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 the​ p-value criteria. Use a level of significance of 0.05. What is the best

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Chapter1: Starting With Matlab
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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 the​ p-value criteria. Use a level of significance of

0.05.
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.)
 
A.
Credit
Score=nothing+Balance
 
B.
Credit
Score=nothing+Years+Balance
 
C.
Credit
Score=nothing+Years+Balance+Utilization
 
D.
Credit
Score=nothing+Balance+Utilization
 
E.
Credit
Score=nothing+Utilization
 
F.
Credit
Score=nothing+Years+Utilization
 
G.
Credit
Score=nothing+Years
 
Credit Score Years of Credit History Revolving Balance ($) Revolving Utilization (%)
728 21 11420 0.23
576 8 7300 0.71
680 13 20100 0.52
628 17 12900 0.68
530 10 5800 0.77
798 21 9100 0.14
736 6 35300 0.22
623 6 22900 0.64
594 18 16600 0.51
663 22 9300 0.34
703 18 22100 0.16
503 15 12600 0.84
568 7 7800 0.72
623 4 37500 0.88
777 12 6200 0.08
805 11 10600 0.04
643 8 17400 0.57
526 13 27100 0.78
814 22 13500 0.05
766 4 11300 0.72
558 4 2600 0.98
620 8 8500 0.35
645 14 16100 0.23
691 2 3400 0.11
652 13 7600 0.05
698 17 20400 0.24
704 8 11800 0.13
638 6 29200 0.85
510 3 2100 1
680 14 7700 0.07
488 6 1100 0.84
585 2 8600 0.63
702 18 12900 0.28
706 24 10100 0.21
588 17 31100 0.79
623 6 16300 0.56
698 18 9800 0.12
777 12 6200 0.06
805 11 10600 0.05
643 6 17400 0.58
539 18 27100 0.77
804 22 13500 0.04
763 3 11300 0.72
570 6 2300 0.97
603 11 12150 0.84
705 13 11800 0.18
639 7 29200 0.83
512 4 2100 0.99
598 19 29100 0.77
736 12 13100 0.25
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