Multivariate Analysis
TeachersKOBAYASHI, YasuyukiStaffInfo
Grade, SemesterYear 1 1st semest [Master's program, Division of Integrated Science and Engineering]
CategoryGeneral Engineer Subjects
Elective, CreditsElective 2credit
 Syllabus Number

Course Description

First you will review basic statistic technique. Second you will study multivariate techniques such as regression analysis, principal component analysis, discriminant analysis, cluster analysis, etc. You will understand the theories to interpret the analysis results. You will also obtain the ability to apply the multivariate analysis technique through case studies and exercises using computer software.This subject corresponds to the diploma policy DP2.

Course Objectives

As multivariate is widely applied to various fields including science and technology, you will be able to understand basic techniques of statistic and multivariate, and will be able to apply them to practical problem solving session.

Grading Policy

Your overall grade in this class will be decided based on the following: - Score of the presentation result of the practical exercise using computers for 50%,- Score of the submitted report for 50%. However, if you are not eager to attending the lecture or the exercise, you will not be given the credit.

Textbook and Reference

KindTitleAuthorPublisher
Textbook"Zukai to suuti-rei de manabu tahenryoukaiseki-nyuumon", ISBN: 978-4-542-60112-3.NoguchiJapanese Standard Association
TextbookThe lecture materials will be posted on LMS.
ReferencesIntroduction to Multivariate Analysis, ISBN: 978-4781909806.Nagata and MunechikaScience-sha
ReferencesYou can find various books dealing with multivariate analysis for your personal purpose.
ReferencesWe will use Microsoft Excel for computer exercise so that you can use it after this lecture.

Requirements(Assignments)

It is desirable that you understand mathematical statistics and linear algebra of undergraduates. You should review the exercises for each technique of multivariate analysis and apply them in your own problems.

Note

In addition to lectures, you will take computer exercises almost every class to understand the lecture well. The contents of the lectures and exercises may be changed appropriately according to students' understanding level and students' own research interest. This class will be taught in Japanese. However, if necessary, English will also be available.

Schedule

1Introduction and basic statistical technique (1): how to summarize in statistics
2Basic statistical technique (2): correlation analysis
3Basic statistical technique (3): foundation of probability distribution and normal distribution
4Basic statistical technique (4): inference and test
5Basic statistical technique (5): how to select statistical models
6Regression analysis(1): single regression analysis and formulation
7Regression analysis(2): important points for single regression analysis
8Regression analysis(3): multiple regression analysis and formulation
9Regression analysis(4): important points for multiple regression analysis and variable selection method
10Regression analysis(5): how to handle qualitative variables and linear algebra essential for multivariate analysis
11Regression analysis(6): logistic regression
12Cluster analysis: hierarchical method and nonhierarchical method
13The other methods: principal component analysis, discriminant analysis, etc
14Practical exercise using computers
15Presentation of the practical exercise using computers