In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. The author returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.

# Simple Linear Models With Categorical Dependent Variables: Binary Logistic Regression

### Simple Linear Models With Categorical Dependent Variables: Binary Logistic Regression

What if your research question doesn’t fall neatly into one of the models we have discussed already? What if you are interested in understanding what predicts whether a student will graduate, whether someone will get sick or be healthy, whether someone saves for retirement, or whether an individual voted in the last election?

In this chapter, we will cover the basic aspects of linear modeling when you have a categorical (binary yes/no or 1/0) dependent variable (DV). We will efficiently move through basic logistic regression modeling when there is either a binary or continuous independent variable (IV; it is also possible to perform a logistic regression with a categorical IV as in Chapter 4). ...