| 授業方針・テーマ |
Substantial difficulties of real-world systems lie in the involvement of a large number of related factors that deviate statistically. Multivariate analyses and statistics are common tools for understanding and modeling these intricate systems. This course is arranged for those who had few opportunities to study statistics, multivariate analyses, and some basis for these mathematics. We learn intermediate topics of classic multivariate analyses and related statistics. We also practice how to apply each method of multivariate analysis on real data and interpret the results throughout the course. |
習得できる知識・能力や授業の
目的・到達目標 |
Statistical mathematics for multivariate analyses.
General treatment of multivariate data.
Implementation of multivariate analyses by using Python.
Presentation skills for statistics. |
授業計画・内容
授業方法 |
1. Introduction & Multiple regression analysis 1
2. Multiple regression analysis 2
3. Multiple regression analysis 3
3. Outlier analysis
* First homework on multiple regression analysis
4. Principal component analysis 1
5. Principal component analysis 2
6. Factor analysis
* Second homework on PCA or FA
7. Discriminat analysis
8. Structural equation modeling 1
9. Structural equation modeling 2
* Third homework on SEM
10. Covariance selection 1
11. Covariance selection 2
12. Preparation for the final presentation
13. Online presentation by all students
14. Online presentation by all students
15. Final presentation by selected students |
| 授業外学習 |
Homework for self-study are provided every week during the class. |
| テキスト・参考書等 |
Course materials are provided on kibaco. No special recommendation of text books. |
| 成績評価方法 |
Based on 3 reports and one final presentation including the quality and quantity of questions and answers among the students. |
質問受付方法
(オフィスアワー等) |
Appointment by e-mail. |
特記事項
(他の授業科目との関連性) |
Especially nothing. |
| 備考 |
|
シラバス照会