The Make Model button beneath the Half Normal Plot creates a Fit Model dialog that includes all the highlighted effects.
1.
Open the Plackett-Burman.jmp sample data table, found in Design Experiment folder.
2.
Select Analyze > Specialized Modeling > Specialized DOE Models > Fit Two Level Screening.
3.
4.
Click the Make Model Button beneath the Half Normal Plot.
Note that the Catalyst*Stir Rate interaction is highlighted, but the Stir Rate main effect is not. In accordance with the principle of Effect Heredity, add the Stir Rate main effect to the model. See Effect Heredity in Starting Out with DOE.
5.
Select Stir Rate and click Add in the Fit Model window.
6.
Click Run.
The Whole Model actual-by-predicted plot, shown in An Actual-by-Predicted Plot, appears at the top of the Fit Model report. You see at a glance that this model fits well. The blue line falls outside the bounds of the 95% confidence curves (red-dotted lines), which tells you the model is significant. The model p-value (p = 0.0208), R2, and RMSE appear below the plot. The RMSE is an estimate of the standard deviation, assuming that the unestimated effects are negligible.
An Actual-by-Predicted Plot
To see a scaled estimates report, use Effect Screening > Scaled Estimates found in the red triangle menu on the Response Percent Reacted title bar. When there are quadratic or polynomial effects, the coefficients and the tests for them are more meaningful if effects are scaled and coded. The Scaled Estimates report includes a bar chart of the individual effects embedded in a table of parameter estimates. The last column of the table has the p-values for each effect.
Example of a Scaled Estimates Report
Open the Effect Details disclosure icon to see outline nodes for the Catalyst and Temperature effects. To run a power analysis for an effect, click the red triangle icon on its title bar and select Power Analysis.
This example shows a power analysis for the Catalyst variable, using default value for α (0.05), the root mean square error and parameter estimate for Catalyst, for a sample size of 12. The resulting power is 0.8926, which means that in similar experiments, you can expect an 89% chance of detecting a significant effect for Catalyst.
Example of a Power Analysis
Refer to Power Analysis in the Fitting Linear Models book for details.

Help created on 9/19/2017