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 Polytomous Categorical Dependent Variables: Multinomial and Ordinal Logistic Regression

### Simple Linear Models With Polytomous Categorical Dependent Variables: Multinomial and Ordinal Logistic Regression

Not all outcomes are continuous or simple dichotomous variables. In this chapter, we will explore outcomes that are a bit more complex. Think of career choice. There are many ways we can group careers, but whatever one you use, there is little rationale for ordering or prioritizing careers. Using the techniques covered in this chapter, you can model predictors of career choice.

Graduation from high school or college is also a complex phenomenon, although in the last chapter, we viewed it as a binary (yes/no) outcome. For example, some students graduate on time, whereas some graduate but not on time (some graduate early, some late). Some students drop out of school, ...