The alias matrix addresses the issue of how terms that are not included in the model affect the estimation of the model terms, if they are indeed active. In the Alias Terms outline, you list potentially active effects that are not in your assumed model but that might bias the estimates of model terms. The alias matrix entries represent the degree of bias imparted to model parameters by the Alias Terms effects. See Alias Terms and Alias Matrix.

The Alias Matrix Summary table lists the terms in the assumed model. These are the terms that correspond to effects listed in the Model outline. Given a design, for each entry in the Term column, the square root of the sum of the squared alias matrix entries for the terms corresponding to effects in the Alias Terms outline is computed. This value is reported in the Root Mean Squared Values column for the given design. For an example, see Example of Calculation of Alias Matrix Summary Values.

Alias Matrix Summary for Two Designs shows the Alias Matrix Summary report for the Plackett-Burman and Definitive Screening designs constructed in Designs of Same Run Size, with only main effects in the Model outline. All two-factor interactions are in the Alias Terms list. The table shows that, for the Definitive Screening Design, main effects are uncorrelated with two-factor interactions.

The Root Mean Squares Values are colored according to a color gradient shown under the Alias Matrix Summary table. You can control the color legend using the options in the Alias Matrix Summary red triangle menu. See Color Dashboard.

For additional background on the Alias Matrix, see The Alias Matrix in Technical Details. See also Lekivetz, R. (2014).

For example, to obtain the Alias Matrix Summary entry in Alias Matrix Summary for Two Designs corresponding to X1, square the terms in the row for X1 in the Alias Matrix, average these, and take the square root. You obtain 0.2722.