Feb 16, 2018 9:20 AM
| Last Modified: Mar 2, 2018 1:14 PM
Level: Intermediate Ryan Lekivetz, JMP Senior Research Statistician Developer, SAS Bradley Jones, JMP Distinguished Research Fellow, SAS
JMP 14 has several new DOE features. Perhaps the most important is a new design tool for creating balanced incomplete block designs (BIBDs). These designs have one treatment factor and one blocking factor. Each treatment level appears the same number of times overall, and each treatment level appears together with every other treatment level in some block the same number of times. This creates a special kind of balance. We will provide more details about this design and demonstrate the UI of the new tool. The Custom Designer has added a pair of optimality criteria for generating A-optimal designs and weighted A-optimal designs. An A-optimal design minimizes the average variance of the parameter estimates. The A-criterion is similar to the well-known D-criterion in that it focuses on the precise estimation of model parameters. There are two benefits to A-optimal designs. First, it is easier to understand how the A-criterion produces good designs than it is with the D-criterion, making it easier for new DOE practitioners to adopt. Second, weighted A-optimal designs allow for putting different amounts of emphasis on different groups of parameters. For example, one might want to emphasize pure quadratic effects over two-factor interactions and both of these over main effect estimation. The Compare Designs tool can now accommodate up to five designs in the UI and up to 10 designs through scripting. Previously, the limit was three designs. We will demonstrate this additional capability with a UI example and a scripting example that includes the new A-optimal and weighted A-optimal designs.