Screening designs are often used to test a large number of factors or interactions. When there are degrees of freedom for error, allowing construction of an error estimate, the experimental results can be analyzed using the usual regression techniques (Analyze > Fit Model). You can use normal plots, Bayes Plots (also known as Box-Meyer Bayes Plots), and Pareto plots to identify factors of interest. See “Effect Screening Plot Options” in Fitting Linear Models.
However, sometimes there are no degrees of freedom for error. In this case, assuming effect sparsity, the Screening platform (DOE > Classical > Factor Screening > Fit Two Level Screening) provides a way to analyze the results of a two-level design. The Screening platform accepts multiple responses and multiple factors. It automatically shows significant effects with plots and statistics. See Screening Designs. For examples in the current chapter, see Example of Modifying Generating Rules in a Fractional Factorial Design and Example of a Plackett-Burman Design.