| 授業方針・テーマ |
Real-world systems often exhibit substantial complexity because they involve numerous interrelated factors that vary statistically. Multivariate analysis and statistical modeling are essential tools for understanding and representing such intricate systems. This course is designed for students who have had limited prior exposure to statistics or the mathematical foundations underlying these methods. We will cover intermediate topics in classical multivariate analysis and related statistical techniques. Throughout the course, students will also practice applying these analytical methods to real datasets and learn how to interpret the results appropriately. |
習得できる知識・能力や授業の
目的・到達目標 |
Mathematical foundations relevant to statistical and multivariate analysis
General techniques for handling and preprocessing multivariate data
Practical implementation of multivariate analysis methods using Python or MATLAB
Presentation and communication skills for reporting statistical results |
授業計画・内容
授業方法 |
1. Introduction & Multiple Regression Analysis I: Model equations and analytical solutions
2. Multiple Regression Analysis II: Model evaluation and diagnostics
3. Multiple Regression Analysis III: Variable selection methods
4. Multiple Regression Analysis IV: Logistic regression and categorical predictors
5. Multiple Regression Analysis V: Regularization methods (Ridge and Lasso)
6. Outlier detection and influence analysis
7. Group Work: Data analysis project based on multiple regression
8. Group Work (continued): Analysis refinement and interim interviews
9. Group Work (continued)
10. Parameter Estimation Methods I: Maximum likelihood estimation
11. Parameter Estimation Methods II: Bayesian estimation
12. Presentations and discussion
13. Presentations and discussion
14. Presentations and discussion
15. Presentations and discussion |
| 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 a report and 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