Deck 20: Categorical Outcomes: Logistic Regression

Question 1

By what alternative name is the z-statistic known?

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

Answer 1

Wald statistic

Question 2

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?

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

Answer 2

Deviance = -2 × log-likelihood

Question 3

Why were the answers provided in Q20 selected?

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.

Answer 3

The variable(s) have a value of 5 or less in them.

Question 4

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?

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

Question 5

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

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

Question 6

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?

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 7

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?

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

Question 8

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

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 9

Which of the following variables best exemplify multinomial logistic regression?

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

Question 10

What type of distribution does deviance follow?

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

Question 11

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?

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

Question 12

Which of the following statements best describes stepwise regression?

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 13

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

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

Question 14

What is meant by the term 'parsimony'?

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 15

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

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

Question 16

Which of the following is a categorical variable?

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 17

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

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 18

Which of the following variables best exemplify binary logistic regression?

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

Question 19

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

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 20

What range is used when evaluating the predictive ability of an independent (X) variable (e.g. numbers of passes in field hockey) and the outcome (Y) variable (e.g. outcome of the game - win, draw or lose)?

A) 1 to 100
B) 0 to 100
C) 0 to 1
D) -1 to +1

Question 21

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) 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 22

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

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 23

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

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.