Lost in JMP? Slimming Down for a Better Fit (2026-EU-PO-2802)

JMP is a well, is well known for its ease of use. This is one reason that JMP Student Edition is used at universities around the world, often deployed off the shelf. With my co-presenter, Co Repkemer, who has extensive experience in teaching, we want to explore how easy JMP out of the box is for students, our baseline. Options for further improvement of this learning experience include customization, especially for onboarding new users, packaging for sharing and record usability, and extensions, taking users to the next level. We also like to invite everyone to discuss with us the need and best practices, making life easier for both instructors and students. This poster summarizes more detailed material shared in a recent academic webinar, see the reference on the left in the lower corner. Okay, thanks, Volker. At the Eindhoven University of Technology in the Netherlands, I organize a data analysis course program for PhD students and graduated engineers. It consists of a refresher course on applied data analysis and three short follow-up courses, one on design of experiments, one on time series analysis, and one on predictive modeling. JMP is introduced in these courses to get our engineers and researchers familiar with both state-of-the-art analysis techniques and the importance of a documented, reproducible approach that can be shared with their colleagues. As first-time users of JMP might be overwhelmed by the large set of analysis techniques and options provided in JMP, we tailor the JMP environment, taking level of the course, the JMP role, and key tools used in the course into account, as summarized in the schedule at the bottom right. Starting with an off-the-shelf JMP Student Edition, a standard workflow can be followed using the user-friendly JMP graphical user interface. In the example indicated, very simple, of fitting a linear regression model, the Fit Y by X platform or the Fit Model platform would be adequate, offering a wealth of additional red triangle options and even analysis presets that combine tasks such as exploratory data analysis, model fit, and model evaluation. However, tailoring the JMP environment can improve the user experience and keep them focused on the data analysis tasks they are performing and on the inter-interpretation of the results. My colleague Volker will summarize some directions to tailor the JMP environment in an adequate way. Thanks, Co. Customizing the JMP user interface can provide easier access to the relevant JMP tools, especially for new users. We will discuss several options. My two favorites are saved scripts bundled with the data table and also platform presets, which reduce the need for red triangle options. No course needs all functionality. If you want to focus on the relevant capabilities, you can also tailor the JMP menus and toolbars. Besides removing menu items, you can also add custom menus like Modern Six Sigma. The next direction, helpful for new and intermediate users, addresses the packaging of course content, sharing datasets, analysis workflows and results, and supporting reproducibility, documentation, and communication. Options include JMP journals and projects, as well as JMP workflows and the new notebooks. Finally, we want to suggest leveraging external extensions to JMP software, either resources from the JMP user community or add-ins from the JMP marketplace. While several add-ins add convenience for the instructor or student, we want to follow the white paper from Russ Wolfinger and shed, shed some light on AI-based tools like the LearnBot, which can, used wisely, work like an embedded tutor or co-pilot and help taking students to the next level with less or even no guidance by the instructor.