SAS Institute Japan Company Profile

JMP is 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 minimizing time and costs. In this seminar, we will focus on the features of experimental design, specifically the sample size explorer and Bayesian optimization, and demonstrate examples of their application in the manufacturing industry. 【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 present analysis examples using JMP. Additionally, we will introduce a method to simulate the relationship between the required data numbers for t-tests and statistical power using the sample size explorer. Second half: How to utilize JMP Pro Bayesian optimization – Application to simulation experimental data The Bayesian optimization platform is originally designed for experimental data that includes measurement errors, but there is also a strong demand to apply it to simulation experimental data that does not contain errors, such as CAE. In this seminar, we will introduce approaches for utilizing Bayesian optimization with simulation experimental data.

"Data processing takes time," "I keep repeating the same tasks," "Creating graphs is surprisingly difficult." Do you have such concerns? As the importance of data utilization increases, a lot of time is spent on tasks such as data processing and graph creation. Especially when working with spreadsheet software, mistakes due to repetitive manual tasks are often a challenge. JMP not only offers advanced statistical analysis but also has a wealth of features for data preprocessing and visualization. In this seminar, we will introduce methods to streamline data operations from loading and processing to graph creation and automation. 【Target Audience】 Anyone, regardless of industry, who regularly processes data or creates graphs using spreadsheet software. Those who feel burdened by the effort of creating monthly or weekly reports and graphs. Individuals who want to streamline data preparation tasks and spend more time on analysis and reporting. Those who want to learn how to utilize JMP not only for analysis but also for data preprocessing and visualization. ▼Details & Registration https://www.jmp.com/ja/events/live-webinars/non-series/2026/10-08-data-processing

In the health and medical field, which is directly connected to people's lives, it is essential to carefully interpret the results of data analysis and apply them to decision-making. This seminar will first introduce basic analytical methods using JMP 19 and explain the scenarios in which each method can be utilized. Next, we will discuss the thought process for causal interpretations, such as "Did a certain treatment improve the condition?" and how to estimate causal effects using JMP Pro 19. - Comparison of results before and after treatment using paired t-tests - Group comparisons using t-tests - Group comparisons considering pre-treatment values through analysis of covariance - Distinguishing between correlation and causation - Estimation of causal treatment effects This seminar will present these fundamental analytical methods with demonstrations. Those without specialized statistical knowledge and those unfamiliar with JMP are also very welcome. ▼ Details & Registration https://www.jmp.com/ja/events/live-webinars/non-series/2026/09-11-health-medical-data