The accompanying Credit Approval Decisions data provide information on credit history for a sample of banking...

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The accompanying Credit Approval Decisions data provide information on credit history for a sample of banking customers. Use regression analysis to identify the best model for predicting the credit score as a function of the other numerical variables, using both the p-value and 1 statistic criteria. How do the models compare? Which would you choose? Use a level of significance of 0 05 Click the icon to view the Credit Approval Decisions data What is the best model using the p-value criteria? Select the best answer below and fill in the answer boxes to complete your choice. (Type integers or decimals rounded to three decimal places as needed) OA. Credit Score-+ Years+)Balance OB. Credit Score= OC. Credit Score= OD. Credit Score= OE. Credit Score= OF Credit Score = + Balance+ (Utilization Years + + Utilization Years (Balance + Utilization + + (Years Utilization OG. Credit Score= What is the best model using the t-statistic criteria? Select the best answer below and fill in the answer boxes to complete your choice. (Type integers or decimals rounded to three decimal places as needed.) OA. Credit Score= Years OB. Credit Score= Balance OC. Credit Score= + Balance+ () Utilization O D. Credit Score= Years + Balance O E. Credit Score=+Years + () Balance + + + Balance Utilization Years+ Years + Utilization Compare the models Which model should be chosen? The first model's R² value is, which is The principle of parsimony (Type integers or decimals rounded to three decimal places as needed) OE. Credit Score= OF Credit Score= OG. Credit Score= Balance+ Utilization Utilization the second moder's R² value of Therefore, based on the R² values, the better model is the because the R values close (that is, they differ by 005), so the better model is the model m P se <f Cre he eg Cre Cre Cre Credit Approval Decisions data table Credit Score Years of Credit History 20 9 11 15 12 730 578 682 630 532 800 738 625. 596 665 705 505 570 625 22 7 5 17 24 19 16 6 3 Revolving Balance ($) 11,420 7,300 20,100 12,900 5,800 9,100 35,300 22,900 16,600 9,300 22,100 12,600 7,800 37,500 Revolving Utilization (%) 0.23 0.71 0.52 0.68 0.77 0.14 0.22 0.64 0.51 0.34 0.16 0.84 0.72 0.88 0 1 com or p pose Fick Cre es the integ Cre Cre Cre 625 596 665 705 505 570 625 779 807 645 528 816 768 560 622 647 5 17 24 19 16 6 3 13 10 7 14 20 2 4 9 13 22,900 16,600 9,300 22,100 12,600 7,800 37,500 6,200 10,600 17,400 27,100 13,500 11,300 2,600 8,500 16,100 0.64 0.51 0.34 0.16 0.84 0.72 0.88 0.08 0.04 0.57 0.78 0.05 0.72 0.98 0.35 0.23 647 693 654 700 706 640 512 682 490 587 704 708 590 625 700 779 13 – - - ಟ - - 7 ಸ ಸ 1 ೦ – ನ - ಪ 12 15 12 17 22 18 16 13 16,100 3,400 7,600 20,400 11,800 29,200 2,100 7,700 1,100 8,600 12,900 10,100 31,100 16,300 9,800 6,200 0.23 0.11 0.05 0.24 0.13 0.85 1.00 0.07 0.84 0.63 0.28 0.21 0.79 0.56 0.12 0.06 590 625 700 779 807 645 541 806 765 572 605 707 641 514 600 738 18 8 16 13 10 7 14 20 2 4 10 11 8 3 18 15 31,100 16,300 9,800 6,200 10,600 17,400 27,100 13,500 11,300 2,300 12,150 11,800 29,200 2,100 29,100 13,100 0.79 0.56 0.12 0.06 0.05 0.58 0.77 0.04 0.72 0.97 0.84 0.18 0.83 0.99 0.77 0.25 The accompanying Credit Approval Decisions data provide information on credit history for a sample of banking customers. Use regression analysis to identify the best model for predicting the credit score as a function of the other numerical variables, using both the p-value and 1 statistic criteria. How do the models compare? Which would you choose? Use a level of significance of 0 05 Click the icon to view the Credit Approval Decisions data What is the best model using the p-value criteria? Select the best answer below and fill in the answer boxes to complete your choice. (Type integers or decimals rounded to three decimal places as needed) OA. Credit Score-+ Years+)Balance OB. Credit Score= OC. Credit Score= OD. Credit Score= OE. Credit Score= OF Credit Score = + Balance+ (Utilization Years + + Utilization Years (Balance + Utilization + + (Years Utilization OG. Credit Score= What is the best model using the t-statistic criteria? Select the best answer below and fill in the answer boxes to complete your choice. (Type integers or decimals rounded to three decimal places as needed.) OA. Credit Score= Years OB. Credit Score= Balance OC. Credit Score= + Balance+ () Utilization O D. Credit Score= Years + Balance O E. Credit Score=+Years + () Balance + + + Balance Utilization Years+ Years + Utilization Compare the models Which model should be chosen? The first model's R² value is, which is The principle of parsimony (Type integers or decimals rounded to three decimal places as needed) OE. Credit Score= OF Credit Score= OG. Credit Score= Balance+ Utilization Utilization the second moder's R² value of Therefore, based on the R² values, the better model is the because the R values close (that is, they differ by 005), so the better model is the model m P se <f Cre he eg Cre Cre Cre Credit Approval Decisions data table Credit Score Years of Credit History 20 9 11 15 12 730 578 682 630 532 800 738 625. 596 665 705 505 570 625 22 7 5 17 24 19 16 6 3 Revolving Balance ($) 11,420 7,300 20,100 12,900 5,800 9,100 35,300 22,900 16,600 9,300 22,100 12,600 7,800 37,500 Revolving Utilization (%) 0.23 0.71 0.52 0.68 0.77 0.14 0.22 0.64 0.51 0.34 0.16 0.84 0.72 0.88 0 1 com or p pose Fick Cre es the integ Cre Cre Cre 625 596 665 705 505 570 625 779 807 645 528 816 768 560 622 647 5 17 24 19 16 6 3 13 10 7 14 20 2 4 9 13 22,900 16,600 9,300 22,100 12,600 7,800 37,500 6,200 10,600 17,400 27,100 13,500 11,300 2,600 8,500 16,100 0.64 0.51 0.34 0.16 0.84 0.72 0.88 0.08 0.04 0.57 0.78 0.05 0.72 0.98 0.35 0.23 647 693 654 700 706 640 512 682 490 587 704 708 590 625 700 779 13 – - - ಟ - - 7 ಸ ಸ 1 ೦ – ನ - ಪ 12 15 12 17 22 18 16 13 16,100 3,400 7,600 20,400 11,800 29,200 2,100 7,700 1,100 8,600 12,900 10,100 31,100 16,300 9,800 6,200 0.23 0.11 0.05 0.24 0.13 0.85 1.00 0.07 0.84 0.63 0.28 0.21 0.79 0.56 0.12 0.06 590 625 700 779 807 645 541 806 765 572 605 707 641 514 600 738 18 8 16 13 10 7 14 20 2 4 10 11 8 3 18 15 31,100 16,300 9,800 6,200 10,600 17,400 27,100 13,500 11,300 2,300 12,150 11,800 29,200 2,100 29,100 13,100 0.79 0.56 0.12 0.06 0.05 0.58 0.77 0.04 0.72 0.97 0.84 0.18 0.83 0.99 0.77 0.25

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