Question 1

MLE compares the probability of a correct guess between two models by ______ the logged likelihood of Model 1 ______ Model 2.

Accepted Answer

A)  adding; to 
B)  subtracting; from 
C)  multiplying; and 
D)  dividing; and 
A)  adding; to 
B)  subtracting; from 
C)  multiplying; and 
D)  dividing; and

Question 2

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 ______.

Accepted Answer

A)  probability 
B)  logged odds 
C)  exponent 
D)  odds ratio 
A)  probability 
B)  logged odds 
C)  exponent 
D)  odds ratio

Question 3

If both the independent and dependent variables are ______ level, then ordinary least squares (OLS) regression would most likely be applied.

Accepted Answer

A)  nominal 
B)  ordinal 
C)  ratio 
D)  interval 
A)  nominal 
B)  ordinal 
C)  ratio 
D)  interval

Question 4

A variable coded as voted/not voted is an example of a ______ variable.

Accepted Answer

A)  hypothetical 
B)  maximized 
C)  logistic 
D)  binary 
A)  hypothetical 
B)  maximized 
C)  logistic 
D)  binary

Question 5

MEMs stands for ______ effects at the means.

Accepted Answer

A)  maximum 
B)  marginal 
C)  minimal 
D)  multiple 
A)  maximum 
B)  marginal 
C)  minimal 
D)  multiple

Question 6

Suppose the probability of voting equals .6 and the probability of not voting equals .4. What are the odds of voting?

Accepted Answer

A)  1:2 
B)  2:1 
C)  3:2 
D)  6:1 
A)  1:2 
B)  2:1 
C)  3:2 
D)  6:1

Question 7

Logistic regression produces a $\chi$2 statistic we can use to determine the significance of the association between the variables.

Accepted Answer

A) True 
 B)False

Question 8

Dummy variables can be used with logistic regression.

Accepted Answer

A) True 
 B)False

Question 9

MLE stands for maximum likelihood estimation.

Accepted Answer

A) True 
 B)False

Question 10

Logistic regression is designed to predict the ______ of an event occurring.

Accepted Answer

A)  proportion 
B)  variability 
C)  probability 
D)  category 
A)  proportion 
B)  variability 
C)  probability 
D)  category

Question 11

A binary variable is one that can assume only two values.

Accepted Answer

A) True 
 B)False

Question 12

Common logarithms are used widely in electronics and experimental sciences.

Accepted Answer

A) True 
 B)False

Question 13

Logistic regression uses natural logarithms known as ______.

Accepted Answer

A)  Base-10 
B)  Base-e 
C)  Base-2 
D)  Base-b 
A)  Base-10 
B)  Base-e 
C)  Base-2 
D)  Base-b

Question 14

The maximum likelihood estimation employs the same approach as the proportional reduction error.

Accepted Answer

A) True 
 B)False

Question 15

Logistic regression can only be used with one dependent and one independent variable.

Accepted Answer

A) True 
 B)False

MLE compares the probability of a correct guess between two models by the logged likelihood of Model 1 Model 2.

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 ______.

If both the independent and dependent variables are ______ level, then ordinary least squares (OLS) regression would most likely be applied.

A variable coded as voted/not voted is an example of a ______ variable.

MEMs stands for ______ effects at the means.

Suppose the probability of voting equals .6 and the probability of not voting equals .4. What are the odds of voting?

Logistic regression produces a $\chi$ ² statistic we can use to determine the significance of the association between the variables.

Dummy variables can be used with logistic regression.

MLE stands for maximum likelihood estimation.

Logistic regression is designed to predict the ______ of an event occurring.

A binary variable is one that can assume only two values.

Common logarithms are used widely in electronics and experimental sciences.

Logistic regression uses natural logarithms known as ______.

The maximum likelihood estimation employs the same approach as the proportional reduction error.

Logistic regression can only be used with one dependent and one independent variable.

The Definition and Measurement of Concepts

Measuring and Describing Variables

Proposing Explanations, Framing Hypotheses, and Making Comparisons

Research Design, Research Ethics, and Evidence of Causation

Making Controlled Comparisons

Foundations of Statistical Inference

Tests of Significance and Measures of Association

Correlation and Linear Regression

Filters

Exam 9: Logistic Regression

MLE compares the probability of a correct guess between two models by ______ the logged likelihood of Model 1 ______ Model 2.

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 ______.

If both the independent and dependent variables are ______ level, then ordinary least squares (OLS) regression would most likely be applied.

A variable coded as voted/not voted is an example of a ______ variable.

MEMs stands for ______ effects at the means.

Suppose the probability of voting equals .6 and the probability of not voting equals .4. What are the odds of voting?

Logistic regression produces a χ\chiχ 2 statistic we can use to determine the significance of the association between the variables.

Dummy variables can be used with logistic regression.

MLE stands for maximum likelihood estimation.

Logistic regression is designed to predict the ______ of an event occurring.

A binary variable is one that can assume only two values.

Common logarithms are used widely in electronics and experimental sciences.

Logistic regression uses natural logarithms known as ______.

The maximum likelihood estimation employs the same approach as the proportional reduction error.

Logistic regression can only be used with one dependent and one independent variable.

The Definition and Measurement of Concepts

Measuring and Describing Variables

Proposing Explanations, Framing Hypotheses, and Making Comparisons

Research Design, Research Ethics, and Evidence of Causation

Making Controlled Comparisons

Foundations of Statistical Inference

Tests of Significance and Measures of Association

Correlation and Linear Regression

Filters

MLE compares the probability of a correct guess between two models by the logged likelihood of Model 1 Model 2.

Logistic regression produces a $\chi$ ² statistic we can use to determine the significance of the association between the variables.