The data if any needed for this assignment is posted on eLearn. If after giving some thought, you have problems doing this assignment, do not hesitate to email or meet with me. You can

19 May The data if any needed for this assignment is posted on eLearn. If after giving some thought, you have problems doing this assignment, do not hesitate to email or meet with me. You can

Posted at 04:30h in Business & Finance / Marketing by

The data if any needed for this assignment is posted on eLearn. If after giving some thought, you have problems doing this assignment, do not hesitate to email or meet with me. You can discuss with each other, but please finish and write up the answers independently. Please submit a soft copy of your homework (typed answers in this word document and attach the SPSS output file to eLearn).

The National Bank of Fort Worth, Texas wants to examine methods for predicting sub-par payment performance on loans. They have data on unsecured consumer loans made over a 3-day period in October 2013 with a final maturity of 2 years. There are a total of 348 observations in the sample. The data, which have been transformed to provide confidentiality, include the following:

PAST DUE: Coded as 1 if the loan payment is past due and zero otherwise

CBSCORE: Score generated by the CSC Credit reporting agency from 400 to 839 with higher values indicating better credit rating

DEBT: Debt ratio calculated by taking required monthly payments on all debt and dividing it by gross monthly income of applicant and co-applicant. This ratio represents the amount of the applicant’s income that will go towards repayment of debt

GROSS INC: Gross monthly income of applicant and co-applicant

LOAN AMT: Loan Amount

You have been asked to examine the feasibility of predicting past-due loan payment. Report your results to the bank in a two-part report. The report should include an executive summary with a brief non-technical description of your results (less than 1 -page) and an accompanying technical report with the details of your analysis. The data are in an excel file posted on eLearn.

For the report, you should consider the following: Use of logistic to analyze the data; appropriate variables which are useful in predicting performance; the hit-rate in the estimation sample and how it compares with appropriate benchmark criteria.

