JMP Pro

This seminar is aimed at engineers primarily in the manufacturing industry, focusing on data utilization, and will explain practical approaches to factor exploration and predictive model building. 1. What to do before the model: Key points for data preparation and trial & error using JMP We will introduce the necessary preprocessing and feature creation required before analysis. - Understanding the contents of the data (visualization and summary statistics) - Dealing with missing values and outliers - Creating variables to enhance model accuracy (feature engineering) - Tips for efficiently progressing the creation of analysis data 2. How to proceed with factor exploration and predictive model building without making it a black box Based on the data prepared in the first half, we will explain the process of factor exploration and predictive model building. - Visualization of factors using correlation analysis, regression models, and decision trees - Model evaluation using training and validation data - Comparison and selection of multiple machine learning models (e.g., neural networks, random forests, etc.) - Intuitive understanding of important factors and prediction results ▼ Details & Registration https://www.jmp.com/ja/events/seminars/non-series/2026/06-19-factor

We will introduce the features and applications of "Bayesian Optimization." In product design and development, it is essential to efficiently find optimal conditions within a limited number of trials. Design of Experiments (DOE) is one effective method, but it requires conducting multiple experiments in advance, making it challenging to explore optimal conditions while minimizing the number of experiments, especially in cases with complex nonlinear relationships. In this seminar, we will explain the basic concepts and advantages of "Bayesian Optimization," which allows us to approach optimal conditions with fewer experiments. Furthermore, we will introduce a method that uses "Bayesian Optimization" to sequentially propose the next experimental conditions to test, considering multiple characteristics and constraints while utilizing profiles, thus advancing optimization efficiently with concrete examples. Main content: - What can be done with Design of Experiments (DOE) and its limitations - Overview of Bayesian Optimization and its benefits - Features of "Bayesian Optimization" in JMP Pro ▼ Details & Registration https://www.jmp.com/ja/events/live-webinars/non-series/2026/05-28-pro-bayes

We will introduce the approach of "Design of Experiments (DOE)" that is being increasingly utilized in the research and development field of chemical manufacturers. We will explain methods to address complex experimental challenges, such as formulation planning, factor screening, and analysis of response variations, along with specific case studies. Additionally, we will discuss how to utilize statistical methods not only for improving the efficiency of experiments but also for gaining deeper insights. What you will learn in this seminar: - How to advance formulation optimization using custom plans and formulation plans - Efficient identification of important factors using definitive screening designs - Applications of DOE targeting functional data such as time series and spectra - Experimental planning and data analysis methods using JMP Program: - Optimization plans and formulation plans - Connecting screening and optimization – Practical application of definitive screening designs - Exploring experimental plans for "response variations": Analyzing the impact of formulation component ratios on response changes ▼ Details & Registration https://www.jmp.com/ja/events/live-webinars/non-series/2026/05-19-chem-innovation