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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.Assuming the debt ratio 30%,compute the increase in the probability of defaulting when the balance ratio goes up from 5% to 15%. 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.Assuming the debt ratio 30%,compute the increase in the probability of defaulting when the balance ratio goes up from 5% to 15%. ,
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.Assuming the debt ratio 30%,compute the increase in the probability of defaulting when the balance ratio goes up from 5% to 15%. Note: The p-values of the corresponding tests are shown in parentheses below the estimated coefficients.
Refer to Exhibit 17.9.Assuming the debt ratio 30%,compute the increase in the probability of defaulting when the balance ratio goes up from 5% to 15%.


Definitions:

Declarative Statement

A sentence that makes a statement or expresses an opinion, rather than giving a command or asking a question.

Simple Statement

A clear and straightforward declaration or expression, often consisting of only one clause.

Natural Language

Human languages, including both spoken and written forms, capable of expressing an infinite variety of ideas and emotions.

Logic of Declarative Statements

The formal principles governing the truth or falsehood of statements that declare facts.

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