Logistic Regression Ch.9 Full Test Bank - Political Analysis 6e Complete Test Bank by Philip H. Pollock. DOCX document preview.
Chapter 9: Logistic Regression
Test Bank
Multiple Choice
1. Logistic regression is designed to predict the ______ of an event occurring.
A. proportion
B. variability
C. probability
D. category
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Knowledge
Answer Location: The Logistic Regression Approach
Difficulty Level: Easy
2. Suppose the probability of voting equals .6 and the probability of not voting equals .4. What are the odds of voting?
A. 1:2
B. 2:1
C. 3:2
D. 6:1
Learning Objective: 9-2: How logistic regression is similar to--and different from--ordinary least squares regression.
Cognitive Domain: Application
Answer Location: The Logistic Regression Approach
Difficulty Level: Hard
3. As we move from one value of the independent variable to the next, we describe the relationship between it and a change in the category of the dependent variable as the ______.
A. probability
B. logged odds
C. exponent
D. odds ratio
Learning Objective: 9-2: How logistic regression is similar to--and different from--ordinary least squares regression.
Cognitive Domain: Comprehension
Answer Location: Logistic Regression Approach to Vote Choice in the 2016 Presidential Election
Difficulty Level: Easy
4. Logistic regression uses natural logarithms known as ______.
A. Base-10
B. Base-e
C. Base-2
D. Base-b
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Knowledge
Answer Location: The Logistic Regression Approach
Difficulty Level: Easy
5. A natural logarithm is approximately equal to ______.
A. 2
B. 2.5
C. 2.72
D. 2.83
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Knowledge
Answer Location: The Logistic Regression Approach
Difficulty Level: Easy
6. The natural log of a correct guess is called the ______ in logistic regression.
A. logged likelihood
B. maximum likelihood
C. pseudo R-square
D. outcome
Learning Objective: 9-3: How maximum likelihood estimation works.
Cognitive Domain: Comprehension
Answer Location: Finding the Best Fit: Maximum Likelihood Estimation
Difficulty Level: Easy
7. MLE compares the probability of a correct guess between two models by ______ the logged likelihood of Model 1 ______ Model 2.
A. adding; to
B. subtracting; from
C. multiplying; and
D. dividing; and
Learning Objective: 9-3: How maximum likelihood estimation works.
Cognitive Domain: Comprehension
Answer Location: Finding the Best Fit: Maximum Likelihood Estimation
Difficulty Level: Easy
8. Logistic regression estimates the ______ effects at the means when using more than one independent variable.
A. marginal
B. maximum
C. minimum
D. micro
Learning Objective: 9-5: How to use probabilities to interpret logistic regression results.
Cognitive Domain: Comprehension
Answer Location: Marginal Effects at the Means
Difficulty Level: Easy
9. MEMs stands for ______ effects at the means.
A. maximum
B. marginal
C. minimal
D. multiple
Learning Objective: 9-5: How to use probabilities to interpret logistic regression results.
Cognitive Domain: Knowledge
Answer Location: Marginal Effects at the Means
Difficulty Level: Easy
10. MERs stands for ______ effects at representative values.
A. minimal
B. maximum
C. marginal
D. multiple
Learning Objective: 9-5: How to use probabilities to interpret logistic regression results.
Cognitive Domain: Knowledge
Answer Location: Marginal Effects at Representative Values
Difficulty Level: Easy
11. A variable coded as voted/not voted is an example of a ______ variable.
A. hypothetical
B. maximized
C. logistic
D. binary
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Comprehension
Answer Location: Introduction
Difficulty Level: Easy
12. To use logistic regression with a categorical dependent variable, at least one independent variable must be measured at the ______ level.
A. nominal
B. ordinal
C. ratio
D. interval
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Comprehension
Answer Location: Introduction
Difficulty Level: Easy
13. If both the independent and dependent variables are ______ level, then ordinary least squares (OLS) regression would most likely be applied.
A. nominal
B. ordinal
C. ratio
D. interval
Learning Objective: 9-2: How logistic regression is similar to--and different from--ordinary least squares regression.
Cognitive Domain: Comprehension
Answer Location: Introduction
Difficulty Level: Easy
14. The probability of an event can be expressed as a percentage between 0 and ______.
A. 10
B. 50
C. 75
D. 100
Learning Objective: 9-2: How logistic regression is similar to--and different from--ordinary least squares regression.
Cognitive Domain: Knowledge
Answer Location: The Logistic Regression Approach
Difficulty Level: Easy
15. The ______ of an event is equal to the number of times it occurs divided by the total number of chances for it to occur.
A. odds
B. probability
C. logged odds
D. proportion
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Comprehension
Answer Location: The Logistic Regression Approach
Difficulty Level: Easy
16. The ______ of an outcome are a ratio of the number of expected occurrences to the number of occurrences of other outcome(s).
A. probability
B. odds
C. proportion
D. chances
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Comprehension
Answer Location: The Logistic Regression Approach
Difficulty Level: Easy
17. ______ expresses a number as an exponent of some constant or base.
