Utilization of the experimental design features of the statistical analysis software JMP in the manufacturing industry.
JMP is a software that not only excels in statistical analysis and graph creation but also has robust features for experimental design. By utilizing experimental design, efficient data collection can be achieved while keeping time and costs down. In this seminar, we will focus on the features of experimental design, specifically the sample size explorer and Bayesian optimization, and present examples of their application in the manufacturing industry in a demonstration format.
[Overview]
First half: Utilization of t-tests in the manufacturing industry and simulation of required data numbers
We will explain the overview and examples of t-tests, which are used for various purposes in the manufacturing industry, and introduce analysis examples using JMP. Additionally, we will demonstrate how to simulate the relationship between the required data numbers for t-tests and statistical power using the sample size explorer.
Second half: Utilizing JMP Pro Bayesian optimization - Application to simulation experimental data
The Bayesian optimization platform is originally designed for experimental data that includes measurement errors; however, there is also a significant demand to apply it to simulation experimental data that does not include errors, such as CAE. In this seminar, we will introduce approaches for utilizing Bayesian optimization with simulation experimental data.

| Date and time | Friday, Oct 09, 2026 02:00 PM ~ 04:00 PM Online seminar using Zoom |
|---|---|
| Entry fee | Free *Advance registration is required: https://www.jmp.com/ja/events/live-webinars/discovery-seminar-series/2026/10-09 |
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