This is a signal-to-noise ratio. It compares the range of the predicted values at the design points to the average prediction error. Ratios greater than 4 indicate adequate model discrimination.

\[\frac{max(\hat{Y})\ -\ min(\hat{Y})}{\sqrt{\bar{V}_{\hat{Y}}}} > 4\]

\[\bar{V}_{\hat{Y}} = \frac{p\hat{\sigma}^2}{n}\]

Where,

\(\hat{Y}\) are the predictions at the run settings

\(p\hat{\sigma}^2\) is the residual mean square from ANOVA table.

\(p\) is the number of terms in the model.

\(n\) is the number of runs in the design.