Austin, TX October 21-24

Performing experiments correctly isn’t always straightforward, especially when you have a factor that’s difficult to change or reset between runs – a common challenge in industrial settings. If you don’t account for this restriction in your design and analysis, your results can be misleading. In this workshop, learn how to handle this real-world issue using JMP Custom Design. Through a practical case study with a hard-to-change factor, we show you how to design a split-plot experiment and analyze the results using restricted maximum likelihood estimation (REML). This session is ideal for intermediate to advanced JMP users who are already familiar with ANOVA, regression, and the basics of DOE.