Constrained Surface Design Results - Confounding Tab

Select the Confounding tab of the Constrained Surface Design Results dialog to access options to review the correlation matrix for the design matrix, and thus to determine the confounding of main effects and interactions.

Correlation matrix

Click the Correlation matrix button to display a correlation matrix of the columns of the current design matrix. The number of effects displayed in this matrix depend on the choice of the model in the Compute correlations for box (see below). Note that these correlations are computed from the rescaled (to the ±1 range) factor values.

Inverse

Click the Inverse button to display (1) the inverse of the correlation matrix, and (2) the standardized inverse of the correlation matrix. This matrix can be interpreted as the correlation matrix of effects; that is, it is the standardized variance/covariance matrix of the parameter estimates for the current model. The greater the absolute value of a correlation between effects in this matrix, the more redundant are the respective effects.

Compute correlations for

The option selected in the Compute correlations for group box determines which terms will be included in the model. The common first and second-order response surface models are shown below, for the example case of a 2-factor design.

Linear main effects only.

y = b0 + b1*x1 + b2*x2

Lin./quad. main effects.

main effects.

y = b0 + b1*x1 + b2*x2 + b11*x1*x1 +b 22*x2*x2

Linear main eff. + 2-ways.

y = b0 + b1*x1 + b2*x2 + b12*x1*x2

Lin/quad main eff. + 2-ways.

y = b0 + b1*x1 + b2*x2 + b11*x1*x1 + b22*x2*x2 + b12*x1*x2

For additional information about response surface designs, refer also to the discussion of central composite designs in the Introductory Overview.