Advanced Experimental Design Using JMP (2022-KR-30MP-1341)Yonghanju, Sunmoon University
Advances in big data, artificial intelligence, and data science have greatly contributed to data-based decision-making activities. In industries where various data can be collected or in the mass production stage of products, the acquired data can be processed in various forms, and data analysis activities can be conducted by developing or applying an appropriate analysis model. However, in the R&D stage of developing new technology or process conditions, it is somewhat difficult to obtain various types of information due to limitations in time and resources. In this environment, based on JMP, we will learn about design of experiments, a data analysis technique that can obtain various experimental results based on a small number of data. In this article, along with a brief introduction to the principles of design of experiments, various statistics that can be obtained through JMP's experimental design are introduced. Based on the experimental variables, levels, and response variables obtained from the planned experimental design conditions, a significance test is carried out, along with an explanation of the estimation of the confidence interval and the parameters and random factors. In particular, we would like to suggest how to use the JMP experimental design method for custom design considering the constraints of the experimental environment along with the generally widely used full factorial experimental method, partial factorial experimental method, and response surface analysis method. Through this content, it is expected that the experimental design method will be used in the right place in the actual industry.Breakout
