Overview of the Screening Platform

The analysis of screening designs depends on effect sparsity, where most effects are assumed to be inactive. Using this assumption, effects with small estimates can help estimate the error in the model and determine whether the larger effects are active. Basically, if all the effects are inactive, they should vary randomly, with no effect deviating substantially from the other effects.

Data from a screening experiment can be analyzed using Fit Model (Analyze > Fit Model) or Screening (Analyze > Modeling > Screening). Use the Screening platform to analyze data from screening experiments In accordance with the following guidelines:

•	If your factors are all two-level and orthogonal, then all of the statistics in the Screening platform are appropriate.

•	For highly supersaturated main effect designs, the Screening platform is effective in selecting factors, but is not as effective at estimating the error or the significance. The Monte Carlo simulation to produce p-values uses assumptions that are not valid for this case.

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If you have a categorical or a discrete numeric factor with more than two levels, then the Screening platform is not an appropriate way to analyze the data. JMP treats the associated model terms as continuous. The variation for the factor is scattered across main and polynomial effects for that term.

•	If your data are not orthogonal, then the constructed estimates are different from standard regression estimates. JMP can pick out big effects, but it does not effectively test each effect. This is because later effects are artificially orthogonalized, making earlier effects look more significant.

•	The Screening platform is not appropriate for mixture designs.