Baltimore October 19-22

Choosing a statistical model can feel overwhelming, especially when multiple options appear to “work.” This workshop provides a practical framework for selecting models based on the structure of your data, the question you’re trying to answer, and the...

This workshop introduces a graphical tool for designing studies in JMP that helps you build complex models visually. We review concepts such as what a model is, why crossing and nesting matter, and when fixed versus random effects are appropriate. Thr...

Every data set tells a story, and effective visualization helps bring that story to life. This hands-on workshop covers practical principles for effective data visualization, with examples drawn from common scientific and engineering scenarios. Partic...

Many analysts spend more time than necessary repeating common tasks or navigating the interface instead of focusing on insights. This workshop highlights practical ways to streamline your day‑to‑day work by customizing JMP to fit how you analyze data....

Choosing sample size is ultimately a risk management decision, but many practitioners rely on guesswork or rules of thumb. This workshop demystifies sample size and power by breaking calculations into their core components: uncertainty, effect size, r...

Not all experimental challenges call for the same approach. Some benefit from broad exploration, others from careful refinement, and some from a mix of both. In this hands‑on workshop, participants learn how to choose experimental strategies that fit ...