Question 1

Which of the following is another name for 'multinomial'?

Accepted Answer

A)  Polychotomous 
B)  Bichotomous 
C)  Serialchotomous 
D)  Linearchotomous 
A)  Polychotomous 
B)  Bichotomous 
C)  Serialchotomous 
D)  Linearchotomous A

Question 2

By what alternative name is the z-statistic known?

Accepted Answer

A)  Wald statistic 
B)  Murphy's statistic 
C)  Katch statistic 
D)  Wilmore statistic 
A)  Wald statistic 
B)  Murphy's statistic 
C)  Katch statistic 
D)  Wilmore statistic A

Question 3

In linear and multiple regression, Y is predicted from one or more independent (X) variables. In logistic regression, it is the probability of the X variables predicting Y that is used. What is the likelihood that the X variable(s) will predict Y if a probability value of .1 is produced?

Accepted Answer

A)  Very unlikely 
B)  Very likely 
C)  Likely 
D)  A prefect prediction 
A)  Very unlikely 
B)  Very likely 
C)  Likely 
D)  A prefect prediction A

Question 4

Which of the following is the most appropriate example of logistic regression?

Accepted Answer

A)  The use of stature, aerobic capacity, muscular strength and previous sports played to predict whether an individual should enter an elite rowing or weightlifting training programme. 
B)  The use of aerobic capacity to predict marathon race time. 
C)  The use of aerobic capacity to predict whether an individual should develop muscular strength and endurance. 
D)  The use of stature, aerobic capacity, muscular strength and previous sports played to predict aerobic capacity. 
A)  The use of stature, aerobic capacity, muscular strength and previous sports played to predict whether an individual should enter an elite rowing or weightlifting training programme. 
B)  The use of aerobic capacity to predict marathon race time. 
C)  The use of aerobic capacity to predict whether an individual should develop muscular strength and endurance. 
D)  The use of stature, aerobic capacity, muscular strength and previous sports played to predict aerobic capacity.

Question 5

Which of the following variables best exemplify multinomial logistic regression?

Accepted Answer

A)  Gender 
B)  Sport played 
C)  Aerobic capacity 
D)  Muscular endurance 
A)  Gender 
B)  Sport played 
C)  Aerobic capacity 
D)  Muscular endurance

Question 6

What type of distribution does deviance follow?

Accepted Answer

A)  Chi-square distribution 
B)  t-distribution 
C)  F-distribution 
D)  z-distribution 
A)  Chi-square distribution 
B)  t-distribution 
C)  F-distribution 
D)  z-distribution

Question 7

Why were the answers provided in Q20 selected?

Accepted Answer

A)  The variable(s) have a value of 0 in them. 
B)  The variable(s) have a value of 5 or less in them. 
C)  The variables are continuous as opposed to categorical. 
D)  The variables are categorical as opposed to continuous. 
A)  The variable(s) have a value of 0 in them. 
B)  The variable(s) have a value of 5 or less in them. 
C)  The variables are continuous as opposed to categorical. 
D)  The variables are categorical as opposed to continuous.

Question 8

A value of 1.4 was obtained from the odds ratio calculation. How would this impact upon findings?

Accepted Answer

A)  As the predictor variables increase, so will the odds of the outcome increase. 
B)  As the predictor variables decrease, so will the odds of the outcome increase. 
C)  The relationship is not significant. 
D)  Results A and B are both possible. 
A)  As the predictor variables increase, so will the odds of the outcome increase. 
B)  As the predictor variables decrease, so will the odds of the outcome increase. 
C)  The relationship is not significant. 
D)  Results A and B are both possible.

Question 9

Suppose you were investigating the influence of player decisions (e.g. where to distribute a pass) during a game of basketball on the outcome of the match. The partial correlation value between the predictor and outcome variable is known as the R-statistic in logistic regression and ranges from -1 to +1. If a value of -.83 was obtained, what would you conclude about the influence player decisions have on the outcome of the game?

Accepted Answer

A)  As the predictor value increases, the likelihood of the outcome variable decreases. 
B)  As the predictor value increases, the likelihood of the outcome variable increases. 
C)  The difference between the predictor and outcome variables is due to chance. 
D)  All of the above are correct depending on the conditions. 
A)  As the predictor value increases, the likelihood of the outcome variable decreases. 
B)  As the predictor value increases, the likelihood of the outcome variable increases. 
C)  The difference between the predictor and outcome variables is due to chance. 
D)  All of the above are correct depending on the conditions.

