Overview of the Fit Two Level 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 Fit Two Level Screening (Analyze > Specialized Modeling > Specialized DOE Models > Fit Two Level Screening). Use the Fit Two Level Screening platform to analyze data from screening experiments In accordance with the following ...
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