1. Suppose the Bank of Delaware would like to develop a regression model to predict a person's credit score based on his or her age,
1. Suppose the Bank of Delaware would like to develop a regression model to predict a person's credit score based on his or her age, weekly income, and the type of primary residence (whether he or she owns or rents his or her primary residence). The file "Bank of Delaware 1" provides these data for 60 customers. As demonstrated in the lecture, please create a subset data of size 50 and perform your statistical analysis for the subset data. Please note that the subset data should be a random sample of the given data.
(a) State the multiple linear regression model in context of the problem.
(b) Is the multiple linear regression model in part (a) significant? Show details of the hypothesis test. Use ? = 0.06.
(c) Is age a significant predictor? Show details of the hypothesis test. Use ? = 0.06.
(d) Write down the estimated multiple linear regression equations for the two types of primary residences separately.
(e) Provide the detailed interpretations of the coefficients of the slopes of the regression equation in the context of the problem. 1
(f) Compute a 94% confidence interval for the coefficient of age using computer software. Interpret it in the context of the problem. Please verify your result manually. This will help you for your final exam.
(g) Use your estimated regression equation to predict the average credit score for a 38-year-old person who earns $1,200 per week, and owns his or her residence.
Bank_of_Delaware_1 (15) Credit Score Income ($) Age Residence Education 518 644 38 Rent High school 533 1033 29 Rent High school 561 1073 33 Own High school 564 1100 46 Own Bachelor 573 538 40 Rent Bachelor 578 781 34 Own High school 591 1403 56 Own Bachelor 594 1566 39 Rent Graduate 597 1258 50 Rent High school 600 1159 35 Rent High school 604 1231 36 Own High school 604 985 37 Rent Bachelor 607 1260 41 Own Bachelor 614 1114 45 Rent High school 615 1112 44 Rent Bachelor 617 1177 57 Own High school 617 1201 39 Rent Bachelor 621 1867 34 Rent Bachelor 623 1494 44 Own Bachelor 625 625 33 Rent Bachelor 625 1560 41 Own High school 633 1365 37 Own High school 636 683 41 Own Bachelor 639 794 56 Own Bachelor 642 1252 42 Own High school 643 1099 49 Own Bachelor 644 924 53 Rent Bachelor 650 1323 43 Own Bachelor 654 1620 53 Rent Graduate 655 915 43 Rent Bachelor 657 998 43 Own Graduate 659 1587 38 Own Graduate 662 796 44 Own High school 669 1405 39 Rent Bachelor 675 1479 44 Rent Bachelor 675 1419 51 Rent Bachelor 675 889 25 Own Graduate 677 994 45 Rent Bachelor 678 1135 33 Own Bachelor 679 1810 46 Own High school 687 1105 46 Rent Graduate 688 1140 47 Own Bachelor 688 710 42 Own Bachelor 693 1869 49 Own Bachelor 693 1122 50 Rent Bachelor 695 1210 34 Own Bachelor 695 1158 51 Rent Bachelor 699 1385 44 Rent Graduate 699 1376 51 Rent High school 702 1802 52 Own High school 703 1699 65 Rent Bachelor 709 819 42 Own Bachelor 712 1025 43 Own Bachelor 717 1568 54 Own High school 731 1521 55 Own Bachelor 737 1293 41 Own Bachelor 749 1088 34 Own Bachelor 753 906 58 Own Bachelor 784 1418 59 Own Bachelor 810 1393 52 Own GraduateJersey_Shore_Realtors 2 (7) Rental ($) Bedrooms Age Blocks Month 875 2 12 3 June 900 3 2 June 900 2 15 2 June 1090 3 13 1.5 June 1175 3 12 3 June 1250 5 11 2.5 June 1400 3 5 1.5 June 1400 4 8 2 June 1500 3 7 2 June 1600 3 6 2 June 1700 4 11 1.5 June 1800 4 7 3 June 1900 4 20 2 June 2000 4 5 3 June 2000 4 9 1.5 June 2200 5 4 1.5 June 2300 6 12 2.5 June 2500 4 10 2 June 2600 4 5 1 June 4 14 2 June 5 10 1.5 June 4 9 1.5 June 5 10 2 June 4 9 1 June 5 5 1.5 June 7000 6 14 0.5 June 1475 3 6 2 July 1900 3 18 1 July 5 3 1.5 July 5 11 2.5 July 4 9 3 July 3 8 2 July 3 4 2.5 July 4 11 2 July 2900 4 15 1.5 July 3000 3 13 1 July 3000 4 9 2.5 July 3400 4 13 2.5 July 3600 4 11 2 July 3700 4 6 1 July 4000 4 11 1 July 4300 5 9 1.5 July 4700 5 3 1.5 July 4900 5 11 1.5 July 5000 3 9 2 July 5200 5 16 1 July 5700 5 17 1.5 July 6000 6 3 2 July 6500 5 11 1 July 7000 4 9 1.5 July 7200 6 17 1 July 7600 4 12 1 July 8000 5 13 1 July 9000 6 21 0.5 July 10000 5 17 1 July 12900 5 10 0.5 July 1300 2 15 3 August 1700 3 10 2.5 August 1800 3 16 1.5 August 1900 3 11 2 August 2000 4 9 2.5 August 2100 4 6 3 August 2200 4 11 1.5 August 4 5 1.5 August 4 6 2 August 4 13 2 August 4 11 3 August 4 11 4 6 1 August 3500 5 8 2 August 5 10 2 August 5 14 1.5 August 4 10 1.5 August 4 20 2 August 5 8 1 August 5 2 1 August 5 12 2 August 4 13 2.5 August 5 1 1.5 August 5 9 2 August 5400 6 14 2 August 5770 5 10 1.5 August 6000 5 1 2 August 7800 4 12 1 August 8000 5 13 1.5 August 5 16 0.5 August 10500 6 21 1.5 August 12000 4 2 0.5 August
