Subset the data to include only those individuals who lived in an urban area. Predict whether or
Question:
Subset the data to include only those individuals who lived in an urban area. Predict whether or not an individual’s marriage will end up in a divorce, a separation, or a remarriage using predictor variables such as sex, parents’ education, height, weight, number of years of education, self-esteem scale, and whether the person is outgoing as a kid and/or adult.
ID | Age | Urban | Mother_Edu | Father_Edu | Siblings | Black | Hispanic | White | Christian | WomenPlace | Male | FamilySize | Self_Esteem | Height | Weight | Outgoing_Kid | Outgoing_Adult | HealthPlan | Income | Marital_Status | Education | WeeksEmployed |
1 | 21 | 1 | 8 | 8 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 5 | 65 | |||||||||
2 | 20 | 1 | 5 | 8 | 8 | 0 | 0 | 1 | 1 | 1 | 0 | 5 | 16 | 62 | 120 | 0 | 1 | 1 | 0 | 1 | 12 | 0 |
3 | 18 | 1 | 10 | 12 | 3 | 0 | 0 | 1 | 1 | 0 | 0 | 5 | 20 | 1 | 1 | 1 | 0 | 1 | 12 | 52 | ||
4 | 17 | 1 | 11 | 12 | 3 | 0 | 0 | 1 | 1 | 0 | 0 | 5 | 67 | 110 | 0 | 1 | ||||||
5 | 20 | 1 | 12 | 12 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 4 | 23 | 63 | 130 | |||||||
6 | 19 | 1 | 12 | 12 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 4 | 27 | 64 | 200 | 1 | 1 | 1 | 40000 | 1 | 16 | 52 |
7 | 15 | 1 | 12 | 12 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 3 | 26 | 65 | 131 | 0 | 1 | 1 | 25000 | 3 | 12 | 52 |
8 | 21 | 1 | 9 | 6 | 7 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 23 | 65 | 179 | 1 | 1 | 1 | 27400 | 3 | 13 | 52 |
9 | 16 | 1 | 12 | 10 | 4 | 0 | 0 | 1 | 1 | 0 | 1 | 6 | 26 | 66 | 145 | 1 | 1 | 1 | 52000 | 1 | 14 | 52 |
10 | 19 | 1 | 12 | 12 | 3 | 0 | 0 | 1 | 1 | 0 | 0 | 3 | 19 | 66 | 115 | 0 | 1 | |||||
11 | 20 | 1 | 12 | 12 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 3 | 71 | 155 | 1 | 1 | 1 | 55000 | 0 | 16 | 52 | |
12 | 20 | 1 | 15 | 12 | 3 | 0 | 0 | 1 | 1 | 0 | 0 | 3 | 30 | 66 | 118 | 0 | 1 | |||||
13 | 21 | 1 | 12 | 16 | 2 | 0 | 0 | 1 | 1 | 0 | 1 | 5 | 25 | 71 | 180 | 0 | 1 | 1 | 60000 | 2 | 16 | 52 |
14 | 16 | 1 | 12 | 12 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 5 | 21 | 67 | 135 | 1 | 1 | 1 | 48000 | 2 | 18 | 52 |
15 | 15 | 1 | 12 | 12 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 4 | 23 | 73 | 185 | 1 | 0 | 1 | 0 | 1 | 16 | 0 |
16 | 21 | 1 | 12 | 12 | 3 | 0 | 0 | 1 | 1 | 0 | 0 | 4 | 25 | 63 | 130 | 1 | 1 | 1 | 38000 | 1 | 13 | 52 |
17 | 22 | 1 | 12 | 15 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 24 | 69 | 160 | 1 | 1 | 0 | 48000 | 0 | 13 | 52 |
18 | 21 | 1 | 12 | 16 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 28 | 69 | 155 | 1 | 1 | 1 | 120000 | 3 | 13 | 52 |
19 | 22 | 1 | 10 | 12 | 3 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 28 | 64 | 120 | 1 | 1 | |||||
20 | 20 | 1 | 12 | 18 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 5 | 21 | 64 | 120 | 0 | 1 | 1 | 52000 | 1 | 17 | 52 |
21 | 18 | 1 | 12 | 18 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 5 | 28 | 62 | 133 | 1 | 1 | 1 | 82000 | 1 | 16 | 52 |
22 | 16 | 1 | 12 | 12 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 4 | 17 | 64 | 110 | 0 | 1 | 1 | 36000 | 1 | 16 | 52 |
23 | 21 | 1 | 12 | 12 | 2 | 0 | 0 | 1 | 1 | 0 | 1 | 5 | 72 | 175 | ||||||||
24 | 18 | 1 | 12 | 12 | 2 | 0 | 0 | 1 | 1 | 0 | 1 | 5 | 28 | 71 | 180 | 1 | 1 | |||||
25 | 20 | 1 | 14 | 16 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 19 | 67 | 125 | 0 | 1 | 1 | 20000 | 1 | 14 | 52 |
26 | 17 | 1 | 16 | 17 | 2 | 0 | 0 | 1 | 1 | 0 | 1 | 4 | 25 | 67 | 136 | 1 | 1 | |||||
27 | 19 | 1 | 14 | 20 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 4 | 23 | 63 | 123 | 1 | 1 | 1 | 13126 | 1 | 16 | 44 |
28 | 15 | 1 | 14 | 20 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | 19 | 65 | 114 | 0 | 1 | 1 | 24000 | 3 | 13 | 52 |
29 | 19 | 1 | 0 | 4 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 4 | 30 | 67 | 146 | 0 | 1 | 1 | 50000 | 1 | 12 | 52 |
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Business Analytics
ISBN: 9781265897109
2nd Edition
Authors: Sanjiv Jaggia, Alison Kelly, Kevin Lertwachara, Leida Chen
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