Sheet1

PAST DUE	CBSCORE	DEBT	GROSS INC	LOAN AMT
0	711	99	717	500
0	652	79	2417	1500
1	654	63	3333.330078125	6547
0	650	62	2125	1800
0	605	57	2249.5	10000
1	774	56	4956.990234375	6000
0	650	56	2333	2200
1	667	55	4500	7060.91015625
0	705	54	2750	11300
0	710	54	3000	1200
1	699	53	1995.5	10000
1	698	53	1001	2000
0	685	52	1125	4000
0	700	51	3201	2500
0	729	51	1516	1300
0	670	50	2421.6599121094	10000
0	671	49	1080	1460
1	713	48	3581.4099121094	6000
0	690	48	1833.3299560547	5000
1	682	48	3041.6599121094	4000
0	601	48	3045	2137.3000488281
1	676	47	3500	11000
0	693	47	3250	6000
0	704	47	2333	6000
1	649	46	4408.16015625	14000
0	731	46	3333.3200683594	10800
1	750	46	2950	7000
1	674	45	3000	13000
0	643	45	6123	10000
0	717	45	3333	2400
0	678	45	1200	2000
1	619	44	2333.330078125	10000
0	720	44	2500	3000
0	688	44	2000	1500
0	666	43	4231.16015625	6000
0	710	43	2040	3500
0	620	43	1875	1000
1	637	43	2580	800
0	703	42	3500	10000
1	710	42	1400	2000
0	765	42	1508	1200
0	711	41	4083	8590.7099609375
1	730	41	4458.330078125	7000
0	695	41	2500	3021
1	735	40	5321.16015625	14000
1	675	40	4499.990234375	11643
0	647	40	5466.580078125	10000
1	657	40	2500	9000
0	748	40	2833	3600
0	721	39	8250	10000
1	508	39	4735	8000
0	744	39	2499.9899902344	7000
1	660	39	1666.6600341797	6000
0	687	39	4502	3650
0	689	39	2367	1000
1	651	38	3033.330078125	12530
1	596	38	2180.580078125	10000
1	612	38	3499.9899902344	4000
0	708	38	3988	3500
0	672	38	1900	1500
0	770	37	2707.4099121094	9000
0	725	37	2460	3832.7800292969
0	731	37	2097.25	3500
0	684	37	1500	3000
0	783	37	1750	1100
1	694	36	4171.830078125	13667
0	656	36	3083.330078125	13000
1	637	36	5838	11500
1	602	36	4166.66015625	10000
0	753	36	3333.330078125	3000
1	694	36	2481	2000
1	722	36	2250	2000
0	710	36	2800	1500
1	741	36	975	200
1	633	35	3315.330078125	12500
1	660	35	2246.830078125	10000
0	735	35	2500	8000
1	627	35	6566.16015625	6000
1	703	35	2620.830078125	6000
0	638	35	4167	3000
0	726	35	2899.9899902344	3000
0	649	35	2501	3000
0	738	35	2150	2000
1	690	35	1400	1100
0	679	35	3199	1000
0	706	34	3608.330078125	14000
1	689	34	5077.91015625	10000
0	701	34	3900	3000
0	658	34	1401	1500
1	699	33	6710.41015625	14000
0	693	33	3166.6599121094	7503.8999023438
1	626	33	2883.330078125	7000
1	653	33	1734.7299804688	6000
0	715	33	2999	4700
0	750	33	5583	4000
0	638	33	2000	2000
1	675	33	3500	1500
1	637	33	1220	1000
1	625	32	5250	14222
1	699	32	3666.6599121094	12610
0	719	32	4041.6599121094	10000
0	726	32	3583.330078125	10000
1	642	32	7166.66015625	6084
0	686	32	2917	4900
1	638	32	2554	4800
0	708	32	2600	2500
1	667	32	5957.330078125	2001
0	665	32	2000	2000
1	715	32	1600	1000
1	698	31	3583.330078125	12000
1	713	31	2400	8000
1	705	31	2675	7000
1	716	31	5416.66015625	6000
0	694	31	3500	4256
0	667	31	2800	2500
1	711	31	893	2001
0	584	31	4700	1000
0	710	30	3389.330078125	13000
0	645	30	2643.1599121094	7000
1	667	30	4981	5000
0	731	30	2300	4000
0	638	30	1400	2000
0	738	30	2000	1600
0	710	30	3750	1500
1	651	30	3300	1300
0	641	30	1350	1000
1	642	29	3166.6599121094	14000
1	665	29	4083.080078125	10000
1	696	29	4000	9000
0	716	29	2886.4099121094	7000
0	766	29	3200	6500
0	716	29	2000	6000
0	672	29	4240	5000
1	765	29	3333.330078125	5000
1	723	29	3177.4099121094	5000
1	746	29	2933.330078125	5000
0	709	29	2500	4000
0	640	29	2324	3000
0	679	29	1226	1400
1	706	29	3345	1000
0	735	28	2583.330078125	14000
1	641	28	3000	12000
1	728	28	3899.9899902344	11000
1	704	28	3416.6599121094	11000
1	653	28	5157.66015625	10850
1	683	28	7833.330078125	10000
1	689	28	5749.990234375	10000
1	700	28	2600	10000
1	694	28	4166.66015625	8500
1	628	28	4416.66015625	7010.9501953125
1	684	28	3916.6599121094	7000
0	644	28	3142.5700683594	7000
1	708	28	4446.66015625	6000
1	690	28	5962.66015625	5000
1	636	28	2666.6599121094	5000
0	681	28	2000	4000
0	680	28	5702	2650
1	734	28	3750	1000
0	742	28	1100	1000
0	723	28	2833.330078125	600
0	774	27	3500	12000
1	743	27	5416.66015625	10000
0	717	27	4499.990234375	10000
1	657	27	2984.580078125	10000
1	698	27	1916.6600341797	10000
0	717	27	3916.6599121094	9000
