Take the scenario presented in the Bank Loan case study. For this scenario, come up with 1) the Business opportunity, 2) Business Objectives, 3) Business Questions, 4) Data Analytic questions, 5) Data collection plan of action and 6) the actual hypothesis tests or regression analyses you would do to answer the questions in step 4. (You don't have to do the hypothesis tests or regression analysis here. Just mention what you would do.) Put your answers in a word file.

This case study concerns a bank's efforts to reduce the rate of loan defaults. A loan officer at a bank needs to be able to identify characteristics that are indicative of people who are likely to default on loans and use those characteristics to identify good and bad credit risks. The loan officer also needs to be able to better quantify an individuals credit risk level. Information on 850 past and prospective customers in contained in bankloan. The first 700 cases are customers who were previously given loans. The last 150 cases are prospective customers that the bank needs to assess. The data set includes the following variables: 1. Age: customer age in years. 2. College: an indicator of whether the customer has a college education 3. Employment: years that the customer has been with his/her current employer 4. Address: years that the customer has lived at his/her current address 5. Income: household annual income (in $1,000) 6. Debt _to _Income: debt to income ratio (x100) 7. Credit _Debt: credit card debt (in $1,000) 8. Other _Debt: other debt (in $1,000) 9. Risk _Score: Credit risk score (the higher the score, the more risky) 10. Default _Indicator: an indicator of whether the customer had previously defaulted

Age in years	College Education	Years with current employer	Years at current address	Household income in thousands	Debt to income ratio (x100)	Credit card debt in thousands	Other debt in thousands	Credit Risk Score	Previously Defaulted
41	1	17	12	176	9.3	11.359392	5.008608	808.3943274	1
27	0	10	6	31	17.3	1.362202	4.000798	198.2974762	0
40	0	15	14	55	5.5	0.856075	2.168925	10.0361081	0
41	0	15	14	120	2.9	2.65872	0.82128	22.13828376	0
24	0	2	0	28	17.3	1.787436	3.056564	781.5883142	1
41	0	5	5	25	10.2	0.3927	2.1573	216.7089415	0
39	0	20	9	67	30.6	3.833874	16.668126	185.9601084	0
43	0	12	11	38	3.6	0.128592	1.239408	14.70865349	0
24	0	3	4	19	24.4	1.358348	3.277652	748.0412036	1
36	0	0	13	25	19.7	2.7777	2.1473	815.0570131	0
27	0	0	1	16	1.7	0.182512	0.089488	350.309226	0
25	0	4	0	23	5.2	0.252356	0.943644	239.0539023	0
52	0	24	14	64	10	3.9296	2.4704	9.790173473	0
37	0	6	9	29	16.3	1.715901	3.011099	364.4940475	0
48	0	22	15	100	9.1	3.7037	5.3963	11.87390385	0
36	0	9	6	49	8.6	0.817516	3.396484	96.70407786	1
36	0	13	6	41	16.4	2.918216	3.805784	212.0503906	1
43	0	23	19	72	7.6	1.181952	4.290048	1.404870603	0
39	0	6	9	61	5.7	0.563274	2.913726	104.1453903	0
41	1	0	21	26	1.7	0.099008	0.342992	91.9180135	0
39	0	22	3	52	3.2	1.154816	0.509184	4.373536462	0
47	0	17	21	43	5.6	0.587552	1.820448	3.047352362	0
28	0	3	6	26	10	0.4316	2.1684	293.9321797	0
29	0	8	6	27	9.8	0.402192	2.243808	106.7996198	0
21	0	1	2	16	18	0.24192	2.63808	629.7774553	1
25	1	0	2	32	17.6	2.14016	3.49184	861.3134014	0
45	0	9	26	69	6.7	0.707319	3.915681	16.46115799	0
43	0	25	21	64	16.7	0.951232	9.736768	1.437993467	0
33	0	12	8	58	18.4	3.084208	7.587792	276.7066755	0