In a previous article, I showed why there is no R-squared for nonlinear regression. Anyone who uses nonlinear regression will also notice that there are no P values for the predictor variables. What’s going on?

Just like there are good reasons not to calculate R-squared for nonlinear regression, there are also good reasons not to calculate P values for the coefficients.

Why not—and what to use instead—are the subjects of this column.

Why are P values possible for linear regression?

This may be an unexpected question, but the best way to understand why there are no P values in nonlinear regression is to understand why you can calculate them in linear regression.

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