Lecture 6 - Logistic Regression

Question 1

What is the primary purpose of logistic regression?

Answer 1

To model categorical outcomes based on predictor variables.

Question 2

What type of outcome does logistic regression model?

Answer 2

Categorical outcomes.

Question 3

What are the two main types of logistic regression?

Answer 3

Binomial Logistic Regression and Multinomial Logistic Regression.

Question 4

How does logistic regression differ from linear regression in terms of outcome?

Answer 4

Logistic regression is used for categorical outcomes, while linear regression is used for continuous outcomes.

Question 5

What is the equation for logistic regression?

Answer 5

y = 1 / (1 + e^(-v)), where v = bx + c.

Question 6

What does the odds ratio represent in logistic regression?

Answer 6

The measure of association between predictor variables and the outcome, indicating how the odds change with a unit increase in the predictor.

Question 7

What test is used to evaluate the significance of individual predictors in logistic regression?

Answer 7

Wald Test.

Question 8

What does a significant Wald Test indicate?

Answer 8

That the predictor variable significantly contributes to the model.

Question 9

Name a test used to assess the goodness-of-fit in logistic regression.

Answer 9

Hosmer and Lemeshow Test.

Question 10

What does the Hosmer and Lemeshow Test evaluate?

Answer 10

How well the model’s predicted outcomes match the actual outcomes.

Question 11

What is multicollinearity and why is it important in logistic regression?

Answer 11

Multicollinearity refers to high correlations between predictor variables, which can affect the stability and interpretation of the regression coefficients.

Question 12

What assumption in logistic regression involves checking the linearity of the logit?

Answer 12

The assumption that there is a linear relationship between the logit of the dependent variable and the predictor.

Question 13

What procedure is used to test the linearity of the logit?

Answer 13

Box-Tidwell procedure.

Question 14

What is the difference between the ‘Enter’ method and the ‘Stepwise’ method in logistic regression?

Answer 14

The ‘Enter’ method includes all predictors simultaneously, while the ‘Stepwise’ method adds or removes predictors based on statistical criteria.

Question 15

How can you interpret the coefficient in logistic regression?

Answer 15

By exponentiating the coefficient, which shows how the odds change with a unit increase in the predictor.

Question 16

What is a common way to report the overall significance of a logistic regression model?

Answer 16

Using the chi-squared (Χ²) test.

Question 17

What does a significant chi-squared (Χ²) test result indicate in logistic regression?

Answer 17

That the model with the predictors explains a significant proportion of the variance in the outcome.

Question 18

How is predictive accuracy assessed in logistic regression?

Answer 18

By evaluating the proportion of correctly classified cases and comparing observed and predicted outcomes.

Question 19

What are the pseudo R-squared values used for in logistic regression?

Answer 19

To provide an approximation of the variance explained by the model, similar to R² in linear regression.

Question 20

In the context of logistic regression, what does an odds ratio greater than 1 signify?

Answer 20

That the odds of the outcome increase with an increase in the predictor.

Question 21

In the context of logistic regression, what does an odds ratio less than 1 signify?

Answer 21

That the odds of the outcome decrease with an increase in the predictor.

Question 22

What is the Box-Tidwell procedure used for in logistic regression?

Answer 22

To test the linearity of the logit for continuous predictors.

Question 23

What steps are involved in performing logistic regression analysis in SPSS?

Answer 23

Select predictors and dependent variable, configure options, interpret output.