Question 1

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. A

Question 2

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. B

Question 3

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 4

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.

Question 5

Examine the performance analysis data below. Which variables may cause problems with the analysis by inflating the standard error? (You may select more than one option.)

Accepted Answer

A)  Shots on target 
B)  Shots off target 
C)  Goals scored 
D)  Unforced errors 
A)  Shots on target 
B)  Shots off target 
C)  Goals scored 
D)  Unforced errors

Question 6

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

Question 7

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 8

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 9

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 10

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 11

Which of the following statements best describes stepwise regression?

Accepted Answer

A)  Predictors are entered into an equation based on statistical analysis as opposed to underpinning theory. 
B)  Predictors are entered into an equation based on underpinning theory as opposed to statistical analysis. 
C)  Predictors are entered into an equation based on underpinning theory and statistical analysis. 
D)  Predictors are entered into an equation based on random allocation. 
A)  Predictors are entered into an equation based on statistical analysis as opposed to underpinning theory. 
B)  Predictors are entered into an equation based on underpinning theory as opposed to statistical analysis. 
C)  Predictors are entered into an equation based on underpinning theory and statistical analysis. 
D)  Predictors are entered into an equation based on random allocation.

Question 12

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 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

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 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

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 17

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 18

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

Question 19

Which of the following assumptions should be examined to ensure that bias is minimized? (You may select more than one option.)

Accepted Answer

A)  Linearity between the continuous predictors and the logit of the outcome variable 
B)  Outcome variables follow a chi-square distribution 
C)  Independence of errors, thus reducing overdispersion 
D)  The continuous predictors follow a log transformation 
A)  Linearity between the continuous predictors and the logit of the outcome variable 
B)  Outcome variables follow a chi-square distribution 
C)  Independence of errors, thus reducing overdispersion 
D)  The continuous predictors follow a log transformation

Question 20

How does logistic regression modelling overcome the issue of violating linearity?

Accepted Answer

A)  Equation expressed in logarithmic terms 
B)  Equation expressed in sigma terms 
C)  Equation expressed in alpha terms 
D)  Equation expressed in polynomial terms 
A)  Equation expressed in logarithmic terms 
B)  Equation expressed in sigma terms 
C)  Equation expressed in alpha terms 
D)  Equation expressed in polynomial terms

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

Why were the answers provided in Q20 selected?

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

What is meant by the term 'parsimony'?

Examine the performance analysis data below. Which variables may cause problems with the analysis by inflating the standard error? (You may select more than one option.)

By what alternative name is the z-statistic known?

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?

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

Which of the following statements best describes stepwise regression?

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

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

Which of the following variables best exemplify multinomial logistic regression?

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?

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 assumptions should be examined to ensure that bias is minimized? (You may select more than one option.)

How does logistic regression modelling overcome the issue of violating linearity?

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

Filters

Exam 20: Categorical Outcomes: Logistic Regression

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

Why were the answers provided in Q20 selected?

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

What is meant by the term 'parsimony'?

Examine the performance analysis data below. Which variables may cause problems with the analysis by inflating the standard error? (You may select more than one option.)

By what alternative name is the z-statistic known?

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?

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

Which of the following statements best describes stepwise regression?

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

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

Which of the following variables best exemplify multinomial logistic regression?

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?

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 assumptions should be examined to ensure that bias is minimized? (You may select more than one option.)

How does logistic regression modelling overcome the issue of violating linearity?

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

Filters