Question 1

You obtain a regression coefficient of .132. This is interpreted as the predicted log of the odds of the dependent variable increases by .132 for a unit increase in the independent variable.

Accepted Answer

In logistic regression, a regression coefficient represents the change in the log odds of the dependent variable for a one-unit change in the independent variable, so a coefficient of .132 means the predicted log odds increase by .132 for a unit increase in the independent variable.

Question 2

Exponentiating the value in question 21 results in a value of 1.14. This is interpreted as a 114% increase in the odds of the dependent variable with a one unit increase in the independent variable.

Accepted Answer

Exponentiating the value results in an odds ratio of 1.14, which is interpreted as a 14% increase in the odds of the dependent variable with a one unit increase in the independent variable, not 114%.

Question 3

The logistic regression model is appropriate for predicting a dependent variable with 3 categories.

Accepted Answer

Logistic regression is suitable for binary outcomes. For a dependent variable with 3 categories, multinomial logistic regression is appropriate.

Question 4

While there are disadvantages of using a predicted probability model, one advantage is the ease of interpreting the effect of the independent variable on the dependent variable.

Accepted Answer

Predicted probability models, such as logistic regression, output probabilities that can be directly interpreted as the effect of a one-unit change in the independent variable on the probability of the dependent variable occurring, making the interpretation of the model's coefficients straightforward in terms of effect size and direction.

Question 5

What are the three main issues of predicting a binary dependent variable in an OLS regression equation? Explain each.

Accepted Answer

The answer of What are the three main issues of...

Quiz 14: Regression Analysis With a Dichotomous Dependent Variable: Logit Models

You obtain a regression coefficient of .132. This is interpreted as the predicted log of the odds of the dependent variable increases by .132 for a unit increase in the independent variable.

Exponentiating the value in question 21 results in a value of 1.14. This is interpreted as a 114% increase in the odds of the dependent variable with a one unit increase in the independent variable.

The logistic regression model is appropriate for predicting a dependent variable with 3 categories.

While there are disadvantages of using a predicted probability model, one advantage is the ease of interpreting the effect of the independent variable on the dependent variable.

What are the three main issues of predicting a binary dependent variable in an OLS regression equation? Explain each.