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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. (Using Excel)Refer to Exhibit 17.9.Suppose that only applicants with excellent and good ratings are qualified for a loan.Assume that the balance ratio,x<sub>1</sub>,of those who apply is normally distributed with μ<sub>1</sub> = 18% and σ<sub>2</sub> = 6%,while their debt ratio,x<sub>2</sub>,is normally distributed with μ<sub>2</sub> = 30% and σ<sub>2</sub> = 8%.Because of limited capabilities of Excel,assume also that x<sub>1</sub> and x<sub>2</sub> are independent.Using Random Number Generator in Data Analysis of Excel,simulate 1000 applications to estimate the percent of those that are qualified for a loan. 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. (Using Excel)Refer to Exhibit 17.9.Suppose that only applicants with excellent and good ratings are qualified for a loan.Assume that the balance ratio,x<sub>1</sub>,of those who apply is normally distributed with μ<sub>1</sub> = 18% and σ<sub>2</sub> = 6%,while their debt ratio,x<sub>2</sub>,is normally distributed with μ<sub>2</sub> = 30% and σ<sub>2</sub> = 8%.Because of limited capabilities of Excel,assume also that x<sub>1</sub> and x<sub>2</sub> are independent.Using Random Number Generator in Data Analysis of Excel,simulate 1000 applications to estimate the percent of those that are qualified for a loan. ,
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. (Using Excel)Refer to Exhibit 17.9.Suppose that only applicants with excellent and good ratings are qualified for a loan.Assume that the balance ratio,x<sub>1</sub>,of those who apply is normally distributed with μ<sub>1</sub> = 18% and σ<sub>2</sub> = 6%,while their debt ratio,x<sub>2</sub>,is normally distributed with μ<sub>2</sub> = 30% and σ<sub>2</sub> = 8%.Because of limited capabilities of Excel,assume also that x<sub>1</sub> and x<sub>2</sub> are independent.Using Random Number Generator in Data Analysis of Excel,simulate 1000 applications to estimate the percent of those that are qualified for a loan. Note: The p-values of the corresponding tests are shown in parentheses below the estimated coefficients.
(Using Excel)Refer to Exhibit 17.9.Suppose that only applicants with excellent and good ratings are qualified for a loan.Assume that the balance ratio,x1,of those who apply is normally distributed with μ1 = 18% and σ2 = 6%,while their debt ratio,x2,is normally distributed with μ2 = 30% and σ2 = 8%.Because of limited capabilities of Excel,assume also that x1 and x2 are independent.Using Random Number Generator in Data Analysis of Excel,simulate 1000 applications to estimate the percent of those that are qualified for a loan.

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Definitions:

U.S. President

The head of state and government of the United States of America, serving as the executive leader and commander-in-chief under the U.S. Constitution.

Labor Dispute

A disagreement between workers and employers related to employment conditions, including wages, hours, and workplace policies.

Final Offer Arbitration

An arbitration process where the arbitrator must choose one party's final offer in its entirety, without modification, encouraging fair and realistic proposals.

Disputed Contract Terms

Aspects of a labor contract that are in disagreement or contention between employers and employees or their representatives.

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