Question 10

Which of the following is a categorical variable?

Accepted Answer

A)  Player motivation (Likert scale between 1 and 10) 
B)  Outcome of a match (win, draw or lose) 
C)  Shots on target during a football match 
D)  Complete passes during a football match 
A)  Player motivation (Likert scale between 1 and 10) 
B)  Outcome of a match (win, draw or lose) 
C)  Shots on target during a football match 
D)  Complete passes during a football match

Question 11

Which of the following is the most appropriate explanation of logistic regression?

Accepted Answer

A)  The use of predictor variables that are categorical or continuous, but the outcome variable is a categorical dichotomy variable. 
B)  The use of predictor variables that have to be continuous, but the outcome variable is a categorical dichotomy variable. 
C)  The use of predictor variables that have to be categorical, but the outcome variable is a categorical dichotomy variable. 
D)  The use of predictor variables that are categorical or continuous, but the outcome variable is a continuous variable. 
A)  The use of predictor variables that are categorical or continuous, but the outcome variable is a categorical dichotomy variable. 
B)  The use of predictor variables that have to be continuous, but the outcome variable is a categorical dichotomy variable. 
C)  The use of predictor variables that have to be categorical, but the outcome variable is a categorical dichotomy variable. 
D)  The use of predictor variables that are categorical or continuous, but the outcome variable is a continuous variable.

Question 12

Logistic regression uses the log-likelihood statistic to evaluate the fit of a model, based on the predicted and observed values, and is closely related to the deviance, but how is deviance expressed?

Accepted Answer

A)  Deviance = -2 × log-likelihood 
B)  Deviance = -2 / log-likelihood 
C)  Deviance = log-likelihood / (-2) 
D)  Deviance = log-likelihood / ((-2) × (-2)) 
A)  Deviance = -2 × log-likelihood 
B)  Deviance = -2 / log-likelihood 
C)  Deviance = log-likelihood / (-2) 
D)  Deviance = log-likelihood / ((-2) × (-2))

Question 13

Logistic regression is most appropriate on which of the following models?

Accepted Answer

A)  Non-linear model 
B)  Linear model 
C)  Serial model 
D)  Multiple regression model 
A)  Non-linear model 
B)  Linear model 
C)  Serial model 
D)  Multiple regression model

Question 14

If you inspect the data and find a standardized residual greater than 3, what would this suggest?

Accepted Answer

A)  An outlying observation / data point. 
B)  The standardized score is perfectly acceptable. 
C)  Equality of variance is significant. 
D)  Further analysis is required to be certain. 
A)  An outlying observation / data point. 
B)  The standardized score is perfectly acceptable. 
C)  Equality of variance is significant. 
D)  Further analysis is required to be certain.

Question 15

As with the t-statistic in linear regression, the z-statistic is used in logistic regression, but what does the value tell the researcher?

Accepted Answer

A)  Whether the b coefficient for variable X (i.e. gradient) is significantly different from 0 or not 
B)  Whether the b coefficient for variable X (i.e. gradient) is significantly different from 1 or not 
C)  Whether the b coefficient for variable Y (i.e. gradient) is significantly different from 0 or not 
D)  Whether the b coefficient for variable Y (i.e. gradient) is significantly different from 1 or not 
A)  Whether the b coefficient for variable X (i.e. gradient) is significantly different from 0 or not 
B)  Whether the b coefficient for variable X (i.e. gradient) is significantly different from 1 or not 
C)  Whether the b coefficient for variable Y (i.e. gradient) is significantly different from 0 or not 
D)  Whether the b coefficient for variable Y (i.e. gradient) is significantly different from 1 or not

Question 16

A group of researchers were interested in predicting the outcome of football matches. They found that time in possession of the ball, shots on target from within the area and corners won perfectly predicted the result. This is known as complete separation. Is this a positive or a negative finding and why?

Accepted Answer

A)  Positive - increased accuracy 
B)  Positive - reduction in Type I error 
C)  Negative - creates large standard errors 
D)  Negative - increased Type II error 
A)  Positive - increased accuracy 
B)  Positive - reduction in Type I error 
C)  Negative - creates large standard errors 
D)  Negative - increased Type II error

Question 17

Based on the data presented above, three of the variables (undisclosed) were deemed sufficient to predict the outcome of a match. A chi-square value of 6.74 was obtained. How would you interpret this value?

