Level: Beginner
【Presenter】
Tatsuro Yamamoto, Gore Japan Statistics
[Presentation overview]
In this announcement. We will introduce the "log-linear variance model" that can be used in JMP model fitting. Equal variances are normally assumed when performing regression analysis or analysis of variance (general linear model) in JMP model fitting. However, the data we actually deal with often deviate from this assumption. The log-linear variance model is a very useful method in such cases. As a countermeasure against unequal variance, some countermeasures such as variable transformation are used. On the other hand, JMP's log-linear variance model analyzes the data as it is, analyzes (models) how the factors affect the mean and variance, and simply uses the results to predict the profile. You can grasp it. We will also introduce some examples of use at our company.
[Profile of the presenter]
In March 1994, he graduated from Nagoya Institute of Technology Graduate School of Chemical Engineering, and got a job at a chemical manufacturer. Until I changed jobs, I specialized in organic synthesis. After moving to Gore Japan LLC, he committed himself to statistics and has been involved in statistical education and consultation for over 20 years. Shortly after I started committing to this work, I discovered JMP and was convinced that there was no better statistical software for engineers to use. Since then, we have promoted the spread of JMP as a statistical tool for internal use. He has a strong interest in practical statistics, and continues to think about how to utilize statistics using JMP in a wide range of fields from research and development to production technology.
