Level: Intermediate
Compared to R, Python, etc., JMP (Pro) allows you to enjoy analysis more easily. While it is not a fully custom-made analysis, it is fully capable of handling semi-customized analysis. With JMP, you can easily perform the following tasks:
1) Analysis can be performed with just a mouse, without typing commands; 2) graphs and statistics can be combined; 3) the analysis process can be recorded in a script; 4) reports can be output along with the flow of the analysis process; and 5) since it is based on statistical thinking, it is ideal for systematic understanding and learning. This report will use numerical examples to discuss prediction and classification possible with JMP. Methods used include kernel smoothing, SVM, and neuro-discrimination. A comparison with traditional statistical multivariate analysis will also be made to deepen understanding.
Joined Ricoh Co., Ltd. in 1984. Since then, he has been involved in in-house quality management and reliability management, as well as promoting and disseminating statistics. He served as head of the QM Promotion Office in the Quality Headquarters and director of the SF Business Center before assuming his current position. He was a lecturer at the Faculty of Engineering, Tokyo University of Science (1997-1998) and a part-time lecturer at the Faculty of Policy Management, Keio University (2000-2004). His main areas of expertise are statistical quality control and reliability engineering. His major publications include "Graphical Modeling in Practice," "How to Utilize Multivariate Data with JMP," "23 Lectures on Statistical Calculation Methods with Anscombe-Style Numerical Examples," "Multivariate Analysis for Engineers Using JMP," and "Eye-Opening Multivariate Analysis."
*No handouts will be provided.
