The file ClassificationData.xlsx contains the following information about the top 25 MBA pro- grams: percentage of applicants accepted, percentage of accepted applicants who enroll, mean GMAT score of enrollees, mean undergraduate GPA of enrollees, annual cost of school (for state schools, this is the cost for out-of-state students), percentage of students who are minori- ties, percentage of students who are non-U.S. residents, and mean starting salary of graduates (in thousands of dollars). Use these data to divide the top 25 schools into 4 clusters, using for example the K-Means clustering algorithm, and interpret your clusters. The method is ex- plained in our textbook: Section 8.8 in the Fourth and Fifth Edition or Section 14.3 in the Sixth Edition. More precisely, use Evolutionary Solver to find 4 schools to be used as cluster centers and to assign all other schools to one of these cluster centers. Each school is then assigned to the nearest cluster center, where nearest is defined in terms of the eight attributes. The objective is to minimize the sum of the distances from each school to its cluster center.

Hint: Your model will have four decision variables (changing cells) corresponding to the in- dexes of the four schools chosen as cluster centers. In addition, please note that you need to first standardize the value of each attribute by subtract- ing the attribute's mean and dividing the difference by the attribute's standard deviation.

AutoSave OFF AH ? CE ... ClassificationData Home Insert Draw Page Layout Formulas Data Review View Tell me Share Comments Calibri v 11 AA = ab Wrap Text General Insert v Ex AY- O. Ex Delete Paste BIUV DVAv E Merge & Center v $ ~ % " Conditional Format Cell Sort & Find & Analyze Show Formatting as Table Styles Format v Filter Select Data ToolPak B1 fx B C D E G H K M N 0 1 |MBA program data W N School % accepted % accepted who enroll Mean GMAT Mean GPA Total Cost % minority % non-US Mean Starting Salary 4 Wharton 15 71 662 3.42 $240 16 30 102 5 Michigan 28 44 645 3.3 29800 15 26 86 6 Northwestern 14 69 660 3.3 32600 9 24 99 Harvard 13 88 680 3.5 30100 19 27 114 8 Virginia 19 49 660 3.1 31200 20 12 93 9 Columbia 14 70 660 3.3 3220 12 24 93 10 Stanford 7 81 690 3.6 34500 25 25 111 11 Chicago 23 57 685 3.4 34200 5 23 90 12 MIT 14 12 650 3.5 36700 15 37 101 13 Dartmouth 14 49 669 3.39 32700 9 16 104 14 Duke 17 50 646 3.33 3010 12 19 84 15 UCLA 17 55 651 3.5 2710 10 20 91 16 Berkeley 13 51 652 3.42 29100 11 35 91 17 NYU 20 11 646 3 .3 32700 79 18 Indiana 20 630 3.2 21000 19 Washington U 506 3.2 2800 20 Carnegie-Mellon 638 3.2 27200 21 Cornell 634 3.3 29600 22 UNC 530 3 .3 17500 16 23 Texas 12 531 3.3 19100 14 24 Rochester 34 530 3.22 2820 25 Yale 54 676 3.38 32000 26 SMU 48 601 3 26300 27 Vanderbilt 42 47 615 3.2 29700 28 Thunderbird 75 64 $72 3.41 23800 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 D Data + Ready + 100%