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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 t-statistic criteria. How do Which would you choose? Use a level of significance of 0.05. E 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.) O A. Credit Score = OYears + (O Balance B. Credit Score 760.800 + (-219.013) Utilization OC. Credit Score = +OBalance O D. Credit Score +OYears OE. Credit Score= +OYears + ( Balance + (Dutilization OF. Credit Score = +(OBalance + (O Utlization OG. Credit Score = OYears + ( Uilization 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.) O A. Credit Score = IOYears + ( Balance O B. Credit Score =+O Balance O. Credit Score =I•OBalance + (O Utilization OD. Credit Score =+DUtilization OE. Credit Score =+OYears + OUtilization OF. Credit Score =+OYears + O Balance + (DUtilization OG. Credit Score =+OYears

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 t-statistic criteria. How do Which would you choose? Use a level of significance of 0.05. E 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.) O A. Credit Score = OYears + (O Balance B. Credit Score 760.800 + (-219.013) Utilization OC. Credit Score = +OBalance O D. Credit Score +OYears OE. Credit Score= +OYears + ( Balance + (Dutilization OF. Credit Score = +(OBalance + (O Utlization OG. Credit Score = OYears + ( Uilization 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.) O A. Credit Score = IOYears + ( Balance O B. Credit Score =+O Balance O. Credit Score =I•OBalance + (O Utilization OD. Credit Score =+DUtilization OE. Credit Score =+OYears + OUtilization OF. Credit Score =+OYears + O Balance + (DUtilization OG. Credit Score =+OYears

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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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 t-statistic criteria. How do the models compare? Which would you choose? Use a level of significance of 0.05. E 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.) O A. Credit Score = Years + Balance B. Credit Score = 760.800 + (- 219.013 ) Utilization O C. Credit Score = Balance O D. Credit Score = Years O E. Credit Score = ) Years + (O Balance + (DUtilization O F. Credit Score = Balance + (O Utilization O G. Credit Score = ) Years + (DUtilization 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.) O A. Credit Score = + O Years + ( Balance O B. Credit Score = Balance OC. Credit Score = Utilization Balance + O D. Credit Score = Utilization O E. Credit Score = Years + Utilization OF. Credit Score = Years + Balance + DUtilization O G. Credit Score = Years
Transcribed Image Text: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 t-statistic criteria. How do the models compare? Which would you choose? Use a level of significance of 0.05. E 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.) O A. Credit Score = Years + Balance B. Credit Score = 760.800 + (- 219.013 ) Utilization O C. Credit Score = Balance O D. Credit Score = Years O E. Credit Score = ) Years + (O Balance + (DUtilization O F. Credit Score = Balance + (O Utilization O G. Credit Score = ) Years + (DUtilization 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.) O A. Credit Score = + O Years + ( Balance O B. Credit Score = Balance OC. Credit Score = Utilization Balance + O D. Credit Score = Utilization O E. Credit Score = Years + Utilization OF. Credit Score = Years + Balance + DUtilization O G. Credit Score = Years
