Question 1

In logistic regression, the dependent variable is called a logit, which is

Accepted Answer

A)  The natural log of the odds 
B)  The rolling mean 
C)  A log rolling 
D)  A mean log 
A)  The natural log of the odds 
B)  The rolling mean 
C)  A log rolling 
D)  A mean log B

Question 2

What does the log-likelihood measure?

Accepted Answer

A)  The number of degrees of freedom in relation to the parameter set for the model 
B)  The way in which observed and predicted values fit the model of regression* 
C)  The value of the correlation coefficient R and its corresponding R2 value 
D)  None of the above 
A)  The number of degrees of freedom in relation to the parameter set for the model 
B)  The way in which observed and predicted values fit the model of regression* 
C)  The value of the correlation coefficient R and its corresponding R2 value 
D)  None of the above B

Question 3

In determining the odds that a specific type of antibiotic preparation will cure otitis media in children, the odds ratio value was calculated to be greater than 1. With increases in the predictor, what will this mean for the outcome of the result?

Accepted Answer

A)  A greater chance of a cure being achieved than if not treated. 
B)  A lesser chance of a cure being achieved. 
C)  A lesser chance of treatment being effective. 
D)  None of the above. 
A)  A greater chance of a cure being achieved than if not treated. 
B)  A lesser chance of a cure being achieved. 
C)  A lesser chance of treatment being effective. 
D)  None of the above. A

Question 4

Logistic regression

Accepted Answer

A)  Estimates values in the outcome variable. 
B)  Estimates a probability that the outcome variable assumes a certain value. 
C)  Estimates probabilities in the outcome variable. 
D)  Estimates the probability that the predictor variables assume certain values. 
A)  Estimates values in the outcome variable. 
B)  Estimates a probability that the outcome variable assumes a certain value. 
C)  Estimates probabilities in the outcome variable. 
D)  Estimates the probability that the predictor variables assume certain values.

Question 5

Large log-likelihood values do not show

Accepted Answer

A)  The extent of the relationship between independent and dependent variables. 
B)  That there are a large number of unexplained observations in the data. 
C)  That there is a large measure of deviance. 
D)  That the model is a good fit. 
A)  The extent of the relationship between independent and dependent variables. 
B)  That there are a large number of unexplained observations in the data. 
C)  That there is a large measure of deviance. 
D)  That the model is a good fit.

Question 6

Multinomial logistic regression might be used to predict which of the following examples

Accepted Answer

A)  Men prefer to use non-scented ointment in the treatment of fungal infection, whilst women prefer to use scented powder varieties in preference to scented ointment. 
B)  Men use non-scented ointment to treat fungal infection. 
C)  Women use non-scented powder to treat fungal infection. 
D)  Women use scented powder to treat fungal infection whilst men use non-scented powder. 
A)  Men prefer to use non-scented ointment in the treatment of fungal infection, whilst women prefer to use scented powder varieties in preference to scented ointment. 
B)  Men use non-scented ointment to treat fungal infection. 
C)  Women use non-scented powder to treat fungal infection. 
D)  Women use scented powder to treat fungal infection whilst men use non-scented powder.

Question 7

What is suggested by very high standard errors in parameter estimates in logistic regression?

Accepted Answer

A)  Shrinkage 
B)  Multicollinearity 
C)  An outlier 
D)  A dummy variable 
A)  Shrinkage 
B)  Multicollinearity 
C)  An outlier 
D)  A dummy variable

Question 8

How would you request a logistic regression analysis in SPSS?

Accepted Answer

A)  Analyze → Nonparametric → Binomial 
B)  Analyze → Regression → Ordinal 
C)  Analyze → Regression → Binary Logistic 
D)  Analyze → Loglinear → General 
A)  Analyze → Nonparametric → Binomial 
B)  Analyze → Regression → Ordinal 
C)  Analyze → Regression → Binary Logistic 
D)  Analyze → Loglinear → General

Question 9

Which of the following is not used in logistic regression?

Accepted Answer

A)  Forced entry 
B)  Forward method 
C)  Predictor method 
D)  Backward method 
A)  Forced entry 
B)  Forward method 
C)  Predictor method 
D)  Backward method

Question 10

Collinearity diagnostics can be used for what purpose?

Accepted Answer

A)  To determine which predictor is causing the lowest value. 
B)  To determine which predictor is causing the highest value. 
C)  To determine the accuracy of the regression model. 
D)  To determine which predictor is causing the data to be skewed. 
A)  To determine which predictor is causing the lowest value. 
B)  To determine which predictor is causing the highest value. 
C)  To determine the accuracy of the regression model. 
D)  To determine which predictor is causing the data to be skewed.

Question 11

Which of the following is not an assumption of logistic regression?

