Question 1

Logistic regression is unable to produce nonlinear models.

Accepted Answer

A) True 
 B)False  False

Question 2

The accuracy of classification should also be reported in the narrative.

Accepted Answer

A) True 
 B)False  True

Question 3

A good-fitting model in logistic regression will typically have fairly low values for -2 Log Likelihood, significant model chi-square, and variables with odds ratios greater than or equal to 1.

Accepted Answer

A) True 
 B)False  False

Question 4

The value that is being predicted in logistic regression is a probability, which ranges from 0 to 1.

Accepted Answer

A) True 
 B)False

Question 5

Which of the following is true in the interpretation of results?

Accepted Answer

A)  Cox & Snell R Square and Nagelkerke R Square represent two different estimates of the amount of variance in the DV accounted for by the overall model. 
B)  Chi-square statistics with levels of significance are computed for the model, block, and step. 
C)  Wald is a measure of significance for B and represents the significance of each variable in its ability to contribute to the model. 
D)  All of the above are true. 
A)  Cox & Snell R Square and Nagelkerke R Square represent two different estimates of the amount of variance in the DV accounted for by the overall model. 
B)  Chi-square statistics with levels of significance are computed for the model, block, and step. 
C)  Wald is a measure of significance for B and represents the significance of each variable in its ability to contribute to the model. 
D)  All of the above are true.

Question 6

Which of the following responses is not true of logistic regression?

Accepted Answer

A)  It is basically an extension of multiple regression in situations where the DV is not a continuous or quantitative variable. 
B)  Like both multiple regression and discriminant analysis, logistic regression requires the same assumptions about the distributions of the predictor variables (IVs) to be made by the researcher. 
C)  This procedure is also sometimes used as an alternative to discriminant analysis. 
D)  The basic concepts that are fundamental to multiple regression analysis-namely that several variables are regressed onto another variable using one of several selection processes-are the same for logistic regression analysis, although the meaning of the resultant regression equation is considerably different. 
A)  It is basically an extension of multiple regression in situations where the DV is not a continuous or quantitative variable. 
B)  Like both multiple regression and discriminant analysis, logistic regression requires the same assumptions about the distributions of the predictor variables (IVs) to be made by the researcher. 
C)  This procedure is also sometimes used as an alternative to discriminant analysis. 
D)  The basic concepts that are fundamental to multiple regression analysis-namely that several variables are regressed onto another variable using one of several selection processes-are the same for logistic regression analysis, although the meaning of the resultant regression equation is considerably different.

Question 7

The odds ratio represents the increase (or decrease if Exp(B) is less than 1) in odds of being classified in a category when the predictor variable increases by 1.

Accepted Answer

A) True 
 B)False

Question 8

Logistic regression is not sensitive to high correlations among predictor variables.

Accepted Answer

A) True 
 B)False

Question 9

In binary logistic regression, the DV may be dichotomous and the IVs may be continuous or categorical.

Accepted Answer

A) True 
 B)False

Question 10

Logistic regression is also not sensitive to outliers.

Accepted Answer

A) True 
 B)False

Question 11

Probabilities are simply the number of outcomes of a specific type expressed as a proportion of the total number of possible outcomes.

Accepted Answer

A) True 
 B)False

Question 12

Smaller values on the -2 Log Likelihood indicate that the model fits the data better.

Accepted Answer

A) True 
 B)False

Question 13

Logistic regression is also sometimes used as an alternative to discriminant analysis.

Accepted Answer

A) True 
 B)False

Question 14

Like both discriminant analysis and multiple regression, logistic regression requires that assumptions about the distributions of the predictor variables need to be made by the researcher.

Accepted Answer

A) True 
 B)False

Question 15

The results section in logistic regression should contain a table that includes B, Wald, df, level of significance, and odds ratio.

Accepted Answer

A) True 
 B)False

Question 16

Logistic regression is basically an extension of multiple regression in situations where the DV is not a continuous or quantitative variable.

Accepted Answer

A) True 
 B)False

Question 17

The results summary should always describe how variables have been transformed or deleted.

