For this example, you have constructed a definitive screening design to determine which of six factors have an effect on the yield of an extraction process. The data are given in the Extraction sample data table, located in the Design Experiment folder. Because the design is a definitive screening design, each factor has three levels. See the Definitive Screening Designs topic.
Although the experiment studies six factors, effect sparsity suggests that only a small subset of factors is active. Consequently, you feel comfortable investigating power in a model based on a smaller number of factors. Also, past studies on a related process provide strong evidence to suggest that three of the factors, Propanol, Butanol, and pH, have negligible main effects, do not interact with other factors, and do not have quadratic effects. This leads you to believe that the likely model contains main, interaction, and quadratic effects only for Methanol, Ethanol, and Time. You decide to investigate power in the context of a three-factor response surface model.
Select Help > Sample Data Library and open Design  Experiment/Extraction
Select DOE > Design Diagnostics > Evaluate Design.
Select Methanol, Ethanol, and Time and click X, Factor.
You can add Yield as Y, Response if you wish. But specifying the response has no effect on the properties of the design.
Under Anticipated Coefficient, type 3 next to Methanol*Methanol, Ethanol*Ethanol, and Time*Time.
Click Apply Changes to Anticipated Coefficients.
Power Analysis Outline after Applying Changes to Coefficients

Help created on 9/19/2017