Question 1

What would happen if the age variable was standardised before running the analysis?

Accepted Answer

A) The overall fit of the model would change 
B) The Wald test of significance regarding age would change 
C) The odds ratio and log‐odds regarding age would change 
D) All of the above would happen 
A) The overall fit of the model would change 
B) The Wald test of significance regarding age would change 
C) The odds ratio and log‐odds regarding age would change 
D) All of the above would happen C

Question 2

What can we conclude about the relationship between handedness and accident and emergency visits?

Accepted Answer

A) Left‐handed people are significantly more likely to attend accident and emergency 
B) Right‐handed people are significantly more likely to attend accident and emergency 
C) There is no significant relationship between handedness and accident and emergency attendance 
D) None of the above 
A) Left‐handed people are significantly more likely to attend accident and emergency 
B) Right‐handed people are significantly more likely to attend accident and emergency 
C) There is no significant relationship between handedness and accident and emergency attendance 
D) None of the above C

Question 3

What percentage of accident and emergency attenders are identified by the model?

Accepted Answer

A) 87.3 
B) 66.5 
C) 23 
D) 31.1 
A) 87.3 
B) 66.5 
C) 23 
D) 31.1 D

Question 4

The next questions refer to the following output that comes from a fictional study predicting accident and emergency attendance over the last 5 years from a scale measuring propensity to take risks, age measured in years) and handedness where right‐handed is the reference category).
  
-Does the model including predictors improve fit over the baseline model?

Accepted Answer

A) Yes, because the chi‐square test on the Omnibus Tests of Model Coefficients is significant 
B) Yes, because the ‐2 log‐likelihood of the model is positive 
C) Yes because the overall proportion of correct classifications is bigger than .5 
D) No 
A) Yes, because the chi‐square test on the Omnibus Tests of Model Coefficients is significant 
B) Yes, because the ‐2 log‐likelihood of the model is positive 
C) Yes because the overall proportion of correct classifications is bigger than .5 
D) No

Question 5

Can continuous variables be included as predictors in logistic regression?

Accepted Answer

A) No 
B) Yes, but only if they are dichotomised into two categories 
C) Yes but only if they are cut up into multiple categories 
D) Yes 
A) No 
B) Yes, but only if they are dichotomised into two categories 
C) Yes but only if they are cut up into multiple categories 
D) Yes

Question 6

Logistic regression models a linear relationship between a continuous predictor variable and

Accepted Answer

A) the probability that a dichotomous outcome takes on a particular value 
B) the odds that a dichotomous outcome takes on a particular value 
C) the log‐odds that a dichotomous outcome takes on a particular value 
D) logistic regression cannot include continuous predictor variables 
A) the probability that a dichotomous outcome takes on a particular value 
B) the odds that a dichotomous outcome takes on a particular value 
C) the log‐odds that a dichotomous outcome takes on a particular value 
D) logistic regression cannot include continuous predictor variables

Question 7

What is the ‐2 Log likelihood for the baseline model?

Accepted Answer

A) 17.55 
B) 246.04 
C) 263.59 
D) 228.49 
A) 17.55 
B) 246.04 
C) 263.59 
D) 228.49

Question 8

Could Body‐mass index BMI) be used as the outcome variable in logistic regression?

Accepted Answer

A) Yes, if it was dichotomised on a particular cut‐point 
B) Yes, as a continuous variable 
C) No, because BMI is an ordinal scale 
D) No, because BMI may only be used as a predictor variable 
A) Yes, if it was dichotomised on a particular cut‐point 
B) Yes, as a continuous variable 
C) No, because BMI is an ordinal scale 
D) No, because BMI may only be used as a predictor variable

Question 9

In logistic regression, Wald statistics test

Accepted Answer

A) the overall fit of the model 
B) whether an individual predictor makes a significant independent contribution to the model 
C) the fit of the baseline model 
D) whether the dependent variable is suitable for logistic regression 
A) the overall fit of the model 
B) whether an individual predictor makes a significant independent contribution to the model 
C) the fit of the baseline model 
D) whether the dependent variable is suitable for logistic regression

Question 10

Why are there 3 degrees of freedom for the chi‐square test in the Omnibus Tests of Model Coefficients?

Accepted Answer

A) Because the current model has 3 more parameters than the baseline model 
B) Because the baseline model has 3 more parameters than the current model 
C) Because there are 3 significant Wald statistics in the Variables in the Equation box 
D) Because the Nagelkerke R‐square is less than .33 
A) Because the current model has 3 more parameters than the baseline model 
B) Because the baseline model has 3 more parameters than the current model 
C) Because there are 3 significant Wald statistics in the Variables in the Equation box 
D) Because the Nagelkerke R‐square is less than .33

What would happen if the age variable was standardised before running the analysis?

What can we conclude about the relationship between handedness and accident and emergency visits?

What percentage of accident and emergency attenders are identified by the model?

Can continuous variables be included as predictors in logistic regression?

Logistic regression models a linear relationship between a continuous predictor variable and

What is the ‐2 Log likelihood for the baseline model?

Could Body‐mass index BMI) be used as the outcome variable in logistic regression?

In logistic regression, Wald statistics test

Why are there 3 degrees of freedom for the chi‐square test in the Omnibus Tests of Model Coefficients?

An Introduction to the Research Process

Descriptive Statistics

The Basis of Statistical Testing

Epidemiology

Introduction to Data Screening and Cleaning

Differences Between Two Groups

Differences Between Three or More Conditions

Testing Associations Between Categorical Variables

Measuring Agreement: Correlational Techniques

Linear Regression

Standard Multiple Regression

Interventions and Analysis of Change

Survival Analysis: an Introduction

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Exam 12: Logistic Regression

What would happen if the age variable was standardised before running the analysis?

What can we conclude about the relationship between handedness and accident and emergency visits?

What percentage of accident and emergency attenders are identified by the model?

Can continuous variables be included as predictors in logistic regression?

Logistic regression models a linear relationship between a continuous predictor variable and

What is the ‐2 Log likelihood for the baseline model?

Could Body‐mass index BMI) be used as the outcome variable in logistic regression?

In logistic regression, Wald statistics test

Why are there 3 degrees of freedom for the chi‐square test in the Omnibus Tests of Model Coefficients?

An Introduction to the Research Process

Descriptive Statistics

The Basis of Statistical Testing

Epidemiology

Introduction to Data Screening and Cleaning

Differences Between Two Groups

Differences Between Three or More Conditions

Testing Associations Between Categorical Variables

Measuring Agreement: Correlational Techniques

Linear Regression

Standard Multiple Regression

Interventions and Analysis of Change

Survival Analysis: an Introduction

Filters