Question 1

Complete separation is:

Accepted Answer

A) When the observed variance is bigger than expected from the logistic regression model. 
B) When the outcome variable can be perfectly predicted by one variable or a combination of variables. 
C) When the outcome variable is completely separate from all other variables. 
D) When all predictor variables are perfectly uncorrelated. 
A) When the observed variance is bigger than expected from the logistic regression model. 
B) When the outcome variable can be perfectly predicted by one variable or a combination of variables. 
C) When the outcome variable is completely separate from all other variables. 
D) When all predictor variables are perfectly uncorrelated. B

Question 2

A researcher collected some data to look at tiredness (measured on a 10-point scale ranging from 0 (I'm wide awake) to 10 (I can't keep my eyes open) as a predictor of the following mood states (people were classified into each mood based on a diary): more depressed than normal (score = 2), more anxious than normal (score = 1) or normal (score = 0). Which of the following tests would be the most appropriate method for analysing these data?

Accepted Answer

A) Loglinear analysis 
B) Multiple regression 
C) Multinomial logistic regression 
D) Binary logistic regression 
A) Loglinear analysis 
B) Multiple regression 
C) Multinomial logistic regression 
D) Binary logistic regression C

Question 3

In logistic regression, what is the R-statistic?

Accepted Answer

A) It is the partial correlation between the outcome variable and each of the predictor variables. 
B) It is the semi-partial correlation between the outcome variable and each of the predictor variables. 
C) It is the polychoric correlation between the outcome variable and each of the predictor variables. 
D) It is the correlation between the outcome variable and each of the predictor variables. 
A) It is the partial correlation between the outcome variable and each of the predictor variables. 
B) It is the semi-partial correlation between the outcome variable and each of the predictor variables. 
C) It is the polychoric correlation between the outcome variable and each of the predictor variables. 
D) It is the correlation between the outcome variable and each of the predictor variables. A

Question 4

Multinomial logistic regression can be used on:

Accepted Answer

A) Ordinal predictor variables only. 
B) Both categorical and continuous predictor variables. 
C) Categorical predictor variables only. 
D) Continuous predictor variables only. 
A) Ordinal predictor variables only. 
B) Both categorical and continuous predictor variables. 
C) Categorical predictor variables only. 
D) Continuous predictor variables only.

Question 5

With regard to logistic regression, which of the following sentences about the R-statistic is false?

Accepted Answer

A) The R-statistic is by no means an accurate measure and should be treated with some caution. 
B) A negative value of the R-statistic implies that as the predictor variable decreases, the likelihood ratio of the outcome occurring decreases. 
C) The R-statistic is the partial correlation between the outcome variable and each of the predictor variables. 
D) The R-statistic can vary between -1 and 1. 
A) The R-statistic is by no means an accurate measure and should be treated with some caution. 
B) A negative value of the R-statistic implies that as the predictor variable decreases, the likelihood ratio of the outcome occurring decreases. 
C) The R-statistic is the partial correlation between the outcome variable and each of the predictor variables. 
D) The R-statistic can vary between -1 and 1.

Question 6

The interpretation of the odds ratio, Exp(B), can be generalized to the population if:

Accepted Answer

A) The confidence interval of Exp(
B)  does not cross 0. 
B) The confidence interval of Exp(
B)  does cross 1. 
C) The confidence interval of Exp(
B)  does cross 0. 
D) The confidence interval of Exp(
B)  does not cross 1. 
A) The confidence interval of Exp(
B)  does not cross 0. 
B) The confidence interval of Exp(
B)  does cross 1. 
C) The confidence interval of Exp(
B)  does cross 0. 
D) The confidence interval of Exp(
B)  does not cross 1.

Question 7

Which of the following sentences regarding logistic regression is false?

Accepted Answer

A) The linearity assumption is the same as for linear regression. 
B) In logistic regression we assume the same things as ordinary regression. 
C) If the outcome variable can be predicted perfectly from one predictor variable (or a combination of predictor variables) then we have complete separation. 
D) Violating the assumption of independence can cause overdispersion. 
A) The linearity assumption is the same as for linear regression. 
B) In logistic regression we assume the same things as ordinary regression. 
C) If the outcome variable can be predicted perfectly from one predictor variable (or a combination of predictor variables) then we have complete separation. 
D) Violating the assumption of independence can cause overdispersion.

Question 8

In logistic regression, a classification table:

Accepted Answer

A) Indicates how well a model has predicted group membership. 
B) Allows you to check that your outcome variable has been coded correctly. 
C) Classifies levels of your model in terms of statistical significance. 
D) None of these. 
A) Indicates how well a model has predicted group membership. 
B) Allows you to check that your outcome variable has been coded correctly. 
C) Classifies levels of your model in terms of statistical significance. 
D) None of these.

