Lesson 6: Logistic Regression. Thus far our focus has been on describing interactions or associations between two or three categorical variables mostly via single summary statistics and with significance testing. From this lesson on, we will focus on modeling. Models can handle more complicated situations and analyze the simultaneous effects of multiple variables, including mixtures of categorical and continuous variables. In Lesson 6 and Lesson 7, we study the binary logistic regression , which we will see is an example of a generalized linear model . Binary Logistic Regression is a special type of regression where binary response variable is related to a set of explanatory variables , which can be discrete and/or continuous. The important point here to note is that in linear regression, the expected values of the response variable are modeled based on combination of values taken by the predictors. In logistic regression Probability or Odds of the response taking a particular value is modeled based on combination of values taken by the predictors. Like regression (and unlike log-linear models that we will see later), we make an explicit distinction between a response variable and one or more predictor (explanatory) variables. We begin with two-way tables, then progress to three-way tables, where all explanatory variables are categorical. Then we introduce binary logistic regression with continuous predictors as well. In the last part we will focus on more model diagnostics and model selection. Logistic regression is applicable, for example, if:
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