Developer Tutorial: Handling Covariates Effectively when Designing Experiments
Design of Experiments
This session is for JMP users who understand basic DOE principles and have used JMP to design experiments.
In a designed experiment, a covariate is an input variable that we want to account for in our experiment but which we cannot control to be any value in the way we can for other types of variables. However, if we can measure the values of such inputs ahead of time, we can account for them when designing the experiment.
Prior to JMP 16, JMP focused only on D-optimality for the covariates. Beginning with JMP 16, much of the work for handling covariates occurs behind the scenes. The covariate rows are chosen according to the specified optimality criteria and the integration between covariates and controllable factors have been enhanced to search through more possibilities.
In this session, you will understand the rationale and techniques behind JMP’s covariate handling, see when and how to account for covariates and learn how to integrate covariate inputs with controllable factors when designing experiments.
This JMP Developer Tutorial covers: Specifying and loading covariate factors; integrating controllable factors into the design; determining when to use all rows or a subset of rows; generating the design; interpreting design results.