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 t-statistic criteria. How do the models compare? Which would you choose? Use a level of significance of 0.05. E Click the icon to view the Credit Approval Decisions data. What is the best model using the p-value criteria? Select the best answer b (Type integers or decimals rounded to three decimal places as needed.) Credit approval decisions data table O A. Credit Score = +O Years + O Balance Revolving Balance (S) Revolving Utilization (%) Credit Score Years of Credit VB. Credit Score = 760.800 + (- 219.013) Utilization History O C. Credit Score = + ( O Balance 728 21 11,420 0.23 576 8 7,300 0.71 O D. Credit Score = O Years 0.52 + 680 13 20,100 628 17 12,900 0.68 O E. Credit Score = )Years+ (O Balance + (DUtilization + 530 10 5,800 0.77 OF. Credit Score = O Balance + ( Utilization 798 21 9,100 0.14 + 736 6 35,300 0.22 O G. Credit Score = O Years + O Utilization + 623 6 22,900 0.64 594 18 16,600 0.51 What is the best model using the t-statistic criteria? Select the best answer 663 22 9,300 0.34 (Type integers or decimals rounded to three decimal places as needed.) 703 18 22,100 0.16 503 15 12,600 0.84 O A. Credit Score = + (DYears + D Balance 568 7 7,800 0.72 O B. Credit Score = + (DBalance 623 4 37,500 0.88 777 12 6,200 0.08 O C. Credit Score = Balance + DUtilization + 805 11 10,600 0.04 O D. Credit Score = + DUtilization 643 8 17,400 0.57 526 13 27,100 0.78 O E. Credit Score = + (DYears + DUtilization 814 22 13,500 0.05 766 4. 11,300 0.72 O F. Credit Score = + ( DYears + ( Balance + (D Utilization 558 4 2,600 0.98 O G. Credit Score = Years 620 8 8,500 0.35 + 645 14 16,100 0.23 691 3,400 0.11 652 13 7,600 0.05 698 17 20,400 0.24 704 8 11,800 0.13 638 6 29,200 0.85 510 3 2,100 1.00 680 14 7,700 0.07 488 6 1,100 0.84 585 2 8,600 0.63 702 18 12,900 0.28 706 24 10,100 0.21 588 17 31,100 0.79 623 6 16,300 0.56 698 18 9,800 0.12 777 12 6,200 0.06 805 11 10,600 0.05 643 6 17,400 0.58 539 18 27,100 0.77 804 22 13,500 0.04 763 3 11,300 0.72 570 6 2,300 0.97 603 11 12,150 0.84 705 13 11,800 0.18 639 7 29,200 0.83 512 4 2,100 0.99 598 19 29,100 0.77 736 12 13,100 0.25
Transcribed Image Text: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 t-statistic criteria. How do the models compare? Which would you choose? Use a level of significance of 0.05. E Click the icon to view the Credit Approval Decisions data. What is the best model using the p-value criteria? Select the best answer b (Type integers or decimals rounded to three decimal places as needed.) Credit approval decisions data table O A. Credit Score = +O Years + O Balance Revolving Balance (S) Revolving Utilization (%) Credit Score Years of Credit VB. Credit Score = 760.800 + (- 219.013) Utilization History O C. Credit Score = + ( O Balance 728 21 11,420 0.23 576 8 7,300 0.71 O D. Credit Score = O Years 0.52 + 680 13 20,100 628 17 12,900 0.68 O E. Credit Score = )Years+ (O Balance + (DUtilization + 530 10 5,800 0.77 OF. Credit Score = O Balance + ( Utilization 798 21 9,100 0.14 + 736 6 35,300 0.22 O G. Credit Score = O Years + O Utilization + 623 6 22,900 0.64 594 18 16,600 0.51 What is the best model using the t-statistic criteria? Select the best answer 663 22 9,300 0.34 (Type integers or decimals rounded to three decimal places as needed.) 703 18 22,100 0.16 503 15 12,600 0.84 O A. Credit Score = + (DYears + D Balance 568 7 7,800 0.72 O B. Credit Score = + (DBalance 623 4 37,500 0.88 777 12 6,200 0.08 O C. Credit Score = Balance + DUtilization + 805 11 10,600 0.04 O D. Credit Score = + DUtilization 643 8 17,400 0.57 526 13 27,100 0.78 O E. Credit Score = + (DYears + DUtilization 814 22 13,500 0.05 766 4. 11,300 0.72 O F. Credit Score = + ( DYears + ( Balance + (D Utilization 558 4 2,600 0.98 O G. Credit Score = Years 620 8 8,500 0.35 + 645 14 16,100 0.23 691 3,400 0.11 652 13 7,600 0.05 698 17 20,400 0.24 704 8 11,800 0.13 638 6 29,200 0.85 510 3 2,100 1.00 680 14 7,700 0.07 488 6 1,100 0.84 585 2 8,600 0.63 702 18 12,900 0.28 706 24 10,100 0.21 588 17 31,100 0.79 623 6 16,300 0.56 698 18 9,800 0.12 777 12 6,200 0.06 805 11 10,600 0.05 643 6 17,400 0.58 539 18 27,100 0.77 804 22 13,500 0.04 763 3 11,300 0.72 570 6 2,300 0.97 603 11 12,150 0.84 705 13 11,800 0.18 639 7 29,200 0.83 512 4 2,100 0.99 598 19 29,100 0.77 736 12 13,100 0.25
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