Question 9

The assumption of linearity in logistic regression:

Accepted Answer

A) Assumes that there is a non-linear relationship between the logit of the predictor variables and the outcome variable. 
B) Is the same as the assumption of linearity in linear regression. 
C) Is tested by looking for a significant interaction between all pairs of predictor variables. 
D) Assumes that there is a linear relationship between any continuous predictors and the logit of the outcome variable. 
A) Assumes that there is a non-linear relationship between the logit of the predictor variables and the outcome variable. 
B) Is the same as the assumption of linearity in linear regression. 
C) Is tested by looking for a significant interaction between all pairs of predictor variables. 
D) Assumes that there is a linear relationship between any continuous predictors and the logit of the outcome variable.

Question 10

The statistical implication of using a parsimony heuristic is:

Accepted Answer

A) Predictors are not included unless they have explanatory benefit. 
B) Models should be as complex as possible. 
C) Model parameters should be estimated using maximum likelihood methods. 
D) Iterative estimation should be used. 
A) Predictors are not included unless they have explanatory benefit. 
B) Models should be as complex as possible. 
C) Model parameters should be estimated using maximum likelihood methods. 
D) Iterative estimation should be used.

Question 11

Unlike multiple regression, logistic regression:

Accepted Answer

A) Does not have b weights. 
B) Is not open to sources of bias. 
C) Log-transforms the predictor variables. 
D) Predicts a categorical outcome variable. 
A) Does not have b weights. 
B) Is not open to sources of bias. 
C) Log-transforms the predictor variables. 
D) Predicts a categorical outcome variable.

Question 12

In multinomial logistic regression:

Accepted Answer

A) There must be more than one nominal predictor variable. 
B) All predictors must be log-transformed. 
C) The dependent variable is categorical with three or more levels. 
D) There is more than one predictor variable. 
A) There must be more than one nominal predictor variable. 
B) All predictors must be log-transformed. 
C) The dependent variable is categorical with three or more levels. 
D) There is more than one predictor variable.

Question 13

Imagine we had a sample of 15 couples and we wanted to predict which of the couples are likely to still be together in 1 year's time. In an attempt to do this, we filmed each couple having a 5-minute discussion about a recent 'difficult issue' that had arisen in their relationship . How could we analyse these data?

Accepted Answer

A) Independent t-test 
B) Logistic regression 
C) Multiple regression 
D) One-way repeated-measures ANOVA 
A) Independent t-test 
B) Logistic regression 
C) Multiple regression 
D) One-way repeated-measures ANOVA

Question 14

Which of the following statements about the Wald statistic is false?

Accepted Answer

A) The Wald statistic assesses the individual contribution of a predictor to a logistic regression model. 
B) The Wald statistic tends to be biased when the regression coefficient is large. 
C) The Wald statistic is analogous to the t-statistic in linear regression. 
D) The Wald statistic has a t-distribution. 
A) The Wald statistic assesses the individual contribution of a predictor to a logistic regression model. 
B) The Wald statistic tends to be biased when the regression coefficient is large. 
C) The Wald statistic is analogous to the t-statistic in linear regression. 
D) The Wald statistic has a t-distribution.

Question 15

The ____ the value of the log-likelihood statistic, the ______ unexplained observations there are.

Accepted Answer

A) smaller, more 
B) None of these are true. 
C) larger, more 
D) larger, fewer 
A) smaller, more 
B) None of these are true. 
C) larger, more 
D) larger, fewer

Question 16

In the following table, how many mistakes are made when an animal is predicted to be a dog? $$\begin{array} { | l | l | l | } 
\hline & \text { Actually DoB } & \text { Actually Cat } \
\hline \text { Predicted DeB } & 89 & 8 \
\hline \text { Predicted Cat } & 4 & 55 \
\hline
\end{array}$$

Accepted Answer

A) 84 
B) 8 
C) 55 
D) 4 
A) 84 
B) 8 
C) 55 
D) 4

Question 17

Overdispersion:

Accepted Answer

A) All of these are correct. 
B) Tends to limit standard errors. 
C) Doesn't affect the model parameters (b-values) . 
D) Biases our conclusions about the significance and population value of the model parameters. 
A) All of these are correct. 
B) Tends to limit standard errors. 
C) Doesn't affect the model parameters (b-values) . 
D) Biases our conclusions about the significance and population value of the model parameters.

Question 18

A researcher was interested in predicting whether a person would attempt to commit suicide (score = 1) or not (score = 0) from their depression scores. They found that the value of Exp(B) (the odds ratio) was 2.56. How should this value be interpreted?

Accepted Answer

A) 2.56 times more people who attempted suicide scored highly on depression. 
B) If two people have depression scores that differ by 1 unit, then the odds of the person with the higher score attempting suicide are is 2.56 lower than for the other person. 
C) If two people have depression scores that differ by 1 unit, then the odds of the person with the higher score attempting suicide are 2.56 higher than for the other person. 
D) 25.6% of the variance in suicide attempts is explained by depression. 
A) 2.56 times more people who attempted suicide scored highly on depression. 
B) If two people have depression scores that differ by 1 unit, then the odds of the person with the higher score attempting suicide are is 2.56 lower than for the other person. 
C) If two people have depression scores that differ by 1 unit, then the odds of the person with the higher score attempting suicide are 2.56 higher than for the other person. 
D) 25.6% of the variance in suicide attempts is explained by depression.

