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

The logistic regression coefficient can be transformed into an estimated predicted probability to make it more easily interpreted.

In the logistic regression model, the constant and regression coefficients are estimated using ______.

The likelihood of a model is ______.

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.

Applying an OLS regression equation to a binary dependent variable is called a ______.

Which of the following is NOT a problem that may arise from estimating a binary dependent variable in an OLS regression equation rather than a logistic regression equation ______.

When testing the improvement in the likelihood functions between a baseline model and a model containing two independent variable, obtaining a chi-square less than the critical value means one would ______.

Estimating an OLS model on a binary dependent variable will never result in predicted values of the dependent variable that are less than 0 or greater than 1.

By exponentiating the coefficient for an independent variable in a logistic regression (also called the "antilog"), one will obtain ______.

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

Logistic regression is used when the dependent variable has binary values.

The OLS model is a nonlinear model while the logit model is linear.

The Wald statistic is comparable to the chi-square distribution.

A good logistic model, one wherein the probability of the observed results is high, is one with ______.

In contrast to OLS regression, in logistic regression the change in the probability of y with a 1 unit change in the independent variable is ______.

A perfect fitting model would have a likelihood equal to 1 and -2LL equal to 0.

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.

The odds multiplier reflects the change in the odds of the dependent variable occurring when the dependent variable ______.

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.

To obtain the percent change in the odds of the dependent variable, one must ______.