-Data Assignment -
A Case Study in R, with Communication and Visual Report
General Instructions:
· This data assignment is due on Wed. April 10 (end of day, 11:59 pm). After this time/date, there is a 50% penalty per day.
· This is an individual assignment. You need to complete it independently without help from anyone else.
· Once you put your name on it, you are responsible for all answers.
· Your statistical report should clearly demonstrate all steps of hypothesis test/confidence intervals. Interpretation of any statistical output is required and must accompany all calculations, especially those involving testing procedures and confidence intervals.
· Use R Markdown and knit your document as a word or pdf document. Projects where is code copied and pasted in Word will have a 50% penalty.
· Organize your R code script with comments into a report.
· Include interpretations of statistical analyses.
· Label each question properly, include headings.
· Use R/R Studio only. Projects solved in Excel will not be accepted.
· There is a page limit of about 10 pages for this report.
· START EARLY!
Score (out of 100): _____________________________
NAME_____________________
Signature (I worked individually):___________________
USE CreditCard.csv data posted in Brightspace
Credit Card Company. A Business Analytics Lab Report.
Suppose you work for a credit card company and were assigned an analytics project for which you decided to apply regression analysis methods. Your supervisor asked you to investigate differences in credit card balance between males and females, differences in average balance between different ethnicities Caucasians, Asians and African Americans (AA) and address differences in credit card balances between ma
ied and non-ma
ied. In addition, you were also asked to investigate the effect of Income, Age, and Education on credit card balances.
The following observations for a number of potential customers were recorded and stored in a data file named “CreditCards”: balance (average credit card debt for a number of individuals), as well as quantitative predictors: age, education (years of education), marital Status (ma
ied or not), income (thousands of dollars), limit (credit limit). (Data file, “CreditCards.csv” is posted on Brightspace).
Please provide a self-contained managerial report to present your research findings. To help you organize your writing, I list some points below. For example, your report should incorporate full work on the items below.
1. Do an exploratory analysis of your data (know your data, variable types, generate visuals).
2. Generate a matrix scatterplot (with interpretation).
3. Propose a simple linear regression model to investigate Credit Card Balances between males and females, ignoring the other variables at the moment. Define any dummy variables that you use, interpret coefficient estimates in context, assess model utility using coefficient of determination, and the standard e
or. Clearly address the Question: Is there a significant difference between average card balances between males and females?
4. Suppose you want to investigate the relationship between credit card balance and ethnicity. Set up the regression model and thoroughly explain the model output and state conclusions derived from it. Is there any statistical evidence of a real difference in credit card balance between the ethnicities?
5. Suppose you want to investigate the relationship between credit card balance and marital status. Set up the regression model and thoroughly explain the model output and state conclusions. Is there any statistical evidence of a real difference in credit card balance between the ma
ied and non-ma
ied individuals?
6. Suppose you want to investigate the relationship between credit card balance and Income, Age, Education, Gender, Ethnicity and Marital status. Set up a regression model. Discuss this model and derive conclusions from it.
7. Suppose you would like to predict card balance for a new (unobserved case). Set your particular values for predictors. For example: 57 year old non-ma
ied Caucasian female customer with an income of 90,000 and 21 years of education (you do not need to use this example, pick your own). Find the interval designed to hold a fraction (95%) of the values of the response (credit card balance) for particular predictor values in this regression. Interpret.
8. Suppose your supervisor is asking about other possible insights, and a possibility of formulating interaction models, that may potentially contribute to his/her understanding of the market better. By doing quick model explorations with this data set, can you suggest other possibilities that might offer insightful analysis in the credit card business?
