Design & Analysis of Screening Experiments

Select the 2-level screening (Plackett-Burman) designs option from the Advanced tab of the Design and Analysis of Experiments Startup Panel to display the Design & Analysis of Screening Experiments dialog.  This dialog contains two tabs: Design experiment and Analyze design.

OK

Click the OK button to display the Design of a Screening (Plackett-Burman) Experiment dialog if you are generating a design or to display the Analysis of a Screening Experiment with Two-Level Factors dialog if you are analyzing a design.  

Cancel

Click the Cancel button to close the dialog without performing any analysis and return to the Startup Panel.

Options

Click the Options button to display the Options menu.

Select Cases

Click the Select Cases button to display the Analysis/Graph Case Selection Conditions dialog, which is used to create conditions for which cases will be included (or excluded) in the analysis. More information is available in the case selection conditions' overview, syntax summary, and dialog description.

W

Click the W (Weight) button to display the Analysis/Graph Case Weights dialog, which is used to adjust the contribution of individual cases to the outcome of an analysis by "weighting" those cases in proportion to the values of a selected variable.

Note: Designs. When you need to screen a large number of factors to identify those that may be important (i.e., those that are related to the dependent variable of interest), you need to employ a design that allows you to test the largest number of factor main effects with the least number of observations, that is to construct a resolution III design with as few runs as possible. One way to design such experiments is to confound all interactions with "new" main effects. Such designs are also sometimes called saturated designs, because all information in those designs is used to estimate the parameters, leaving no degrees of freedom to estimate the error term for the ANOVA. Because the added factors are created by equating (aliasing), the "new" factors with the interactions of a full factorial design, these designs always will have 2k runs (e.g., 4, 8, 16, 32, and so on).

Plackett and Burman (1946) showed how full factorial designs can be fractionalized in a different manner, to yield saturated designs where the number of runs is a multiple of 4, rather than a power of 2. These designs are also sometimes call Hadamard matrix designs.