Question 1

Which of the following statements is incorrect?

Accepted Answer

A)  Probabilities are simply the number of outcomes of a specific type expressed as a proportion of the total number of possible outcomes. 
B)  Odds are defined as the ratio of the probability that an event will occur divided by the probability that the event will not occur. 
C)  Odds will always have values that range from 0 to 1. 
D)  The odds ratio is defined as a ratio of the odds of being classified in one category of the DV for two different values of the IV. 
A)  Probabilities are simply the number of outcomes of a specific type expressed as a proportion of the total number of possible outcomes. 
B)  Odds are defined as the ratio of the probability that an event will occur divided by the probability that the event will not occur. 
C)  Odds will always have values that range from 0 to 1. 
D)  The odds ratio is defined as a ratio of the odds of being classified in one category of the DV for two different values of the IV.

Question 2

Logistic regression may produce extremely large parameter estimates and standard errors, especially in situations where combinations of discrete variables result in too many cells with no cases.

Accepted Answer

A) True 
 B)False

Question 3

Probabilities will always have values that range from 0 to 1, but odds may be greater than 1.

Accepted Answer

A) True 
 B)False

Question 4

The significance of each predictor is tested with a t test as in multiple regression.

Accepted Answer

A) True 
 B)False

Question 5

The ultimate model obtained by a logistic regression analysis is a linear function.

Accepted Answer

A) True 
 B)False

Question 6

Cox & Snell R Square and Nagelkerke R Square are essentially estimates of R² indicating the proportion of variability in the DV that may be accounted for by all predictor variables included in the equation.

Accepted Answer

A) True 
 B)False

Question 7

Mathematically speaking, logistic regression is based on:

Accepted Answer

A)  Probabilities. 
B)  Odds. 
C)  The logarithm of the odds. 
D)  All three above responses are correct. 
A)  Probabilities. 
B)  Odds. 
C)  The logarithm of the odds. 
D)  All three above responses are correct.

Question 8

Wald is a measure of association for B and represents the significance of a variable in its ability to contribute to the model.

Accepted Answer

A) True 
 B)False

Question 9

The main output components to interpret in the results obtained from a logistic regression analysis include which of the following?

Accepted Answer

A)  The resulting model, using goodness-of-fit tests. 
B)  A classification table for the DV. 
C)  The table of coefficients for variables included in the model. 
D)  Responses (a), (b), and (c) are correct. 
A)  The resulting model, using goodness-of-fit tests. 
B)  A classification table for the DV. 
C)  The table of coefficients for variables included in the model. 
D)  Responses (a), (b), and (c) are correct.

Question 10

The chi-square goodness-of-fit test compares the actual values for cases on the DV with the predicted values on the DV.

Accepted Answer

A) True 
 B)False

Which of the following statements is incorrect?

Logistic regression may produce extremely large parameter estimates and standard errors, especially in situations where combinations of discrete variables result in too many cells with no cases.

Probabilities will always have values that range from 0 to 1, but odds may be greater than 1.

The significance of each predictor is tested with a t test as in multiple regression.

The ultimate model obtained by a logistic regression analysis is a linear function.

Cox & Snell R Square and Nagelkerke R Square are essentially estimates of R² indicating the proportion of variability in the DV that may be accounted for by all predictor variables included in the equation.

Mathematically speaking, logistic regression is based on:

Wald is a measure of association for B and represents the significance of a variable in its ability to contribute to the model.

The main output components to interpret in the results obtained from a logistic regression analysis include which of the following?

The chi-square goodness-of-fit test compares the actual values for cases on the DV with the predicted values on the DV.

Introduction to Multivariate Statistics

A Guide to Multivariate Techniques

Pre-Analysis Data Screening

Factorial Analysis of Variance

Analysis of Covariance

Multivariate Analysis of Variance and Covariance

Multiple Regression

Path Analysis

Factor Analysis

Discriminant Analysis

Filters

Exam 11: Logistic Regression

Which of the following statements is incorrect?

Logistic regression may produce extremely large parameter estimates and standard errors, especially in situations where combinations of discrete variables result in too many cells with no cases.

Probabilities will always have values that range from 0 to 1, but odds may be greater than 1.

The significance of each predictor is tested with a t test as in multiple regression.

The ultimate model obtained by a logistic regression analysis is a linear function.

Cox & Snell R Square and Nagelkerke R Square are essentially estimates of R² indicating the proportion of variability in the DV that may be accounted for by all predictor variables included in the equation.

Mathematically speaking, logistic regression is based on:

Wald is a measure of association for B and represents the significance of a variable in its ability to contribute to the model.

The main output components to interpret in the results obtained from a logistic regression analysis include which of the following?

The chi-square goodness-of-fit test compares the actual values for cases on the DV with the predicted values on the DV.

Introduction to Multivariate Statistics

A Guide to Multivariate Techniques

Pre-Analysis Data Screening

Factorial Analysis of Variance

Analysis of Covariance

Multivariate Analysis of Variance and Covariance

Multiple Regression

Path Analysis

Factor Analysis

Discriminant Analysis

Filters