Ordinary Least Squares regression provides linear models of continuous variables. However, much data of interest to statisticians and researchers are not continuous and so other methods must be used to create useful predictive models.

The glm() command is designed to perform generalized linear models (regressions) on binary outcome data, count data, probability data, proportion data and many other data types.

In this blog post, we explore the use of R’s glm() command on one such data type. Let’s take a look at a simple example where we model binary data.

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Logistic Regression can be used only for binary dependent variables. It can be invoked using the menu choices at right or through the LOGISTIC REGRESSION syntax command.

The dependent variable must have only two values. If you specify a variable with more than two, you’ll get an error.

One big advantage of this procedure is it allows you to build successive models by entering a group of predictors at a time.

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