The development of big data, artificial intelligence, and data science has contributed greatly 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 appropriate analysis models. However, in the research and development stage where new technologies or process conditions are developed, it is somewhat difficult to obtain various information data due to time and resource limitations. In this environment, we will learn about the design of experiments, a data analysis technique that can obtain various experimental results based on a small amount of data, based on JMP. In this content, we will briefly introduce the principles of the design of experiments and introduce various statistics that can be obtained through the experimental design of JMP. Based on the experimental variables, levels, and response variables obtained from the planned experimental design conditions, we will proceed with the estimation of confidence intervals along with significance tests, and an explanation of parameters and variance factors. In particular, JMP experimental design method is intended to suggest the usage of custom design that considers the constraints of experimental environment along with the commonly used full factorial experiment method, partial factorial experiment method, and response surface analysis method. Through this content, it is expected that experimental design method will be utilized appropriately in actual industrial groups.
