Cubic Splines and Additive Models

Jonathon D. Brown
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Jonathon D. Brown: University of Washington, Department of Psychology

Chapter Chapter 9 in Advanced Statistics for the Behavioral Sciences, 2018, pp 289-319 from Springer

Abstract: Abstract Statistical models commonly assume that the relation between a predictor and a criterion can be described by a straight line. This assumption is often appropriate, but there are times when abandoning it is warranted. Under these circumstances, we have two choices: adapt a linear model to accommodate nonlinear relations (e.g., transform the variables; add cross product terms) or use statistical techniques that directly model nonlinear relations.

Date: 2018
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DOI: 10.1007/978-3-319-93549-2_9

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