CreditCards
Income Limit Age Education Gender Ma
ied Ethnicity Balance
14.891 3606 34 11 Male Yes Caucasian 333
106.025 6645 82 15 Female Yes Asian 903
104.593 7075 71 11 Male No Asian 580
148.924 9504 36 11 Female No Asian 964
55.882 4897 68 16 Male Yes Caucasian 331
80.18 8047 77 10 Male No Caucasian 1151
20.996 3388 37 12 Female No AA 203
71.408 7114 87 9 Male No Asian 872
15.125 3300 66 13 Female No Caucasian 279
71.061 6819 41 19 Female Yes AA 1350
63.095 8117 30 14 Male Yes Caucasian 1407
15.045 1311 64 16 Male No Caucasian 0
80.616 5308 57 7 Female Yes Asian 204
43.682 6922 49 9 Male Yes Caucasian 1081
19.144 3291 75 13 Female No AA 148
20.089 2525 57 15 Female Yes AA 0
53.598 3714 73 17 Female Yes AA 0
36.496 4378 69 15 Female Yes Asian 368
49.57 6384 28 9 Female Yes Asian 891
42.079 6626 44 9 Male No Asian 1048
17.7 2860 63 16 Female No Asian 89
37.348 6378 72 17 Female No Caucasian 968
20.103 2631 61 10 Male Yes AA 0
64.027 5179 48 8 Male Yes AA 411
10.742 1757 57 15 Female No Caucasian 0
14.09 4323 25 16 Female Yes AA 671
42.471 3625 44 12 Female No Caucasian 654
32.793 4534 44 16 Male No AA 467
186.634 13414 41 14 Female Yes AA 1809
26.813 5611 55 16 Female No Caucasian 915
34.142 5666 47 5 Female Yes Caucasian 863
28.941 2733 43 16 Male Yes Asian 0
134.181 7838 48 13 Female No Caucasian 526
31.367 1829 30 10 Male Yes Caucasian 0
20.15 2646 25 14 Female Yes Asian 0
23.35 2558 49 12 Female No Caucasian 419
62.413 6457 71 11 Female Yes Caucasian 762
30.007 6481 69 9 Female Yes Caucasian 1093
11.795 3899 25 10 Female No Caucasian 531
13.647 3461 47 14 Male Yes Caucasian 344
34.95 3327 54 14 Female No AA 50
113.659 7659 66 15 Male Yes AA 1155
44.158 4763 66 13 Female Yes Asian 385
36.929 6257 24 14 Female Yes Asian 976
31.861 6375 25 16 Female Yes Caucasian 1120
77.38 7569 50 12 Female Yes Caucasian 997
19.531 5043 64 16 Female Yes Asian 1241
44.646 4431 49 15 Male Yes Caucasian 797
44.522 2252 72 15 Male Yes Asian 0
43.479 4569 49 13 Male Yes AA 902
36.362 5183 49 15 Male Yes AA 654
39.705 3969 27 20 Male Yes AA 211
44.205 5441 32 12 Male Yes Caucasian 607
16.304 5466 66 10 Male Yes Asian 957
15.333 1499 47 9 Female Yes Asian 0
32.916 1786 60 8 Female Yes Asian 0
57.1 4742 79 18 Female Yes Asian 379
76.273 4779 65 14 Female Yes Caucasian 133
10.354 3480 70 17 Male Yes Caucasian 333
51.872 5294 81 17 Female No Caucasian 531
35.51 5198 35 20 Female No Asian 631
21.238 3089 59 10 Female No Caucasian 108
30.682 1671 77 7 Female No Caucasian 0
14.132 2998 75 17 Male No Caucasian 133
32.164 2937 79 15 Female Yes AA 0
12 4160 28 14 Female Yes Caucasian 602
113.829 9704 38 13 Female Yes Asian 1388
11.187 5099 69 16 Female No AA 889
27.847 5619 78 15 Female Yes Caucasian 822
49.502 6819 55 14 Male Yes Caucasian 1084
24.889 3954 75 12 Male Yes Caucasian 357
58.781 7402 81 12 Female Yes Asian 1103
22.939 4923 47 18 Female Yes Asian 663
23.989 4523 31 15 Male No Caucasian 601
16.103 5390 45 10 Female Yes Caucasian 945
33.017 3180 28 16 Male Yes AA 29
30.622 3293 68 16 Male No Caucasian 532
20.936 3254 30 15 Female No Asian 145