Accepted Answer

A)  Significant at the .01 level 
B)  Significant at the .05 level 
C)  Significant at the .10 level 
D)  Not statistically significant 
A)  Significant at the .01 level 
B)  Significant at the .05 level 
C)  Significant at the .10 level 
D)  Not statistically significant

Question 18

Which of the following variables best exemplify binary logistic regression?

Accepted Answer

A)  Gender 
B)  Sport played 
C)  Aerobic capacity 
D)  Muscular endurance 
A)  Gender 
B)  Sport played 
C)  Aerobic capacity 
D)  Muscular endurance

Question 19

Cook's distance is a measure used to estimate the influence of a specific data point. What would a value of 0.8 suggest?

Accepted Answer

A)  The variable is probably not influencing the model. 
B)  The variable is probably influencing the model. 
C)  A discriminant analysis should take place first before a conclusion can be drawn. 
D)  None of the above are appropriate. 
A)  The variable is probably not influencing the model. 
B)  The variable is probably influencing the model. 
C)  A discriminant analysis should take place first before a conclusion can be drawn. 
D)  None of the above are appropriate.

Question 20

What is meant by the term 'parsimony'?

Accepted Answer

A)  Simpler explanations of a phenomenon are preferred to complex explanations. 
B)  As more variables are identified, the model becomes more informed. 
C)  Complex explanations of a phenomenon are more accurate. 
D)  Simpler explanations have insufficient power. 
A)  Simpler explanations of a phenomenon are preferred to complex explanations. 
B)  As more variables are identified, the model becomes more informed. 
C)  Complex explanations of a phenomenon are more accurate. 
D)  Simpler explanations have insufficient power.

Which of the following is another name for 'multinomial'?

By what alternative name is the z-statistic known?

In linear and multiple regression, Y is predicted from one or more independent (X) variables. In logistic regression, it is the probability of the X variables predicting Y that is used. What is the likelihood that the X variable(s) will predict Y if a probability value of .1 is produced?

Which of the following is the most appropriate example of logistic regression?

Which of the following variables best exemplify multinomial logistic regression?

What type of distribution does deviance follow?

Why were the answers provided in Q20 selected?

A value of 1.4 was obtained from the odds ratio calculation. How would this impact upon findings?

Which of the following is a categorical variable?

Which of the following is the most appropriate explanation of logistic regression?

Logistic regression uses the log-likelihood statistic to evaluate the fit of a model, based on the predicted and observed values, and is closely related to the deviance, but how is deviance expressed?

Logistic regression is most appropriate on which of the following models?

If you inspect the data and find a standardized residual greater than 3, what would this suggest?

As with the t-statistic in linear regression, the z-statistic is used in logistic regression, but what does the value tell the researcher?

Based on the data presented above, three of the variables (undisclosed) were deemed sufficient to predict the outcome of a match. A chi-square value of 6.74 was obtained. How would you interpret this value?

Which of the following variables best exemplify binary logistic regression?

Cook's distance is a measure used to estimate the influence of a specific data point. What would a value of 0.8 suggest?

What is meant by the term 'parsimony'?

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

Which of the following is another name for 'multinomial'?

By what alternative name is the z-statistic known?

In linear and multiple regression, Y is predicted from one or more independent (X) variables. In logistic regression, it is the probability of the X variables predicting Y that is used. What is the likelihood that the X variable(s) will predict Y if a probability value of .1 is produced?

Which of the following is the most appropriate example of logistic regression?

Which of the following variables best exemplify multinomial logistic regression?

What type of distribution does deviance follow?

Why were the answers provided in Q20 selected?

A value of 1.4 was obtained from the odds ratio calculation. How would this impact upon findings?

Which of the following is a categorical variable?

Which of the following is the most appropriate explanation of logistic regression?

Logistic regression uses the log-likelihood statistic to evaluate the fit of a model, based on the predicted and observed values, and is closely related to the deviance, but how is deviance expressed?

Logistic regression is most appropriate on which of the following models?

If you inspect the data and find a standardized residual greater than 3, what would this suggest?

As with the t-statistic in linear regression, the z-statistic is used in logistic regression, but what does the value tell the researcher?

Based on the data presented above, three of the variables (undisclosed) were deemed sufficient to predict the outcome of a match. A chi-square value of 6.74 was obtained. How would you interpret this value?

Which of the following variables best exemplify binary logistic regression?

Cook's distance is a measure used to estimate the influence of a specific data point. What would a value of 0.8 suggest?

What is meant by the term 'parsimony'?

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