A. Common logarithms
B. Logged odds
C. Odds
D. Probability
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Comprehension
Answer Location: The Logistic Regression Approach
Difficulty Level: Easy
18. If there is a .10 probability of an event happening, there is a .90 chance of the event not happening and the odds of the event are ______.
A. .02
B. .07
C. .11
D. .57
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Application
Answer Location: The Logistic Regression Approach
Difficulty Level: Medium
19. The relationship between the odds at one value of the independent variable compared with the odds at the next lower value of the independent variable is called the ______.
A. common logarithm
B. likelihood function
C. logit
D. odds ratio
Learning Objective: 9-2: How logistic regression is similar to--and different from--ordinary least squares regression.
Cognitive Domain: Comprehension
Answer Location: Logistic Regression Approach to Vote Choice in the 2016 Presidential Election
Difficulty Level: Medium
20. Which of the following tells the researcher how completely the independent variable explains the dependent variable.
A. Z-score
B. χ2
C. Pearson’s r
D. R-square
Learning Objective: 9-2: How logistic regression is similar to--and different from--ordinary least squares regression.
Cognitive Domain: Application
Answer Location: Logistic Regression Approach to Vote Choice in the 2016 Presidential Election
Difficulty Level: Medium
True/False
1. Logistic regression works well with multiple independent variables regardless of the level of measurement.
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Comprehension
Answer Location: Introduction
Difficulty Level: Easy
2. A binary variable is one that can assume only two values.
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Knowledge
Answer Location: Introduction
Difficulty Level: Easy
3. Both OLS and logistic regression are flexible in that they permit the use of multiple independent variables, including dummy independent variables.
Learning Objective: 9-2: How logistic regression is similar to--and different from--ordinary least squares regression.
Cognitive Domain: Comprehension
Answer Location: Introduction
Difficulty Level: Easy
4. MLE stands for maximum likelihood estimation.
Learning Objective: 9-3: How maximum likelihood estimation works.
Cognitive Domain: Knowledge
Answer Location: Finding the Best Fit: Maximum Likelihood Estimation
Difficulty Level: Easy
5. A number that summarizes how well a model’s predictions fit the observed data is called an estimator.
Learning Objective: 9-3: How maximum likelihood estimation works.
Cognitive Domain: Knowledge
Answer Location: Finding the Best Fit: Maximum Likelihood Estimation
Difficulty Level: Easy
6. Logistic regression can only be used with one dependent and one independent variable.
Learning Objective: 9-4: How to use logistic regression with multiple independent variables.
Cognitive Domain: Comprehension
Answer Location: Logistic Regression with Multiple Independent Variables
Difficulty Level: Easy
7. Dummy variables can be used with logistic regression.
Learning Objective: 9-4: How to use logistic regression with multiple independent variables.
Cognitive Domain: Comprehension
Answer Location: Introduction
Difficulty Level: Easy
8. Logistic regression produces a χ2 statistic we can use to determine the significance of the association between the variables.
Learning Objective: 9-4: How to use logistic regression with multiple independent variables.
Cognitive Domain: Knowledge
Answer Location: Finding the Best Fit: Maximum Likelihood Estimation
Difficulty Level: Easy
9. Common logarithms are used widely in electronics and experimental sciences.
Learning Objective: 9-1: How to use logistic regression to describe the relationship between an interval-level independent variable and a dichotomous dependent variable.
Cognitive Domain: Knowledge
Answer Location: The Logistic Regression Approach
Difficulty Level: Easy
10. Maximum likelihood estimation (MLE) is the heart and soul of logistic regression.
Learning Objective: 9-3: How maximum likelihood estimation works.
Cognitive Domain: Knowledge
Answer Location: Finding the Best Fit: Maximum Likelihood Estimation
Difficulty Level: Easy
11. The maximum likelihood estimation employs the same approach as the proportional reduction error.
Learning Objective: 9-3: How maximum likelihood estimation works.
Cognitive Domain: Knowledge
Answer Location: Finding the Best Fit: Maximum Likelihood Estimation
Difficulty Level: Easy
12. The likelihood function can take on any value between 0 and 100.
Learning Objective: 9-3: How maximum likelihood estimation works.
Cognitive Domain: Knowledge
Answer Location: Finding the Best Fit: Maximum Likelihood Estimation
Difficulty Level: Easy
13. The marginal effects at the medium approach is useful when more than one independent variable is measured at the interval-level and researches want to convey the effect of one interval-level independent variable on the probability of the outcome, while other interval-level independent variables are help constant at their mean values.
Learning Objective: 9-4: How to use logistic regression with multiple independent variables.
Cognitive Domain: Comprehension
Answer Location: Marginal Effects at the Means
Difficulty Level: Medium
14. Switchover points and full effects can be especially valuable interpretive tools in comparing marginal effects at representative values.
Learning Objective: 9-4: How to use logistic regression with multiple independent variables.
Cognitive Domain: Comprehension
Answer Location: Marginal Effects at Representative Values
Difficulty Level: Medium
15. Marginal effects at the means and marginal effects at representative values are not mutually exclusive strategies for analyzing probabilities.
Learning Objective: 9-4: How to use logistic regression with multiple independent variables.
Cognitive Domain: Comprehension
Answer Location: Marginal Effects at Representative Values
Difficulty Level: Medium