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Exhibit 17

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Exhibit 17.9.A bank manager is interested in assigning a rating to the holders of credit cards issued by her bank.The rating is based on the probability of defaulting on credit cards and is as follows. Exhibit 17.9.A bank manager is interested in assigning a rating to the holders of credit cards issued by her bank.The rating is based on the probability of defaulting on credit cards and is as follows. To estimate this probability,she decided to use the logistic model: , where, y = a binary response variable with value 1 corresponding to a default,and 0 to a no default, x<sub>1</sub> = the ratio of the credit card balance to the credit card limit (in percent), x<sub>2</sub> = the ratio of the total debt to the annual income (in percent). Using Minitab on the sample data,she arrived at the following estimates: Note: The p-values of the corresponding tests are shown in parentheses below the estimated coefficients. Refer to Exhibit 17.9.What are the conclusions of testing the individual significance of the balance ratio and the debt ratio at the 1% level? To estimate this probability,she decided to use the logistic model: Exhibit 17.9.A bank manager is interested in assigning a rating to the holders of credit cards issued by her bank.The rating is based on the probability of defaulting on credit cards and is as follows. To estimate this probability,she decided to use the logistic model: , where, y = a binary response variable with value 1 corresponding to a default,and 0 to a no default, x<sub>1</sub> = the ratio of the credit card balance to the credit card limit (in percent), x<sub>2</sub> = the ratio of the total debt to the annual income (in percent). Using Minitab on the sample data,she arrived at the following estimates: Note: The p-values of the corresponding tests are shown in parentheses below the estimated coefficients. Refer to Exhibit 17.9.What are the conclusions of testing the individual significance of the balance ratio and the debt ratio at the 1% level? ,
where,
y = a binary response variable with value 1 corresponding to a default,and 0 to a no default,
x1 = the ratio of the credit card balance to the credit card limit (in percent),
x2 = the ratio of the total debt to the annual income (in percent).
Using Minitab on the sample data,she arrived at the following estimates: Exhibit 17.9.A bank manager is interested in assigning a rating to the holders of credit cards issued by her bank.The rating is based on the probability of defaulting on credit cards and is as follows. To estimate this probability,she decided to use the logistic model: , where, y = a binary response variable with value 1 corresponding to a default,and 0 to a no default, x<sub>1</sub> = the ratio of the credit card balance to the credit card limit (in percent), x<sub>2</sub> = the ratio of the total debt to the annual income (in percent). Using Minitab on the sample data,she arrived at the following estimates: Note: The p-values of the corresponding tests are shown in parentheses below the estimated coefficients. Refer to Exhibit 17.9.What are the conclusions of testing the individual significance of the balance ratio and the debt ratio at the 1% level? Note: The p-values of the corresponding tests are shown in parentheses below the estimated coefficients.
Refer to Exhibit 17.9.What are the conclusions of testing the individual significance of the balance ratio and the debt ratio at the 1% level?

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