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

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).

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.

,

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

0

1

0

1

Block 0: Beginning Block

Classification Tablea,b

Observed

Predicted

PAST DUE Percentage Correct0 1

Step 0 PAST DUE 0

1

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

63.060 4 . 0 0 0

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.

a.

Classification Tablea

Observed

Predicted

PAST DUE Percentage Correct0 1

Step 1 PAST DUE 0

1

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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