110.968 6662 45 11 Female Yes Caucasian 391
15.354 2101 65 14 Male No Asian 0
27.369 3449 40 9 Female Yes Caucasian 162
53.48 4263 83 15 Male No Caucasian 99
23.672 4433 63 11 Male No Caucasian 503
19.225 1433 38 14 Female No Caucasian 0
43.54 2906 69 11 Male No Caucasian 0
152.298 12066 41 12 Female Yes Asian 1779
55.367 6340 33 15 Male Yes Caucasian 815
11.741 2271 59 12 Female No Asian 0
15.56 4307 57 8 Male Yes AA 579
59.53 7518 52 9 Female No AA 1176
20.191 5767 42 16 Male Yes AA 1023
48.498 6040 47 16 Male Yes Caucasian 812
30.733 2832 51 13 Male No Caucasian 0
16.479 5435 26 16 Male No AA 937
38.009 3075 45 15 Female No AA 0
14.084 855 46 17 Female Yes AA 0
14.312 5382 59 17 Male No Asian 1380
26.067 3388 74 17 Female Yes AA 155
36.295 2963 68 14 Female No AA 375
83.851 8494 47 18 Male No Caucasian 1311
21.153 3736 41 11 Male No Caucasian 298
17.976 2433 70 16 Female No Caucasian 431
68.713 7582 56 16 Male No Caucasian 1587
146.183 9540 66 15 Male No Caucasian 1050
15.846 4768 53 12 Female No Caucasian 745
12.031 3182 58 18 Female Yes Caucasian 210
16.819 1337 74 15 Male Yes Asian 0
39.11 3189 72 12 Male No Asian 0
107.986 6033 64 14 Male Yes Caucasian 227
13.561 3261 37 19 Male Yes Asian 297
34.537 3271 57 17 Female Yes Asian 47
28.575 2959 60 11 Female No AA 0
46.007 6637 42 14 Male Yes Caucasian 1046
69.251 6386 30 12 Female Yes Asian 768
16.482 3326 41 15 Male No Caucasian 271
40.442 4828 81 8 Female No AA 510
35.177 2117 62 16 Female No Caucasian 0
91.362 9113 47 17 Male Yes Asian 1341
27.039 2161 40 17 Female No Caucasian 0
23.012 1410 81 16 Male No Caucasian 0
27.241 1402 67 15 Female Yes Asian 0
148.08 8157 83 13 Male Yes Caucasian 454
62.602 7056 84 11 Female No Caucasian 904
11.808 1300 77 14 Female No AA 0
29.564 2529 30 12 Female Yes Caucasian 0
27.578 2531 34 15 Female Yes Caucasian 0
26.427 5533 50 15 Female Yes Asian 1404
57.202 3411 72 11 Female No Caucasian 0
123.299 8376 89 17 Male No AA 1259
18.145 3461 56 15 Male Yes AA 255
23.793 3821 56 12 Female Yes AA 868
10.726 1568 46 19 Male Yes Asian 0
23.283 5443 49 13 Male Yes AA 912
21.455 5829 80 12 Female Yes AA 1018
34.664 5835 77 15 Female Yes AA 835
44.473 3500 81 16 Female No AA 8
54.663 4116 70 8 Female No AA 75
36.355 3613 35 9 Male Yes Asian 187
21.374 2073 74 11 Female Yes Caucasian 0
107.841 10384 87 7 Male No AA 1597
39.831 6045 32 12 Female Yes AA 1425
91.876 6754 33 10 Male Yes Caucasian 605
103.893 7416 84 17 Male No Asian 669
19.636 4896 64 10 Female No AA 710
17.392 2748 32 14 Male Yes Caucasian 68
19.529 4673 51 14 Male No Asian 642
17.055 5110 55 15 Female Yes Caucasian 805
23.857 1501 56 16 Male Yes Caucasian 0
15.184 2420 69 11 Female Yes Caucasian 0
13.444 886 44 10 Male Yes Asian 0
63.931 5728 28 14 Female Yes AA 581
35.864 4831 66 13 Female Yes Caucasian 534
41.419 2120 24 11 Female No Caucasian 156
92.112 4612 32 17 Male No Caucasian 0
55.056 3155 31 16 Male Yes AA 0
19.537 1362 34 9 Female Yes Asian 0
31.811 4284 75 13 Female Yes Caucasian 429
56.256 5521 72 16 Female Yes Caucasian 1020
42.357 5550 83 12 Female Yes Asian 653
53.319 3000 53 13 Male No Asian 0