0	747	27	5766	8325
1	680	27	2889.080078125	6000
0	669	27	3443	5000
0	776	27	5360	3000
1	640	27	2028	1653
1	630	27	1300	1500
1	716	26	4249.990234375	12500
1	623	26	4690.41015625	11000
1	680	26	4708.330078125	10000
0	705	26	2773.25	8000
1	626	26	4083.330078125	7000
0	697	26	3399.5	6000
0	712	26	2500	4000
1	713	26	2600	3200
1	609	26	4532	3000
1	663	26	2666.6599121094	3000
0	665	26	488	2000
0	762	26	1207	1500
0	738	26	1282	1000
1	704	25	2654.6599121094	10152
0	680	25	2097.25	4771
0	711	25	5300	3000
0	681	24	4583.330078125	12500
1	688	24	5000	10000
1	709	24	3833.3200683594	10000
0	738	24	3105.5	10000
1	688	24	2567	10000
1	690	24	3148	5000
1	612	24	4333	4061.2800292969
0	710	24	8000	4000
0	762	24	4500	4000
1	657	24	3500	3500
1	729	24	2000	3000
0	625	24	2900	2500
0	785	24	3500	2047.6300048828
0	740	24	3167	2000
0	682	24	2500	2000
0	664	23	2500	10000
0	680	23	4050	9873
1	679	23	5350	8749
0	680	23	2916.6599121094	8000
1	742	23	3964.330078125	6000
0	700	23	3110.4899902344	6000
1	626	23	991	5000
0	706	23	6493	3500
1	714	23	4441.990234375	3000
1	744	22	6166.66015625	10000
0	766	22	3250	9000
1	658	22	2500	7000
0	627	22	2500	4011
1	663	22	1554	2713.6999511719
1	716	22	2160	2500
0	664	22	1127	1500
0	672	22	1123	1500
0	711	21	4305	10690
1	639	21	7416.66015625	10000
1	654	21	5677	10000
0	775	21	4166.66015625	10000
1	643	21	3154.6599121094	10000
0	727	21	2779.580078125	10000
0	728	21	1151	8000
1	663	21	5083.330078125	7000
1	601	21	6250	6860
0	751	21	2827	6400
0	743	21	6583	6000
0	687	21	3740	6000
1	636	21	2416.6599121094	5000
0	784	21	5000	3900
1	641	21	2166	3500
0	687	21	2250	2000
0	723	20	5412.330078125	12500
1	661	20	4875	10000
0	695	20	6249.990234375	9000
1	646	20	4029.1599121094	8000
0	723	20	2359.830078125	8000
0	696	20	2166.6599121094	6000
1	662	20	1700	6000
1	657	20	4966.3198242188	4000
1	720	20	4166.66015625	3000
1	666	20	2600	3000
0	804	20	2360	2100
0	762	20	1754	2000
1	665	20	1333	1000
0	747	19	5749.990234375	12500
0	681	19	4500	10000
1	661	19	4333.330078125	9653
0	692	19	4900	7000
1	779	19	7397	5000
1	603	19	5833.3198242188	4000
1	648	19	3000	3500
0	788	19	2435	3000
0	774	19	2500	2500
0	582	19	8175	2100
1	666	19	2586.25	1000
1	637	18	2238	7000
0	647	18	3681	1000
1	589	18	2300	500
1	747	17	6408.330078125	12500
0	727	17	3916.6599121094	8500
0	725	17	3833.330078125	7500
1	739	17	2103.330078125	5000
0	694	17	3333	2000
0	715	17	1011	1750
0	781	16	3333	8500
1	729	16	3250	6000
1	626	16	1721	5000
0	667	16	6250	4060
1	659	16	2550	3503.3500976563
0	728	16	3160	3000
0	784	16	3333	2500
0	775	16	3033	2300
0	737	16	3000	1800
0	759	16	3007	1200
0	728	15	5400	5000
0	714	15	1820	4000
0	730	15	2500	3000
0	713	15	2080	2068
0	680	15	3700	1200
1	631	15	509	1000
0	742	14	6018.330078125	10000
1	696	14	3750	10000
0	720	14	7166.66015625	9000
0	677	14	4583.330078125	7500
0	789	14	2800	3000
1	709	14	1250	3000
1	637	14	4608	2500
1	651	14	2000	2000
0	719	14	1833	2000
0	592	14	5000	1724
0	729	14	2800	1500
0	762	14	1290	1500
0	711	13	8291.66015625	12500
1	769	13	5000	7500
0	725	13	1307	3775
0	711	12	3958	2000
0	726	11	2712	7000
0	738	11	4750	6000
1	611	11	4333	3000
1	537	11	3500	2000
0	699	11	4583	1000
0	622	11	800	1000
1	682	11	589	595
0	692	10	3166.6599121094	8500
1	749	10	6280.240234375	7500
1	662	10	2400	4000
0	718	10	2203	4000
0	780	10	3109	3000
1	659	10	4695	2500
1	712	10	3768.330078125	2001
0	716	9	2583.330078125	10085
0	705	9	3000	6000
0	646	9	1453	1500
0	766	9	2291.6599121094	1000
1	700	8	5458.330078125	5000
0	695	8	4447.66015625	4000
0	686	8	2950	3000
0	713	8	880	2000
1	695	7	5833.330078125	5000
1	648	6	5000	11083
0	649	6	7083.330078125	5000
0	790	6	1500	2723
1	628	6	1250	1500
1	631	6	4615	1000
1	701	5	1800	4000
1	774	5	4583.330078125	3000
0	743	5	742	2500
0	656	5	2218	2000
0	721	5	1755	1700
0	738	5	1560	1000
0	735	5	1125	1000
1	620	4	2125	10000
0	663	4	8190	3000
0	704	4	4333	1000
0	716	3	1600	700
0	667	3	1400	600
0	727	2	1348	9133
0	722	2	1541	4059.4699707031
0	685	2	3556	3500
0	706	2	3200	1500
0	765	1	1731	1500
0	717	0	1053	2622
0	742	0	1850	2500

