ABSTRACT
In the past two chapters, we have examined ordinary least squares (OLS) regressionsimple and multiple regression models-that allow us to examine the relationship between one or more predictors when the outcome is continuous. In this chapter, we are introduced to logistic regression, which can be used when the outcome is categorical. For the purposes of this chapter, we will concentrate on binary logistic regression which is used when
the outcome has only two categories (i.e., dichotomous, binary, or sometimes referred to as a Bernoulli outcome). The logistic regression procedure appropriate for more than two categories is called multinomial (or polytomous) logistic regression. Readers interested in learning more about multinomial logistic regression will be provided some additional references later in this chapter. Also in this chapter, we discuss methods that can be used to enter predictors in logistic regression models. Our objectives are that by the end of this chapter, you will be able to (a) understand the concepts underlying logistic regression, (b) determine and interpret the results of logistic regression, (c) understand and evaluate the assumptions of logistic regression, and (d) have a basic understanding of methods of entering the covariates.