12.238 4865 67 11 Female No Caucasian 836
31.353 1705 81 14 Male Yes Caucasian 0
63.809 7530 56 12 Male Yes Caucasian 1086
13.676 2330 80 16 Female No AA 0
76.782 5977 44 12 Male Yes Asian 548
25.383 4527 46 11 Male Yes Caucasian 570
35.691 2880 35 15 Male No AA 0
29.403 2327 37 14 Female Yes Caucasian 0
27.47 2820 32 11 Female Yes Asian 0
27.33 6179 36 12 Female Yes Caucasian 1099
34.772 2021 57 9 Male No Asian 0
36.934 4270 63 9 Female Yes Caucasian 283
76.348 4697 60 18 Male No Asian 108
14.887 4745 58 12 Male Yes AA 724
121.834 10673 54 16 Male No AA 1573
30.132 2168 52 17 Male No Caucasian 0
24.05 2607 32 18 Male Yes Caucasian 0
22.379 3965 34 14 Female Yes Asian 384
28.316 4391 29 10 Female No Caucasian 453
58.026 7499 67 11 Female No Caucasian 1237
10.635 3584 69 16 Male Yes Asian 423
46.102 5180 81 12 Male Yes AA 516
58.929 6420 66 9 Female Yes AA 789
80.861 4090 29 15 Female Yes Asian 0
158.889 11589 62 17 Female Yes Caucasian 1448
30.42 4442 30 14 Female No AA 450
36.472 3806 52 13 Male No AA 188
23.365 2179 75 15 Male No Asian 0
83.869 7667 83 11 Male No AA 930
58.351 4411 85 16 Female Yes Caucasian 126
55.187 5352 50 17 Female Yes Caucasian 538
124.29 9560 52 17 Female No Asian 1687
28.508 3933 56 14 Male Yes Asian 336
130.209 10088 39 19 Female Yes Caucasian 1426
30.406 2120 79 14 Male Yes AA 0
23.883 5384 73 16 Female Yes AA 802
93.039 7398 67 12 Male Yes AA 749
50.699 3977 84 17 Female No AA 69
27.349 2000 51 16 Female Yes AA 0
10.403 4159 43 7 Male Yes Asian 571
23.949 5343 40 18 Male Yes AA 829
73.914 7333 67 15 Female Yes Caucasian 1048
21.038 1448 58 13 Female Yes Caucasian 0
68.206 6784 40 16 Female No AA 1411
57.337 5310 45 7 Female No Caucasian 456
10.793 3878 29 13 Male No Caucasian 638
23.45 2450 78 13 Male No Caucasian 0
10.842 4391 37 10 Female Yes Caucasian 1216
51.345 4327 46 15 Male No AA 230
151.947 9156 91 11 Female Yes AA 732
24.543 3206 62 12 Female Yes Caucasian 95
29.567 5309 25 15 Male No Caucasian 799
39.145 4351 66 13 Male Yes Caucasian 308
39.422 5245 44 19 Male No AA 637
34.909 5289 62 16 Female Yes Caucasian 681
41.025 4229 79 19 Female Yes Caucasian 246
15.476 2762 60 18 Male No Asian 52
12.456 5395 65 14 Male Yes Caucasian 955
10.627 1647 71 10 Female Yes Asian 195
38.954 5222 76 13 Female No Caucasian 653
44.847 5765 53 13 Female No Asian 1246
98.515 8760 78 11 Female No AA 1230
33.437 6207 44 9 Male No Caucasian 1549
27.512 4613 72 17 Male Yes Asian 573
121.709 7818 50 6 Male Yes Caucasian 701
15.079 5673 28 15 Female Yes Asian 1075
59.879 6906 78 15 Female No Caucasian 1032
66.989 5614 47 14 Female Yes Caucasian 482
69.165 4668 34 11 Female No AA 156
69.943 7555 76 9 Male Yes Asian 1058
33.214 5137 59 9 Male No AA 661
25.124 4776 29 12 Male Yes Caucasian 657
15.741 4788 39 14 Male Yes Asian 689
11.603 2278 71 11 Male Yes Caucasian 0
69.656 8244 41 14 Male Yes AA 1329
10.503 2923 25 18 Female Yes AA 191
42.529 4986 37 11 Male Yes Asian 489
60.579 5149 38 15 Male Yes Asian 443
26.532 2910 58 19 Female Yes Caucasian 52
27.952 3557 35 13 Female Yes Asian 163
