Subset data should be random sample of given data
Bank_of_Delaware_1 Credit Score lncome($) Age Residence Education 518 533 561 564 573 578 591 594 597 600 604 604 607 614 615 617 617 621 623 625 625 633 636 639 642 643 644 650 654 655 657 659 662 669 675 675 675 677 678 679 687 688 688 693 693 695 695 699 699 702 703 709 644 1033 1073 1100 538 781 1403 1566 1258 1159 1231 985 1260 1114 1112 1177 1201 1867 1494 625 1560 1365 683 794 1252 1099 924 1323 1620 915 998 1587 796 1405 1479 1419 889 994 1135 1810 1105 1140 710 1869 1122 1210 1158 1385 1376 1802 1699 819 38 Rent 29 Rent 33 Own 46 Own 40 Rent 34 Own 56 Own 39 Rent 50 Rent 35 Rent 36 Own 37 Rent 41 Own 45 Rent 44 Rent 57 Own 39 Rent 34 Rent 44 Own 33 Rent 41 Own 37 Own 41 Own 56 Own 42 Own 49 Own 53 Rent 43 Own 53 Rent 43 Rent 43 Own 38 Own 44 Own 39 Rent 44 Rent 51 Rent 25 Own 45 Rent 33 Own 46 Own 46 Rent 47 Own 42 Own 49 Own 50 Rent 34 Own 51 Rent 44 Rent 51 Rent 52 Own 65 Rent 42 Own High school High school High school Bachelor Bachelor High school Bachelor Graduate High school High school High school Bachelor Bachelor High school Bachelor High school Bachelor Bachelor Bachelor Bachelor High school High school Bachelor Bachelor High school Bachelor Bachelor Bachelor Graduate Bachelor Graduate Graduate High school Bachelor Bachelor Bachelor Graduate Bachelor Bachelor High school Graduate Bachelor Bachelor Bachelor Bachelor Bachelor Bachelor Graduate High school High school Bachelor Bachelor Page 1 712 717 731 737 749 753 784 810 1025 1568 1521 1293 1088 906 1418 1393 43 Own 54 Own 55 Own 41 Own 34 Own 58 Own 59 Own 52 Own Bank_of_De|aware_1 Bachelor High school Bachelor Bachelor Bachelor Bachelor Bachelor Graduate Page 2 (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 a = 0.06. (c) Is age a significant predictor? Show details of the hypothesis test. Use a = 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. (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.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 le \"Bank of Delaware 1\" in the d21 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
