In the Fit Least Squares report, the Effect Screening options (accessed from the Response red triangle menu) are useful when classical tests for effects are not available. This happens with screening designs, which often provide no degrees of freedom for error.
For these designs, most inferences about effect sizes assume that the estimates for non-intercept parameters are uncorrelated and have equal variances. These assumptions hold for the models associated with many classical experimental designs. However, there are situations where these assumptions do not hold. In both of these situations, the Effect Screening option guides you in determining which effects are significant.
The Effect Screening option uses the principle of effect sparsity (Box and Meyer 1986). This principle asserts that relatively few of the effects that you study in a screening design are active. Most are inactive, meaning that their true effects are zero and that their estimates are random error.
The following Effect Screening options are available:
Scaled Estimates
Gives parameter estimates corresponding to factors that are scaled to have a mean of zero and a range of two. See Scaled Estimates and the Coding of Continuous Terms.
Normal Plot
Identifies parameter estimates that deviate from normality, helping you determine which effects are active. See Normal Plot Report.
Bayes Plot
Computes posterior probabilities for all model terms using a Bayesian approach. See Bayes Plot Report.
Pareto Plot
Plots the absolute values of the orthogonalized and standardized parameter estimates, relating these to the sum of their absolute values. See Pareto Plot Report.