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Title

Introduction of Multivariate Data Analyst
Author

Ken Kikuchi
Organization: Kumamoto University Hospital
Department: Medical Information Technology & Administration Planning
Conference

2004 International Mathematica Symposium
Conference location

Banff, Canada
Description

Multivariate Data Analyst is an on-going private project for the purpose of providing a set of Multivariate Data Analysis tools in education for advanced statistics users and users in business. Multivariate analysis is an area of statistics concerned with methods of analyzing data that contains more than one variable. This is particularly useful when we need to seek hidden structures in the complicated data, or need to extract invisible characteristics from the data. This package expands the analysis capability over the standard statistics package.

This paper demonstrates some of the following currently available functions. Principal Component Analysis (Example 1) Discriminant Analysis Multiple Regression Cluster Analysis (Example 2) Hayashi's Quantification Theory I Hayashi's Quantification Theory II Hayashi's Quantification Theory III Hayashi's Quantification Theory IV (Example 3)
Subjects

*Education > College
*Mathematics > Probability and Statistics
Keywords

multivariate data analysis, statistics, multivariate analysis, Principal Component Analysis, Discriminant Analysis, Multiple Regression, Cluster Analysis, Hayashi's Quantification, Hayashi's Quantification, Hayashi's Quantification, Hayashi's Quantification
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*New Ideas in Symbolic Computation: Proceedings of the 6th International Mathematica Symposium   [in Books]