Individual Assignment on Logistic Regression

The data if any needed for this assignment is posted on eLearn. If after giving some thought, you have problems doing this assignment, do not hesitate to email or meet with me. You can discuss with each other, but please finish and write up the answers independently. Please submit a soft copy of your homework (typed answers in this word document and attach the SPSS output file to eLearn).

PAST DUE: Coded as 1 if the loan payment is past due and zero otherwise

CBSCORE: Score generated by the CSC Credit reporting agency from 400 to 839 with higher values indicating better credit rating

GROSS INC: Gross monthly income of applicant and co-applicant

LOAN AMT: Loan Amount

You have been asked to examine the feasibility of predicting past-due loan payment. Report your results to the bank in a two-part report. The report should include an executive summary with a brief non-technical description of your results (less than 1-page) and an accompanying technical report with the details of your analysis. The data are in an excel file posted on eLearn.

Logistic Regression

[DataSet1]

Case Processing Summary

Unweighted Casesa N Percent

Selected Cases Included in Analysis

Missing Cases

Total

Unselected Cases

Total

3 4 8 1 0 0 . 0

0 . 0

3 4 8 1 0 0 . 0

0 . 0

3 4 8 1 0 0 . 0

If weight is in effect, see classification table for the total number of cases.a.

Dependent Variable Encoding

Original Value Internal Value

Block 0: Beginning Block

Classification Tablea,b

Observed

Predicted

PAST DUE Percentage Correct0 1

Step 0 PAST DUE 0

Overall Percentage

1 9 8 0 1 0 0 . 0

1 5 0 0 . 0

5 6 . 9

Constant is included in the model.a.

The cut value is .500b.

Variables in the Equation

B S.E. Wald d f Sig. Exp(B)

Step 0 Constant – . 2 7 8 . 1 0 8 6 . 5 7 8 1 . 0 1 0 . 7 5 8

Page 1

Variables not in the Equation

Score d f Sig.

Step 0 Variables CBSCORE

DEBT

GROSS INC

LOAN AMT

Overall Statistics

38.886 1 . 0 0 0

. 4 8 8 1 . 4 8 5

7 . 4 3 4 1 . 0 0 6

20.174 1 . 0 0 0

58.080 4 . 0 0 0

Block 1: Method = Enter

Omnibus Tests of Model Coefficients

Chi-square d f Sig.

Step 1 Step

Block

Model

63.060 4 . 0 0 0

Model Summary

Step -2 Log

likelihood Cox & Snell R

Square Nagelkerke R

Square

1 412.728 a . 1 6 6 . 2 2 2

Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.

Classification Tablea

Observed

Predicted

PAST DUE Percentage Correct0 1

Step 1 PAST DUE 0

Overall Percentage

1 5 5 4 3 7 8 . 3

6 2 8 8 5 8 . 7

6 9 . 8

The cut value is .500a.

Page 2

Variables in the Equation

B S.E. Wald d f Sig. Exp(B)

Step 1a CBSCORE

DEBT

GROSS INC

LOAN AMT

Constant

– . 0 1 7 . 0 0 3 35.000 1 . 0 0 0 . 9 8 3

– . 0 0 4 . 0 0 9 . 2 1 0 1 . 6 4 7 . 9 9 6

. 0 0 0 . 0 0 0 . 4 7 9 1 . 4 8 9 1 . 0 0 0

. 0 0 0 . 0 0 0 14.540 1 . 0 0 0 1 . 0 0 0

10.672 2 . 0 3 2 27.595 1 . 0 0 0 43141.305

Variable(s) entered on step 1: CBSCORE, DEBT, GROSS INC, LOAN AMT.a.

Page 3

Logistic Regression

Title
Active Dataset
Case Processing Summary
Dependent Variable Encoding
Block 0: Beginning Block

Title
Classification Table
Variables in the Equation
Variables not

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19 May The data if any needed for this assignment is posted on eLearn. If after giving some thought, you have problems doing this assignment, do not hesitate to email or meet with me. You can

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