Exam 17: Regression Models With Dummy Variables

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.

All variables employed in regression must be quantitative.

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, 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: 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?

Exhibit 17.8.A realtor wants to predict and compare the prices of homes in three neighboring locations.She considers the following linear models: Model A: Price = β₀ + β₁Size + β₂Age + ε, Model B: Price = β₀ + β₁Size + β₂Loc1 + β₃Loc2 + ε, Model C: Price = β₀ + β₁Size + β₂Age + β₃Loc1 + β₄Loc2 + ε, where, Price = the price of a home (in $thousands), Size = the square footage (in square feet), Loc1 = a dummy variable taking on 1 for Location 1,and 0 otherwise, Loc2 = a dummy variable taking on 1 for Location 2,and 0 otherwise. After collecting data on 52 sales and applying regression,her findings were summarized in the following table. Note: The values of relevant test statistics are shown in parentheses below the estimated coefficients. Refer to Exhibit 17.8.What is the p-value for testing the significance of Age in Model C?

Exhibit 17.7.To examine the differences between salaries of male and female middle managers of a large bank,90 individuals were randomly selected and the following variables considered: Salary = the monthly salary (excluding fringe benefits and bonuses), Educ = the number of years of education, Exper = the number of months of experience, Gender = the gender of an individual;1 for males,and 0 for females. The regression results for the models, Model A: Salary = β₀ + β₁Educ + β₂Exper + β₃Gender + β₄Exper × Gender + ε, Model B: Salary = β₀ + β₁Educ + β₂Exper + ε,are summarized below. Note.The values of relevant test statistics are shown in parentheses below the estimated coefficients. Refer to Exhibit 17.7.At 1% significance level,what is the conclusion of testing the joint significance of Exper and Exper × Gender in Model A?

Exhibit 17.8.A realtor wants to predict and compare the prices of homes in three neighboring locations.She considers the following linear models: Model A: Price = β₀ + β₁Size + β₂Age + ε, Model B: Price = β₀ + β₁Size + β₂Loc1 + β₃Loc2 + ε, Model C: Price = β₀ + β₁Size + β₂Age + β₃Loc1 + β₄Loc2 + ε, where, Price = the price of a home (in $thousands), Size = the square footage (in square feet), Loc1 = a dummy variable taking on 1 for Location 1,and 0 otherwise, Loc2 = a dummy variable taking on 1 for Location 2,and 0 otherwise. After collecting data on 52 sales and applying regression,her findings were summarized in the following table. Note: The values of relevant test statistics are shown in parentheses below the estimated coefficients. Refer to Exhibit 17.8.Using Model B,what is the value of the test statistic for testing the joint significance of the variable Time and the interaction variable Time × Prime?

In the model y = β₀ + β₁x + β₂d + β₃xd + ε,for a given x and d = 1,the predicted value of y is given by:

Exhibit 17.8.A realtor wants to predict and compare the prices of homes in three neighboring locations.She considers the following linear models: Model A: Price = β₀ + β₁Size + β₂Age + ε, Model B: Price = β₀ + β₁Size + β₂Loc1 + β₃Loc2 + ε, Model C: Price = β₀ + β₁Size + β₂Age + β₃Loc1 + β₄Loc2 + ε, where, Price = the price of a home (in $thousands), Size = the square footage (in square feet), Loc1 = a dummy variable taking on 1 for Location 1,and 0 otherwise, Loc2 = a dummy variable taking on 1 for Location 2,and 0 otherwise. After collecting data on 52 sales and applying regression,her findings were summarized in the following table. Note: The values of relevant test statistics are shown in parentheses below the estimated coefficients. Refer to Exhibit 17.8.Which of the three models would you choose to make the predictions of the home prices?

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, 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: Note: The p-values of the corresponding tests are shown in parentheses below the estimated coefficients. Refer to Exhibit 17.9.Compute the predicted probability of defaulting for a person whose balance ratio is 5% and debt ratio is 30%.

