RMS Error

RMS error or Root Mean Square error is an estimate of the unexplained variability remaining after a model has been used to fit the data. If the model is good, RMS error should be small compared with the mean value of the response.

Values: Theoretically, the smallest value of RMS Error is zero. However, this implies a perfect fit which is unlikely and should, therefore, be suspect. In general, values which are two orders of magnitude smaller than the mean value of the response are good.

Troubleshooting: If the R-Squared values are very good and the RMS Error is large, it indicates that the model is reasonably good but there is a lot of variability in the data. For physical experiments, it may be useful to check pure repeatability. If both R-squared values and RMS Error values are poor, it is advisable to check the validity of the model and the data.