The director of the MBA program at Salterdine University wants to develop a procedure to determine which applicants to admit to the MBA program. The director believes that an applicant’s undergraduate grade point average (GPA) and score on the GMAT exam are helpful in predicting which applicants will be good students. To assist in this endeavor, the director asked a committee of faculty members to classify 70 of the recent students in the MBA program into two groups: (1) good students and (2) weak students. The file MBAStudents.xlsm summarizes these ratings, along with the GPA and GMAT scores for the 70 students. What are the coordinates of the centroids for the good students and the weak students? Use XLMiner’s standard data partition command to partition the data into a training set (with 60% of the observations) and validation set (with 40% of the observations) using the default seed of 12345. Use discriminant analysis to create a classifier for this data. How accurate is this procedure on the training and validation data sets? Suppose that the MBA director received applications for admission to the MBA program from the following individuals. According to your recommended classifier, which of these individuals do you expect to be good students and which do you expect to be weak? Name GPA GMAT Mike Dimoupolous 3.02 450 Scott Frazier 2.97 587 Paula Curry 3.95 551 Terry Freeman 2.45 484 Dana Simmons 3.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

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The director of the MBA program at Salterdine University wants to develop a procedure to determine which applicants to admit to the MBA program. The director believes that an applicant’s undergraduate grade point average (GPA) and score on the GMAT exam are helpful in predicting which applicants will be good students. To assist in this endeavor, the director asked a committee of faculty members to classify 70 of the recent students in the MBA program into two groups: (1) good students and (2) weak students. The file MBAStudents.xlsm summarizes these ratings, along with the GPA and GMAT scores for the 70 students.

What are the coordinates of the centroids for the good students and the weak students?
Use XLMiner’s standard data partition command to partition the data into a training set (with 60% of the observations) and validation set (with 40% of the observations) using the default seed of 12345.
Use discriminant analysis to create a classifier for this data. How accurate is this procedure on the training and validation data sets?
Suppose that the MBA director received applications for admission to the MBA program from the following individuals. According to your recommended classifier, which of these individuals do you expect to be good students and which do you expect to be weak?

Name GPA GMAT

Mike Dimoupolous 3.02 450

Scott Frazier 2.97 587

Paula Curry 3.95 551

Terry Freeman 2.45 484

Dana Simmons 3.26 524

DATA

Student	Rating	GPA	GMAT
1	1	2.96	671
2	1	3.14	548
3	1	3.22	557
4	1	3.29	602
5	1	3.69	580
6	1	3.46	768
7	1	3.03	701
8	1	3.19	738
9	1	3.63	522
10	1	3.59	663
11	1	2.86	569
12	1	2.85	571
13	1	3.14	494
14	1	3.28	446
15	1	2.89	522
16	1	3.37	590
17	1	2.53	564
18	1	3.24	612
19	1	3.04	596
20	1	3.44	685
21	1	3.34	583
22	1	3.52	574
23	1	3.84	658
24	1	3.07	618
25	1	2.83	541
26	1	2.26	583
27	1	2.75	573
28	1	3.55	602
29	1	5.76	622
30	1	3.48	655
31	1	3.36	588
32	1	3.12	534
33	1	3.22	580
34	1	3.28	602
35	1	3.26	626
36	2	3.33	588
37	2	3.01	555
38	2	3.57	541
39	2	2.8	566
40	2	3.06	569
41	2	1.98	562
42	2	2.69	610
43	2	3.33	548
44	2	2.47	599
45	2	1.85	464
46	2	2.47	478
47	2	2.38	648
48	2	2.36	548
49	2	2.73	477
50	2	2.56	490
51	2	2.3	410
53	2	3.41	434
54	2	2.56	563
55	2	2.25	466
56	2	3.15	388
57	2	3.5	477
58	2	2.89	560
59	2	2.8	519
60	2	3.13	491
61	2	2.54	521
62	2	2.43	500
63	2	2.2	549
64	2	2.36	606
65	2	2.57	617
66	2	2.35	481
67	2	2.72	487
68	2	2.51	533
69	2	2.36	474
70	2	2.19	557

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