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 17.4.A researcher wants to examine how the remaining balance on $100,000 loans taken 10-20 years ago depends on whether the loan was a prime or sub-prime loan.He collected a sample of 25 prime loans and 25 sub-prime loans and records the data in the following variables: Balance = the remaining amount of loan to be paid off (in dollars), Time = the time elapsed from taking the loan, Prime = a dummy variable assuming 1 for prime loans,and 0 for sub-prime loans. The regression results obtained for the models: Model A: Balance = β₀ + β₁Prime + ε Model B: Balance = β₀ + β₁Time + β₂Prime + β₃Time × Prime + ε Model C: Balance = β₀ + β₁Prime + β₂Time × Prime + ε, Are summarized below. Note.The values of relevant test statistics are shown in parentheses below the estimated coefficients. Refer to Exhibit 17.4.Using Model B,what is the alternative hypothesis for testing the significance of Time?

Exhibit 17.4.A researcher wants to examine how the remaining balance on $100,000 loans taken 10-20 years ago depends on whether the loan was a prime or sub-prime loan.He collected a sample of 25 prime loans and 25 sub-prime loans and records the data in the following variables: Balance = the remaining amount of loan to be paid off (in dollars), Time = the time elapsed from taking the loan, Prime = a dummy variable assuming 1 for prime loans,and 0 for sub-prime loans. The regression results obtained for the models: Model A: Balance = β₀ + β₁Prime + ε Model B: Balance = β₀ + β₁Time + β₂Prime + β₃Time × Prime + ε Model C: Balance = β₀ + β₁Prime + β₂Time × Prime + ε, Are summarized below. Note.The values of relevant test statistics are shown in parentheses below the estimated coefficients. Refer to Exhibit 17.4.Using Model B,what is the null hypothesis for testing the joint significance of the variable Time and the interaction variable Time × Prime?

Exhibit 17.5.An over-the-counter drug manufacturer wants to examine the effectiveness of a new drug in curing an illness most commonly in older patients.Thirteen patients are given the new drug and 13 patients are given the old drug.To avoid bias in the experiment,they are not told which drug is given to them.To check how the effectiveness depends on the age of patients,the following data has been collected. Assuming the variables: Effectiveness = the response variable measured on a scale from 0 to 100, Age = the age of a patient (in years), Drug = a binary variable with 1 for the new drug,and 0 for the old drug,the regression model, Effectiveness = β₀ + β₁Age + β₂Drug + β₃Age × Drug,is considered,and the following Excel results are available: Refer to Exhibit 17.5.What is the predicted effectiveness of the old drug for a 62 years old patient?

Consider the regression model y = β₀ + β₁x + β₂d + β₃xd + ε.If the dummy variable d changes from 0 to 1,the estimated changes in the intercept and the slope are b₀ + b₂ and b₂,respectively.

Exhibit 17.1.A researcher has developed the following regression equation to predict the prices of luxurious Oceanside condominium units, , where Price = the price of a unit (in $thousands), Size = the square footage (in square feet), View = a dummy variable taking on 1 for an ocean view unit,and 0 for a bay view unit. Refer to Exhibit 17.1.What is the predicted price of a bay view unit measuring 1500 square feet?

The number of dummy variables representing a qualitative variable should be:

Exhibit 17.8.A realtor wants to predict and compare the prices of homes in three neighboring locations.She considers the following linear models: Model A: Price = β₀ + β₁Size + β₂Age + ε, Model B: Price = β₀ + β₁Size + β₂Loc1 + β₃Loc2 + ε, Model C: Price = β₀ + β₁Size + β₂Age + β₃Loc1 + β₄Loc2 + ε, where, Price = the price of a home (in $thousands), Size = the square footage (in square feet), Loc1 = a dummy variable taking on 1 for Location 1,and 0 otherwise, Loc2 = a dummy variable taking on 1 for Location 2,and 0 otherwise. After collecting data on 52 sales and applying regression,her findings were summarized in the following table.