Speaker: Ding Jianfen , Senior Statistical R&D Engineer, JMP US R&D Headquarters
the speech topic: Application of JMP® multivariate analysis in chemical, daily necessities and food companies
Summary:
The products of any enterprise will go to the consumer market. Compared with other enterprises, the relationship between chemical, daily necessities and food enterprises and consumers is more direct and closely related. In order to make their products gain a foothold in the market and win the favor of consumers, these companies will conduct a lot of research on consumer preferences, including many world-renowned companies. These studies often generate large amounts of sensory-related data. These sensory data with color, fragrance and taste are not only abundant but also of various types. In order to better explore and analyze such data, JMP has increased the research and development of effective multivariate data analysis methods for such data year by year. In this discussion, we will introduce how to apply analysis of variance (ANOVA), K-means clustering, principal component analysis (PCA), partial least squares (PLS), multivariate analysis (MFA), multiple correspondence analysis (MCA) ) and text analyzers to analyze sensory and consumer preference data, emphasizing how these methods work, how each method should be interpreted, and their inherent interrelationships. By elaborating on the best ways to address sensory and consumer preference issues, it is hoped that this discussion will provide analysts with an understanding of multivariate approaches to this type of data, so that each analyst can face the sensory and consumer preferences encountered in the future. When consumers research data, they can effectively use these multivariate tools provided by JMP for analysis.
This post originally written in Chinese (Simplified) and has been translated for your convenience. When you reply, it will also be translated back to Chinese (Simplified).