Question 19

Imagine we were trying to predict whether someone will pass (score = 1), or fail (score = 0) their driving test based on: (1) the number of hours of lessons they'd had; (2) whether or not they owned a car; (3) how many previous tests they had taken; and (4) their score on a test of spatial awareness. What analysis could we use on these data?

Accepted Answer

A) Multinomial logistic regression 
B) Bimodal logistic regression 
C) Binary logistic regression 
D) Multiple regression 
A) Multinomial logistic regression 
B) Bimodal logistic regression 
C) Binary logistic regression 
D) Multiple regression

Question 20

It is useful to compare a logistic regression model against some kind of baseline state. Which of the following is the baseline state that is usually used in logistic regression?

Accepted Answer

A) The model when all predictors and interactions are entered included 
B) The model when only the constant is included 
C) The mean of all scores 
D) The log of all scores 
A) The model when all predictors and interactions are entered included 
B) The model when only the constant is included 
C) The mean of all scores 
D) The log of all scores

Complete separation is:

In logistic regression, what is the R-statistic?

Multinomial logistic regression can be used on:

With regard to logistic regression, which of the following sentences about the R-statistic is false?

The interpretation of the odds ratio, Exp(B), can be generalized to the population if:

Which of the following sentences regarding logistic regression is false?

In logistic regression, a classification table:

The assumption of linearity in logistic regression:

The statistical implication of using a parsimony heuristic is:

Unlike multiple regression, logistic regression:

In multinomial logistic regression:

Which of the following statements about the Wald statistic is false?

The the value of the log-likelihood statistic, the __ unexplained observations there are.

In the following table, how many mistakes are made when an animal is predicted to be a dog? Actually DoB Actually Cat Predicted DeB 89 8 Predicted Cat 4 55

Overdispersion:

A researcher was interested in predicting whether a person would attempt to commit suicide (score = 1) or not (score = 0) from their depression scores. They found that the value of Exp(B) (the odds ratio) was 2.56. How should this value be interpreted?

It is useful to compare a logistic regression model against some kind of baseline state. Which of the following is the baseline state that is usually used in logistic regression?

Why Is My Evil Lecturer Forcing Me to Learn Statistics

The Spine of Statistics

The Phoenix of Statistics

The IBM Spss Statistics Environment

Exploring Data With Graphs

The Beast of Bias

Non-Parametric Models

Correlation

Regression

Comparing Two Means

Moderation, Mediation and More Regression

GlM 1: Comparing Several Independent Means

GlM 2: Comparing Means Adjusted for Other Predictors Analysis of Covariance

GlM 3: Factorial Designs

GlM 4: Repeated-Measures Designs

GlM 5: Mixed Designs

Multivariate Analysis of Variance Manova

Exploratory Factor Analysis

Categorical Outcomes: Chi-Square and Loglinear Analysis

Multilevel Linear Models

Filters

Exam 20: Categorical Outcomes: Logistic Regression

Complete separation is:

In logistic regression, what is the R-statistic?

Multinomial logistic regression can be used on:

With regard to logistic regression, which of the following sentences about the R-statistic is false?

The interpretation of the odds ratio, Exp(B), can be generalized to the population if:

Which of the following sentences regarding logistic regression is false?

In logistic regression, a classification table:

The assumption of linearity in logistic regression:

The statistical implication of using a parsimony heuristic is:

Unlike multiple regression, logistic regression:

In multinomial logistic regression:

Which of the following statements about the Wald statistic is false?

The ____ the value of the log-likelihood statistic, the ______ unexplained observations there are.

In the following table, how many mistakes are made when an animal is predicted to be a dog? Actually DoB Actually Cat Predicted DeB 89 8 Predicted Cat 4 55

Overdispersion:

A researcher was interested in predicting whether a person would attempt to commit suicide (score = 1) or not (score = 0) from their depression scores. They found that the value of Exp(B) (the odds ratio) was 2.56. How should this value be interpreted?

It is useful to compare a logistic regression model against some kind of baseline state. Which of the following is the baseline state that is usually used in logistic regression?

Why Is My Evil Lecturer Forcing Me to Learn Statistics

The Spine of Statistics

The Phoenix of Statistics

The IBM Spss Statistics Environment

Exploring Data With Graphs

The Beast of Bias

Non-Parametric Models

Correlation

Regression

Comparing Two Means

Moderation, Mediation and More Regression

GlM 1: Comparing Several Independent Means

GlM 2: Comparing Means Adjusted for Other Predictors Analysis of Covariance

GlM 3: Factorial Designs

GlM 4: Repeated-Measures Designs

GlM 5: Mixed Designs

Multivariate Analysis of Variance Manova

Exploratory Factor Analysis

Categorical Outcomes: Chi-Square and Loglinear Analysis

Multilevel Linear Models

Filters

The the value of the log-likelihood statistic, the __ unexplained observations there are.