Accepted Answer

A)  Linearity 
B)  Independence of errors 
C)  Multicollinearity 
D)  None of the above 
A)  Linearity 
B)  Independence of errors 
C)  Multicollinearity 
D)  None of the above

Question 12

The odds ratio is

Accepted Answer

A)  The natural log to the bth power, where b is the unstandardized parameter estimate. 
B)  The natural log to the bth power, where b is the standardized parameter estimate. 
C)  The base 10 log to the bth power where b is the unstandardized parameter estimate. 
D)  The base 10 log to the bth power where b is the standardized parameter estimate. 
A)  The natural log to the bth power, where b is the unstandardized parameter estimate. 
B)  The natural log to the bth power, where b is the standardized parameter estimate. 
C)  The base 10 log to the bth power where b is the unstandardized parameter estimate. 
D)  The base 10 log to the bth power where b is the standardized parameter estimate.

Question 13

What does the following graph illustrate? (PD is peritoneal dialysis.)

Accepted Answer

The answer of What does the following graph illustrate? (PD...

Question 14

What does the Wald statistic represent?

Accepted Answer

A)  The predicted value of the correlation coefficient 
B)  The proportionate change in the odds ratio 
C)  The odds ratio of the regression 
D)  Whether a variable is a significant predictor of the outcome 
A)  The predicted value of the correlation coefficient 
B)  The proportionate change in the odds ratio 
C)  The odds ratio of the regression 
D)  Whether a variable is a significant predictor of the outcome

Question 15

With conventional coding in logistic regression of 0 and 1, if there are 100 cases and 40 are coded as 1, what is the mean of the variable?

Accepted Answer

A)  40 
B)  .4 
C)  2.5 
D)  25 
A)  40 
B)  .4 
C)  2.5 
D)  25

Question 16

In the above example, the mean is also equal to

Accepted Answer

A)  The mode 
B)  The probability of randomly drawing a 1 
C)  The median 
D)  The probability of drawing a 0 
A)  The mode 
B)  The probability of randomly drawing a 1 
C)  The median 
D)  The probability of drawing a 0

Question 17

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 was 2.56. How should this value be interpreted?

Accepted Answer

A)  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. 
B)  2.56 times more people who attempted suicide scored highly on depression. 
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 lower than for the other person. 
D)  Suicide rates are 2.56 times as high in depressed people. 
A)  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. 
B)  2.56 times more people who attempted suicide scored highly on depression. 
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 lower than for the other person. 
D)  Suicide rates are 2.56 times as high in depressed people.

Question 18

Which of the following statements is not true about the Wald statistic?

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)  If the Wald statistic is equivalent to the t-statistic in multiple 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)  If the Wald statistic is equivalent to the t-statistic in multiple regression. 
D)  The Wald statistic has a t-distribution.

Question 19

Which of the following is not a research question which could be investigated with logistic regression?

Accepted Answer

A)  Prediction of group membership 
B)  Strength of association between criterion and predictors 
C)  Differences between groups 
D)  Interaction between predictors 
A)  Prediction of group membership 
B)  Strength of association between criterion and predictors 
C)  Differences between groups 
D)  Interaction between predictors

Question 20

Which of the following reasons best describes why overdispersion is a problem in logistic regression?

Accepted Answer

A)  Incorrect correlations may be assumed. 
B)  The standard error will be limited and may cause the test statistic to be too large and more likely to become significant. 
C)  The outliers will not significantly affect the results. 
D)  None of the above. 
A)  Incorrect correlations may be assumed. 
B)  The standard error will be limited and may cause the test statistic to be too large and more likely to become significant. 
C)  The outliers will not significantly affect the results. 
D)  None of the above.

Exam 20: Categorical Outcomes: Logistic Regression

In logistic regression, the dependent variable is called a logit, which is

What does the log-likelihood measure?

In determining the odds that a specific type of antibiotic preparation will cure otitis media in children, the odds ratio value was calculated to be greater than 1. With increases in the predictor, what will this mean for the outcome of the result?

Logistic regression

Large log-likelihood values do not show

Multinomial logistic regression might be used to predict which of the following examples

What is suggested by very high standard errors in parameter estimates in logistic regression?

How would you request a logistic regression analysis in SPSS?

Which of the following is not used in logistic regression?

Collinearity diagnostics can be used for what purpose?

Which of the following is not an assumption of logistic regression?

The odds ratio is

What does the following graph illustrate? (PD is peritoneal dialysis.)

What does the Wald statistic represent?

With conventional coding in logistic regression of 0 and 1, if there are 100 cases and 40 are coded as 1, what is the mean of the variable?

In the above example, the mean is also equal to

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 was 2.56. How should this value be interpreted?

Which of the following statements is not true about the Wald statistic?

Which of the following is not a research question which could be investigated with logistic regression?

Which of the following reasons best describes why overdispersion is a problem 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

The Linear Model Regression

Comparing Two Means

Moderation, Mediation and Multicategory Predictors

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

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