The Difference between Linear and Nonlinear Regression Models - Statistics By Jim
The difference between linear and nonlinear regression models isn’t as straightforward as it sounds. You’d think that linear equations produce straight lines and nonlinear equations model curvature. Unfortunately, that’s not correct. Both types of models can fit curves to your data—so that’s not the defining characteristic. In this post, I’ll teach you how to identify linear and nonlinear regression models. The difference between nonlinear and linear is the “non.” OK, that sounds like a joke, but, honestly, that’s the easiest way to understand the difference. First, I’ll define what linear regression is, and then everything else must be nonlinear regression. I’ll include examples of both linear and nonlinear regression models. A linear regression model follows a very particular form. In statistics, a regression model is linear when all terms in the model are one of the following: Then, you build the equation by only adding the terms together. These rules limit the form to just one type
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