This case study concerns a bank's efforts to calculate creditrisk scores (These are opposite of credit scores. Higher the valuethe riskier the customer) A loan officer at a bank needs to be ableto identify characteristics that are indicative of people who arelikely to default on loans and use those characteristics toidentify good and bad credit risks. The loan officer also needs tobe able to better quantify an individual’s credit risk level.

Information on 700 past customers is given in the file alongwith data for the following variables:

Age: customer age in years.

Employment: years that the customer has beenwith his/her current employer

Address: years that the customer has lived athis/her current address

Income: household annual income (in $1,000)

Debt _to _Income: debt to income ratio(x100)

Risk _Score: Credit risk score (the higher thescore, the more risky)

Default _Indicator: an indicator of whether thecustomer had previously defaulted

Test the hypothesis that the average credit risk score ofcustomers who have previously defaulted on their loans is higherthan those who haven’t defaulted on their loans. (Divide the creditrisk score into two groups those who previously defaulted and thosewho didn’t and then compare the two groups.)

You would like to build a regression model to predict the creditrisk score based on all the other variables. What is the regressionequation?

What is the predicted credit risk score of a customer aged 40,who has been with their employer for 10 years, has lived at thesame address for 3 years, has an income of $200,000, debt to incomeratio of 2.5 and has previously defaulted on a loan. Will theprediction be accurate? Explain

Compute the coefficient of determination (R2) and fullyinterpret its meaning.

Interpret the meaning of the coefficients of the independentvariables income and address.

Which independent variables significant? Defend your answer.

Age in years	Years with current employer	Years at current address	Household income in thousands	Debt to income ratio (x100)	Previously Defaulted	Credit Risk Score
41	17	12	176	9.3	1	808.3943274
27	10	6	31	17.3	0	198.2974762
40	15	14	55	5.5	0	10.0361081
41	15	14	120	2.9	0	22.13828376
24	2	0	28	17.3	1	781.5883142
41	5	5	25	10.2	0	216.7089415
39	20	9	67	30.6	0	185.9601084
43	12	11	38	3.6	0	14.70865349
24	3	4	19	24.4	1	748.0412036
36	0	13	25	19.7	0	815.0570131
27	0	1	16	1.7	0	350.309226
25	4	0	23	5.2	0	239.0539023
52	24	14	64	10	0	9.790173473
37	6	9	29	16.3	0	364.4940475
48	22	15	100	9.1	0	11.87390385
36	9	6	49	8.6	1	96.70407786
36	13	6	41	16.4	1	212.0503906
43	23	19	72	7.6	0	1.404870603
39	6	9	61	5.7	0	104.1453903
41	0	21	26	1.7	0	91.9180135
39	22	3	52	3.2	0	4.373536462
47	17	21	43	5.6	0	3.047352362
28	3	6	26	10	0	293.9321797
29	8	6	27	9.8	0	106.7996198
21	1	2	16	18	1	629.7774553
25	0	2	32	17.6	0	861.3134014
45	9	26	69	6.7	0	16.46115799
43	25	21	64	16.7	0	1.437993467
33	12	8	58	18.4	0	276.7066755
26	2	1	37	14.2	0	503.3218674
45	3	15	20	2.1	0	76.41958523
30	1	10	22	10.5	0	433.6994251
27	2	7	26	6	0	288.7388759
25	8	4	27	14.4	0	231.1006843
25	8	1	35	2.9	0	74.95719559
26	6	7	45	26	0	950.0535168
30	10	4	22	16.1	0	211.9564036
32	12	1	54	14.4	0	335.9969153
28	1	8	24	17.1	1	643.9032953
45	23	5	50	4.2	0	2.268753579
23	7	2	31	6.6	0	132.8782071
34	17	3	59	8	0	31.76854323
42	7	23	41	4.6	0	31.90347933
39	19	5	48	13.1	0	28.07933138
26	0	0	14	7.5	1	511.04996
21	0	1	16	6.8	0	453.6168743
35	13	15	35	4.5	0	10.78188877
47	4	2	26	10.4	0	281.6573725
23	0	2	21	11.4	1	621.7847698