29.705 3351 71 14 Female Yes Asian 148
15.602 906 36 11 Male Yes AA 0
20.918 1233 47 18 Female Yes Asian 16
58.165 6617 56 12 Female Yes Caucasian 856
22.561 1787 66 15 Female No Caucasian 0
34.509 2001 80 18 Female Yes AA 0
19.588 3211 59 14 Female No Asian 199
36.364 2220 50 19 Male No Caucasian 0
15.717 905 38 16 Male Yes Caucasian 0
22.574 1551 43 13 Female Yes Caucasian 98
10.363 2430 47 18 Female Yes Asian 0
28.474 3202 66 12 Male Yes Caucasian 132
72.945 8603 64 8 Female No Caucasian 1355
85.425 5182 60 12 Male Yes AA 218
36.508 6386 79 6 Female Yes Caucasian 1048
58.063 4221 50 8 Male No AA 118
25.936 1774 71 14 Female No Asian 0
15.629 2493 60 14 Male Yes Asian 0
41.4 2561 36 14 Male Yes Caucasian 0
33.657 6196 55 9 Female No Caucasian 1092
67.937 5184 63 12 Male Yes Asian 345
180.379 9310 67 8 Female Yes Asian 1050
10.588 4049 66 13 Female Yes Caucasian 465
29.725 3536 52 15 Female No AA 133
27.999 5107 55 10 Male Yes Caucasian 651
40.885 5013 46 13 Female Yes AA 549
88.83 4952 86 16 Female Yes Caucasian 15
29.638 5833 29 15 Female Yes Asian 942
25.988 1349 82 12 Male No Caucasian 0
39.055 5565 48 18 Female Yes Caucasian 772
15.866 3085 39 13 Male No Caucasian 136
44.978 4866 30 10 Female No Caucasian 436
30.413 3690 25 15 Female No Asian 728
16.751 4706 48 14 Male No Asian 1255
30.55 5869 81 9 Female No AA 967
163.329 8732 50 14 Male Yes Caucasian 529
23.106 3476 50 15 Female No Caucasian 209
41.532 5000 50 12 Male Yes Caucasian 531
128.04 6982 78 11 Female Yes Caucasian 250
54.319 3063 59 8 Female No Caucasian 269
53.401 5319 35 12 Female No AA 541
36.142 1852 33 13 Female No AA 0
63.534 8100 50 17 Female Yes Caucasian 1298
49.927 6396 75 17 Female Yes Caucasian 890
14.711 2047 67 6 Male Yes Caucasian 0
18.967 1626 41 11 Female Yes Asian 0
18.036 1552 48 15 Female No Caucasian 0
60.449 3098 69 8 Male Yes Caucasian 0
16.711 5274 42 16 Female Yes Asian 863
10.852 3907 30 9 Male No Caucasian 485
26.37 3235 78 11 Male Yes Asian 159
24.088 3665 56 13 Female Yes Caucasian 309
51.532 5096 31 15 Male Yes Caucasian 481
140.672 11200 46 9 Male Yes AA 1677
42.915 2532 42 13 Male Yes Asian 0
27.272 1389 67 10 Female Yes Caucasian 0
65.896 5140 49 17 Female Yes Caucasian 293
55.054 4381 74 17 Male Yes Asian 188
20.791 2672 70 18 Female No AA 0
24.919 5051 76 11 Female Yes AA 711
21.786 4632 50 17 Male Yes Caucasian 580
31.335 3526 38 7 Female No Caucasian 172
59.855 4964 46 13 Female Yes Caucasian 295
44.061 4970 79 11 Male Yes AA 414
82.706 7506 64 13 Female Yes Asian 905
24.46 1924 50 14 Female Yes Asian 0
45.12 3762 80 8 Male Yes Caucasian 70
75.406 3874 41 14 Female Yes Asian 0
14.956 4640 33 6 Male No Asian 681
75.257 7010 34 18 Female Yes Caucasian 885
33.694 4891 52 16 Male No AA 1036
23.375 5429 57 15 Female Yes Caucasian 844
27.825 5227 63 11 Male Yes Caucasian 823
92.386 7685 75 18 Female Yes Asian 843
115.52 9272 69 14 Male No AA 1140
14.479 3907 43 16 Male Yes Caucasian 463
52.179 7306 57 14 Male No Asian 1142
68.462 4712 71 16 Male Yes Caucasian 136
18.951 1485 82 13 Female No Caucasian 0
27.59 2586 54 16 Male Yes AA 0
16.279 1160 78 13 Male Yes AA 5
