2 2 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 2 2 2 1 1 1 550 710 660 630 560 670 660 630 640 560 540 600 570 610 650 550 730 610 630 560 620 600 650 560 610 600 460 650 610 500 590 560 550 610 610 480 520 620 580 630 610 560 620 580 670 560 580 680 610 710 72 99 95 93 79 97 88 83 87 56 52 97 48 82 89 66 95 97 82 61 97 68 79 52 48 77 66 99 64 46 92 57 52 79 82 49 43 87 78 75 89 64 79 72 83 39 72 97 89 93 58 91 84 71 58 81 93 87 84 81 71 45 89 81 84 63 99 45 89 74 54 87 95 81 98 81 16 63 93 54 58 74 78 81 78 41 67 74 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1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 710 630 600 660 670 580 680 660 570 680 610 640 600 730 650 640 590 590 700 660 450 560 600 570 590 670 660 560 790 630 580 620 670 580 560 620 710 570 530 690 630 670 630 680 650 550 600 630 580 740 99 84 89 91 83 89 79 83 56 96 82 93 53 98 87 79 68 97 99 93 28 87 75 82 89 98 97 59 99 93 84 85 89 74 74 93 94 69 35 99 87 91 99 89 88 79 99 82 83 99 92 87 67 90 97 54 99 95 84 87 81 78 95 96 91 93 81 41 87 87 46 45 78 58 58 81 81 74 99 78 58 85 91 70 67 71 98 71 81 87 84 91 50 96 92 45 46 87 67 98 99 89 85 95 96 78 96 94 75 97 86 91 84 99 93 91 81 81 98 95 34 72 83 75 81 95 94 73 99 91 78 89 95 78 73 87 99 0 62 97 89 95 89 96 93 69 86 89 79 99 2.9 2.8 2.8 2.8 2.8 2.91 2.9 2.9 2.9 2.8 2.5 2.4 2.5 2.4 2.5 2.67 2.6 2.5 2 2.6 2.1 2.6 2.2 2.5 2.5 2.6 2.5 2.4 2.4 2.1 2.7 3.3 3.6 3.4 3.6 2.4 3.4 2.3 3.3 2.3 2.9 3.3 2.9 2.8 3.45 2.45 2.8 3.8 3 2.2 3 2.75 3 3 2.75 2.83 3 3.5 3 2.75 2.75 2.5 3 2.75 2.5 0 2.75 2.75 2 2 2 3 2.25 2.75 2.25 2.5 2.5 2.5 2.5 2.5 2.75 3 3.25 3.25 3.6 2.75 3.75 2.5 2.75 2.25 2.8 3.25 3.25 3 3.83 2.75 3 3.5 3.25 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 2 2 8 6 2 6 2 4 2 2 1 2 2 3 1 3 2 1 2 4 3 2 3 3 3 4 2 4 4 1 1 5 3 5 3 6 5 6 7 3 3 1 4 2 5 6 7 6 8 31 31 32 32 35 39 24 23 25 26 26 27 25 25 26 24 24 26 29 26 31 23 25 26 40 1 1 1 1 1 2 2 1 2 1 2 1 1 1 1 1 2 2 1 1 1 1 1 1 2 570 640 570 510 570 700 560 660 720 620 630 650 660 610 600 570 600 650 630 630 530 580 540 550 500 75 79 89 79 72 89 55 81 96 78 85 89 99 83 87 75 77 91 72 96 75 64 79 72 60 62 92 41 22 71 98 78 98 98 87 81 89 71 81 62 62 78 84 95 71 45 81 45 58 45 75 92 75 54 75 98 71 95 99 89 90 93 95 86 83 75 84 93 89 91 62 78 65 69 51 2.8 2.7 2.6 2.3 3.3 3.3 3.5 2.5 3.5 2.4 2.9 2.4 3.4 2.4 2.5 2.3 2.6 2.6 2.6 2.6 2.4 2.2 2.6 2.6 2.5 3 2.75 2.5 2.25 4 3.25 3.25 3 3.6 2 3.25 2.25 3.25 2.75 2.5 2.5 3 3 2.5 2.75 2.75 2 2.5 2.75 2.75 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 7 4 5 8 5 2 2 3 2 3 5 2 2 2 2 2 2 3 3 4 2 3 3 15 frstlang salary 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 999 0 0 0 999 998 998 998 998 998 998 998 998 998 998 998 999 0 0 0 0 999 0 0 0 999 0 0 0 0 85000 85000 86000 88000 92000 93000 95000 95000 95000 96000 96000 100000 100000 100000 105000 satis 7 6 6 7 5 6 5 6 4 998 998 998 998 998 998 998 998 998 998 998 4 6 5 5 5 6 7 5 6 4 6 6 5 6 5 6 5 7 6 5 4 5 3 7 5 7 6 6 7 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 105000 105000 105000 105000 105000 106000 106000 107500 108000 110000 112000 115000 115000 118000 120000 120000 120000 120000 146000 162000 0 0 0 0 0 0 0 0 999 998 998 998 998 998 998 998 998 999 0 0 0 999 0 0 998 998 998 0 0 999 6 6 5 6 6 7 6 5 6 5 7 5 5 7 5 5 6 6 6 5 5 7 5 6 5 4 5 6 5 998 998 998 998 998 998 998 998 1 6 6 5 4 5 6 998 998 998 6 6 3 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 0 999 0 0 0 999 0 0 999 0 0 0 0 0 0 82000 92000 93000 95000 95000 96000 96500 98000 98000 98000 99000 100000 100000 101000 103000 104000 105000 105000 105000 107000 112000 115000 115000 130000 145800 0 0 0 0 0 999 0 0 998 998 6 4 5 5 5 5 6 5 6 7 5 5 5 6 5 7 5 6 6 6 7 6 6 6 5 6 5 6 5 6 5 6 5 5 5 6 6 6 7 6 5 4 7 7 7 5 6 6 998 998 1 1 1 2 2 1 2 1 1 1 1 2 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 0 0 999 998 998 998 998 998 999 0 0 999 0 0 0 0 999 0 0 0 999 998 998 998 998 998 998 998 998 999 0 999 0 0 0 0 78256 88500 90000 90000 93000 95000 97000 97000 98000 98000 98000 98000 98000 98000 6 6 6 998 998 998 998 998 5 6 6 6 5 6 6 4 5 5 6 6 2 998 998 998 998 998 998 998 998 6 5 5 6 6 5 5 5 6 7 5 6 7 7 6 7 7 6 6 7 5 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2 1 1 1 100000 100000 101000 101100 102500 105000 106000 107300 108000 112000 998 998 999 0 999 998 998 999 0 0 0 999 0 999 998 998 999 0 999 0 0 999 0 0 0 999 0 0 0 999 999 0 0 0 0 999 999 998 998 998 6 6 6 6 5 5 6 7 6 6 998 998 4 6 7 998 998 4 7 5 6 3 6 6 998 998 4 5 6 5 5 5 6 6 5 4 6 5 7 5 4 5 4 5 6 4 4 998 998 998 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 0 999 999 0 0 0 64000 77000 85000 85000 86000 90000 92000 95000 96000 98000 100000 100000 100400 101600 104000 105000 115000 126710 220000 6 3 3 5 6 5 7 6 6 6 5 5 7 7 6 6 6 7 7 6 6 6 5 6 6