Accepted Answer

A) True 
 B)False

Question 18

Even though logistic regression does not require the adherence to any assumptions about the distribution of predictor variables, several problems may occur if too few cases relative to the number of predictor variables exist in the data.

Accepted Answer

A) True 
 B)False

Question 19

The classification table compares the predicted values for the IVs, based on the logistic regression model, with the actual observed values from the data.

Accepted Answer

A) True 
 B)False

Question 20

In a logistic regression application, odds are defined as the ratio of the probability that an event will occur divided by the probability that the event will not occur.

Accepted Answer

A) True 
 B)False

Logistic regression is unable to produce nonlinear models.

The accuracy of classification should also be reported in the narrative.

A good-fitting model in logistic regression will typically have fairly low values for -2 Log Likelihood, significant model chi-square, and variables with odds ratios greater than or equal to 1.

The value that is being predicted in logistic regression is a probability, which ranges from 0 to 1.

Which of the following is true in the interpretation of results?

Which of the following responses is not true of logistic regression?

The odds ratio represents the increase (or decrease if Exp(B) is less than 1) in odds of being classified in a category when the predictor variable increases by 1.

Logistic regression is not sensitive to high correlations among predictor variables.

In binary logistic regression, the DV may be dichotomous and the IVs may be continuous or categorical.

Logistic regression is also not sensitive to outliers.

Probabilities are simply the number of outcomes of a specific type expressed as a proportion of the total number of possible outcomes.

Smaller values on the -2 Log Likelihood indicate that the model fits the data better.

Logistic regression is also sometimes used as an alternative to discriminant analysis.

Like both discriminant analysis and multiple regression, logistic regression requires that assumptions about the distributions of the predictor variables need to be made by the researcher.

The results section in logistic regression should contain a table that includes B, Wald, df, level of significance, and odds ratio.

Logistic regression is basically an extension of multiple regression in situations where the DV is not a continuous or quantitative variable.

The results summary should always describe how variables have been transformed or deleted.

Even though logistic regression does not require the adherence to any assumptions about the distribution of predictor variables, several problems may occur if too few cases relative to the number of predictor variables exist in the data.

The classification table compares the predicted values for the IVs, based on the logistic regression model, with the actual observed values from the data.

In a logistic regression application, odds are defined as the ratio of the probability that an event will occur divided by the probability that the event will not occur.

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

Logistic regression is unable to produce nonlinear models.

The accuracy of classification should also be reported in the narrative.

A good-fitting model in logistic regression will typically have fairly low values for -2 Log Likelihood, significant model chi-square, and variables with odds ratios greater than or equal to 1.

The value that is being predicted in logistic regression is a probability, which ranges from 0 to 1.

Which of the following is true in the interpretation of results?

Which of the following responses is not true of logistic regression?

The odds ratio represents the increase (or decrease if Exp(B) is less than 1) in odds of being classified in a category when the predictor variable increases by 1.

Logistic regression is not sensitive to high correlations among predictor variables.

In binary logistic regression, the DV may be dichotomous and the IVs may be continuous or categorical.

Logistic regression is also not sensitive to outliers.

Probabilities are simply the number of outcomes of a specific type expressed as a proportion of the total number of possible outcomes.

Smaller values on the -2 Log Likelihood indicate that the model fits the data better.

Logistic regression is also sometimes used as an alternative to discriminant analysis.

Like both discriminant analysis and multiple regression, logistic regression requires that assumptions about the distributions of the predictor variables need to be made by the researcher.

The results section in logistic regression should contain a table that includes B, Wald, df, level of significance, and odds ratio.

Logistic regression is basically an extension of multiple regression in situations where the DV is not a continuous or quantitative variable.

The results summary should always describe how variables have been transformed or deleted.

Even though logistic regression does not require the adherence to any assumptions about the distribution of predictor variables, several problems may occur if too few cases relative to the number of predictor variables exist in the data.

The classification table compares the predicted values for the IVs, based on the logistic regression model, with the actual observed values from the data.

In a logistic regression application, odds are defined as the ratio of the probability that an event will occur divided by the probability that the event will not occur.

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