Subset the data to select only those individuals who lived in an urban area Reduce the dimensionality of the data by converting numerical variables such as age, height, weight, number of years of education, number of siblings, family size, number of weeks employed, self esteem scale, and income into a smaller set of principal components that retain at least 90 of the information in the original data 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 NumberSpouses 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 1 3 18 1 10 12 3 0 0 1 1 0 0 5 20 1 1 1 0 1 12 52 1 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 1 7 15 1 12 12 1 0 0 1 1 0 1 3 26 65 131 0 1 1 25000 3 12 52 2 8 21 1 9 6 7 0 0 1 0 0 0 3 23 65 179 1 1 1 27400 3 13 52 2 9 16 1 12 10 4 0 0 1 1 0 1 6 26 66 145 1 1 1 52000 1 14 52 1 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 0 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 1 14 16 1 12 12 2 0 0 1 1 0 0 5 21 67 135 1 1 1 48000 2 18 52 1 15 15 1 12 12 1 0 0 1 1 0 1 4 23 73 185 1 0 1 0 1 16 0 1 16 21 1 12 12 3 0 0 1 1 0 0 4 25 63 130 1 1 1 38000 1 13 52 1 17 22 1 12 15 2 0 0 1 0 0 1 2 24 69 160 1 1 0 48000 0 13 52 1 18 21 1 12 16 2 0 0 1 0 0 1 1 28 69 155 1 1 1 120000 3 13 52 2 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 1 21 18 1 12 18 2 0 0 1 1 0 0 5 28 62 133 1 1 1 82000 1 16 52 1 22 16 1 12 12 1 0 0 1 1 0 0 4 17 64 110 0 1 1 36000 1 16 52 1 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 1 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 2 28 15 1 14 20 1 0 0 1 0 0 0 4 19 65 114 0 1 1 24000 3 13 52 3 29 19 1 0 4 1 0 0 1 0 1 0 4 30 67 146 0 1 1 50000 1 12 52 1

Question: Subset the data to select only those individuals who lived in an urban area. Reduce the dimensionality of the data by converting numerical variables such

Subset the data to select only those individuals who lived in an urban area. Reduce the dimensionality of the data by converting numerical variables such as age, height, weight, number of years of education, number of siblings, family size, number of weeks employed, self-esteem scale, and income into a smaller set of principal components that retain at least 90% of the information in the original data.

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	NumberSpouses
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	1
3	18	1	10	12	3	0	0	1	1	0	0	5	20			1	1	1	0	1	12	52	1
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	1
7	15	1	12	12	1	0	0	1	1	0	1	3	26	65	131	0	1	1	25000	3	12	52	2
8	21	1	9	6	7	0	0	1	0	0	0	3	23	65	179	1	1	1	27400	3	13	52	2
9	16	1	12	10	4	0	0	1	1	0	1	6	26	66	145	1	1	1	52000	1	14	52	1
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	0
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	1
14	16	1	12	12	2	0	0	1	1	0	0	5	21	67	135	1	1	1	48000	2	18	52	1
15	15	1	12	12	1	0	0	1	1	0	1	4	23	73	185	1	0	1	0	1	16	0	1
16	21	1	12	12	3	0	0	1	1	0	0	4	25	63	130	1	1	1	38000	1	13	52	1
17	22	1	12	15	2	0	0	1	0	0	1	2	24	69	160	1	1	0	48000	0	13	52	1
18	21	1	12	16	2	0	0	1	0	0	1	1	28	69	155	1	1	1	120000	3	13	52	2
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	1
21	18	1	12	18	2	0	0	1	1	0	0	5	28	62	133	1	1	1	82000	1	16	52	1
22	16	1	12	12	1	0	0	1	1	0	0	4	17	64	110	0	1	1	36000	1	16	52	1
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	1
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	2
28	15	1	14	20	1	0	0	1	0	0	0	4	19	65	114	0	1	1	24000	3	13	52	3
29	19	1	0	4	1	0	0	1	0	1	0	4	30	67	146	0	1	1	50000	1	12	52	1

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