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R-squared Is Not Valid for Nonlinear Regression - Statistics By Jim

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Nonlinear regression is an extremely flexible analysis that can fit most any curve that is present in your data. R-squared seems like a very intuitive way to assess the goodness-of-fit for a regression model. Unfortunately, the two just don’t go together. R-squared is invalid for nonlinear regression. Some statistical software calculates R-squared for these models even though it is statistically incorrect. Consequently, it’s important that you understand why you should not trust R-squared for models that are not linear. In this post, I highlight research that shows you how assessing R-squared for nonlinear regression causes serious problems and leads you astray. In my post about how to interpret R-squared, I explain how R-squared is the following proportion: Furthermore, the variances always add up in a particular way: Explained variance + Error variance = Total variance. This arrangement produces an R-squared that is always between 0 – 100%. That all makes sense, right? For linear mod

Nonlinear regression is an extremely flexible analysis that can fit most any curve that is present in your data. R-squared seems like a very intuitive way to assess the goodness-of-fit for a regression model. Unfortunately, the two just don't go together. R-squared is invalid for nonlinear regression. Example of a nonlinear model that displays the relationship between density and electron mobility. Some statistical software calculates R-squared for these models even though it is statistically incorrect. Consequently, it's important that you understand why you should not trust R-squared for mod

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