Question 1

The logistic regression coefficient can be transformed into an estimated predicted probability to make it more easily interpreted.

Accepted Answer

A) True 
 B)False  True

Question 2

In the logistic regression model, the constant and regression coefficients are estimated using ______.

Accepted Answer

A)   maximum likelihood estimation
B)   least-squares methods
C)   proportional odds
D)   semi-standardized coefficient A

Question 3

The likelihood of a model is ______.

Accepted Answer

A)   the likelihood of obtaining the observed results given the sign and magnitude of the regression coefficients
B)   the likelihood that the estimated model is correct
C)   the likelihood that the estimated model can be replicated
D)   the likelihood that the estimated model is spurious A

Question 4

Exponentiating the value in question 21 results in a value of 1.14. This is interpreted as a 114% increase in the odds of the dependent variable with a one unit increase in the independent variable.

Accepted Answer

A) True 
 B)False

Question 5

Applying an OLS regression equation to a binary dependent variable is called a ______.

Accepted Answer

A)   logistic regression model
B)   linear probability model
C)   ordinary least-squares model
D)   probit regression model.

Question 6

Which of the following is NOT a problem that may arise from estimating a binary dependent variable in an OLS regression equation rather than a logistic regression equation ______.

Accepted Answer

A)   increased Type II error rate
B)   predicted values of the dependent variable that extend beyond the bounds of 0 or 1
C)   the functional form of how the independent variable affects the dependent variable may not be linear
D)   the error terms (residuals)  are not normally distributed and thus violate the assumption of homoscedasticity

Question 7

When testing the improvement in the likelihood functions between a baseline model and a model containing two independent variable, obtaining a chi-square less than the critical value means one would ______.

Accepted Answer

A)   reject the null hypothesis that all independent variables in the model are equal to zero
B)   accept the alternative hypothesis that all independent variables in the model are not equal to zero
C)   fail to reject the null hypothesis that all independent variables in the model are equal to zero
D)   none of the above

Question 8

Estimating an OLS model on a binary dependent variable will never result in predicted values of the dependent variable that are less than 0 or greater than 1.

Accepted Answer

A) True 
 B)False

Question 9

By exponentiating the coefficient for an independent variable in a logistic regression (also called the "antilog"), one will obtain ______.

Accepted Answer

A)   odds multiplier
B)   a linear relationship
C)   log of the exponentiated odds
D)   log of the odds

Question 10

What are the three main issues of predicting a binary dependent variable in an OLS regression equation? Explain each.

Accepted Answer

The answer of What are the three main issues of...

Question 11

Logistic regression is used when the dependent variable has binary values.

Accepted Answer

A) True 
 B)False

Question 12

The OLS model is a nonlinear model while the logit model is linear.

Accepted Answer

A) True 
 B)False

Question 13

The Wald statistic is comparable to the chi-square distribution.

Accepted Answer

A) True 
 B)False

Question 14

A good logistic model, one wherein the probability of the observed results is high, is one with ______.

Accepted Answer

A)   a high value of -2LL
B)   a high value of the odds ratio
C)   a small value of -2LL
D)   a small value of chi-square

Question 15

In contrast to OLS regression, in logistic regression the change in the probability of y with a 1 unit change in the independent variable is ______.

Accepted Answer

A)   linear
B)   not constant
C)   constant
D)   quadratic

Question 16

A perfect fitting model would have a likelihood equal to 1 and -2LL equal to 0.

Accepted Answer

A) True 
 B)False

Question 17

While there are disadvantages of using a predicted probability model, one advantage is the ease of interpreting the effect of the independent variable on the dependent variable.

Accepted Answer

A) True 
 B)False

Question 18

The odds multiplier reflects the change in the odds of the dependent variable occurring when the dependent variable ______.

Accepted Answer

A)   increases by 1 unit
B)   decreases by 1 unit
C)   increases by 1%
D)   deceases by 1%

Question 19

You obtain a regression coefficient of .132. This is interpreted as the predicted log of the odds of the dependent variable increases by .132 for a unit increase in the independent variable.

Accepted Answer

A) True 
 B)False

Question 20

To obtain the percent change in the odds of the dependent variable, one must ______.

Accepted Answer

A)   multiply the odds multiplier by 100
B)   subtract the odds multiplier from 1, and then multiply the result by 100
C)   subtract 1 from the odds multiplier, and then multiply the result by 100
D)   multiply the log of the odds by 100

Exam 14: Regression Analysis With a Dichotomous Dependent Variable: Logit Models

The logistic regression coefficient can be transformed into an estimated predicted probability to make it more easily interpreted.

In the logistic regression model, the constant and regression coefficients are estimated using ______.

The likelihood of a model is ______.

Exponentiating the value in question 21 results in a value of 1.14. This is interpreted as a 114% increase in the odds of the dependent variable with a one unit increase in the independent variable.

Applying an OLS regression equation to a binary dependent variable is called a ______.

Which of the following is NOT a problem that may arise from estimating a binary dependent variable in an OLS regression equation rather than a logistic regression equation ______.

When testing the improvement in the likelihood functions between a baseline model and a model containing two independent variable, obtaining a chi-square less than the critical value means one would ______.

Estimating an OLS model on a binary dependent variable will never result in predicted values of the dependent variable that are less than 0 or greater than 1.

By exponentiating the coefficient for an independent variable in a logistic regression (also called the "antilog") , one will obtain ______.

What are the three main issues of predicting a binary dependent variable in an OLS regression equation? Explain each.

Logistic regression is used when the dependent variable has binary values.

The OLS model is a nonlinear model while the logit model is linear.

The Wald statistic is comparable to the chi-square distribution.

A good logistic model, one wherein the probability of the observed results is high, is one with ______.

In contrast to OLS regression, in logistic regression the change in the probability of y with a 1 unit change in the independent variable is ______.

A perfect fitting model would have a likelihood equal to 1 and -2LL equal to 0.

While there are disadvantages of using a predicted probability model, one advantage is the ease of interpreting the effect of the independent variable on the dependent variable.

The odds multiplier reflects the change in the odds of the dependent variable occurring when the dependent variable ______.

You obtain a regression coefficient of .132. This is interpreted as the predicted log of the odds of the dependent variable increases by .132 for a unit increase in the independent variable.

To obtain the percent change in the odds of the dependent variable, one must ______.