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.
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