25.078 3096 27 15 Female Yes Caucasian 81
27.229 3484 51 11 Male No Caucasian 265
182.728 13913 98 17 Male Yes Caucasian 1999
31.029 2863 66 17 Male Yes Asian 415
17.765 5072 66 12 Female Yes Caucasian 732
125.48 10230 82 16 Male Yes Caucasian 1361
49.166 6662 68 14 Female No Asian 984
41.192 3673 54 16 Female Yes Caucasian 121
94.193 7576 44 16 Female Yes Caucasian 846
20.405 4543 72 17 Male No Asian 1054
12.581 3976 48 16 Male Yes Caucasian 474
62.328 5228 83 15 Male No Caucasian 380
21.011 3402 68 17 Male Yes AA 182
24.23 4756 64 15 Female Yes Caucasian 594
24.314 3409 23 7 Female Yes Caucasian 194
32.856 5884 68 13 Male No Caucasian 926
12.414 855 32 12 Male Yes AA 0
41.365 5303 45 14 Male No Caucasian 606
149.316 10278 80 16 Male No AA 1107
27.794 3807 35 8 Female Yes AA 320
13.234 3922 77 17 Female Yes Caucasian 426
14.595 2955 37 9 Male Yes AA 204
10.735 3746 44 17 Female Yes Caucasian 410
48.218 5199 39 10 Male Yes Asian 633
30.012 1511 33 17 Male Yes Caucasian 0
21.551 5380 51 18 Male Yes Asian 907
160.231 10748 69 17 Male No Caucasian 1192
13.433 1134 70 14 Male Yes Caucasian 0
48.577 5145 71 13 Female Yes Asian 503
30.002 1561 70 13 Female Yes Caucasian 0
61.62 5140 71 9 Male Yes Caucasian 302
104.483 7140 41 14 Male Yes AA 583
41.868 4716 47 18 Male No Caucasian 425
12.068 3873 44 18 Female Yes Asian 413
180.682 11966 58 8 Female Yes AA 1405
34.48 6090 36 14 Male No Caucasian 962
39.609 2539 40 14 Male Yes Asian 0
30.111 4336 81 18 Male Yes Caucasian 347
12.335 4471 79 12 Male Yes AA 611
53.566 5891 82 10 Female No Caucasian 712
53.217 4943 46 16 Female Yes Asian 382
26.162 5101 62 19 Female No AA 710
64.173 6127 80 10 Male Yes Caucasian 578
128.669 9824 67 16 Male Yes Asian 1243
113.772 6442 69 15 Male Yes Caucasian 790
61.069 7871 56 14 Male Yes Caucasian 1264
23.793 3615 70 14 Male No AA 216
89 5759 37 6 Female No Caucasian 345
71.682 8028 57 16 Male Yes Caucasian 1208
35.61 6135 40 12 Male No Caucasian 992
39.116 2150 75 15 Male No Caucasian 0
19.782 3782 46 16 Female No Caucasian 840
55.412 5354 37 16 Female Yes Caucasian 1003
29.4 4840 76 18 Female Yes Caucasian 588
20.974 5673 44 16 Female Yes Caucasian 1000
87.625 7167 46 10 Female No AA 767
28.144 1567 51 10 Male Yes Caucasian 0
19.349 4941 33 19 Male Yes Caucasian 717
53.308 2860 84 10 Male Yes Caucasian 0
115.123 7760 83 14 Female No AA 661
101.788 8029 84 11 Male Yes Caucasian 849
24.824 5495 33 9 Male No Caucasian 1352
14.292 3274 64 9 Male Yes Caucasian 382
20.088 1870 76 16 Male No AA 0
26.4 5640 58 15 Female No Asian 905
19.253 3683 57 10 Male No AA 371
16.529 1357 62 9 Male No Asian 0
37.878 6827 80 13 Female No Caucasian 1129
83.948 7100 44 18 Male No Caucasian 806
135.118 10578 81 15 Female Yes Asian 1393
73.327 6555 43 15 Female No Caucasian 721
25.974 2308 24 10 Male No Asian 0
17.316 1335 65 13 Male No AA 0
49.794 5758 40 8 Male No Caucasian 734
12.096 4100 32 13 Male Yes Caucasian 560
13.364 3838 65 17 Male No AA 480
57.872 4171 67 12 Female Yes Caucasian 138
37.728 2525 44 13 Male Yes Caucasian 0
18.701 5524 64 7 Female No Asian 966