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26.4: Beyond Linear Predictors and Outcomes

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    It is important to note that despite the fact that it is called the general linear model, we can actually use the same machinery to model effects that don’t follow a straight line (such as curves). The “linear” in the general linear model doesn’t refer to the shape of the response, but instead refers to the fact that model is linear in its parameters — that is, the predictors in the model only get multiplied the parameters, rather than a nonlinear relationship like being raised to a power of the parameter. It’s also common to analyze data where the outcomes are binary rather than continuous, as we saw in the chapter on categorical outcomes. There are ways to adapt the general linear model (known as generalized linear models) that allow this kind of analysis. We will explore both of these points in more detail in the following chapter.

    This page titled 26.4: Beyond Linear Predictors and Outcomes is shared under a not declared license and was authored, remixed, and/or curated by Russell A. Poldrack via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.