Abstract
Linear regression is a widely applicable modeling tool, but it is not appropriate when the correct model should be nonlinear in the parameters. Such is the case when the study endpoint is a binary variable. The model becomes nonlinear because what is being modeled is the probability that a case experiences the event of interest or that a case is in a particular category of the binary response. As a probability must fall between 0 and 1, the linear regression model cannot accommodate it. In this chapter, we examine this important principle, develop the logistic regression model as an alternative, and consider several examples of this modeling strategy from the research literature.
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DeMaris, A., Selman, S.H. (2013). Logistic Regression. In: Converting Data into Evidence. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7792-1_7
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DOI: https://doi.org/10.1007/978-1-4614-7792-1_7
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