Logistic Regression

Alfred DeMaris and Steven H. Selman
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Alfred DeMaris: Bowling Green State University
Steven H. Selman: University of Toledo, Department of Urology

Chapter Chapter 7 in Converting Data into Evidence, 2013, pp 115-136 from Springer

Abstract: 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.

Keywords: Logistic Regression; Study Endpoint; Coffee Consumption; Caffeine Intake; Charlson Score (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-7792-1_7

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DOI: 10.1007/978-1-4614-7792-1_7

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