Home →
Techniques and Tips →
@RISK Distribution Fitting →
**RMS Error Calculation in Distribution Fitting with (x,y) Pairs**

**Applies to:** @RISK 5.x–7.x

How does @RISK calculate the RMS error that it uses for fit ranking?

For curve data—x values with associated probability densities or cumulative probabilities—@RISK computes the root-mean-square error as a measure of goodness of fit. The equation is in the help file, but it can be hard to relate that to the computations for your particular data set.

The attached example shows how @RISK computes the RMS error for normalized or unnormalized (x,y) data, where y is the height of the probability density curve or relative frequency curve. Although the RMS calculation is the same in @RISK 5.x and 6.x, the example requires @RISK 6.0 or higher because it uses the new RiskFit functions that were introduced in @RISK 6.0.

**See also:** RMS Error Calculation in Distribution Fitting with (x,p) Pairs.

Last edited: 2015-06-19

This page was: Helpful |
Not Helpful