Calculate residual standard error and what it says about model fit.
Residual standard error is the typical prediction error in the response’s own units, which makes it far more interpretable than R-squared. Expressing it relative to the mean gives a scale-free view. A model with high R-squared can still have a residual error too large to be useful, which is why RSE should always be reported alongside it.
Residual Standard Error
RSE = √(Σresiduals² ÷ (n − p − 1))
RSE = √(Σresiduals² ÷ (n − p − 1)) Residual standard error is the typical prediction error in the response’s own units, which makes it far more interpretable than R-squared. Expressing it relative to the mean gives a scale-free view.
A model with high R-squared can still have a residual error too large to be useful, which is why RSE should always be reported alongside it.
This calculator takes 4 inputs: Sum of squared residuals, Sample size, Number of predictors, Mean of the response. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.