授業の目的 【日本語】
Goals of the Course(JPN) | | | Substantial difficulties of dynamic systems in the real world 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 the methods of multivariate analysis on real data and interpret the results throughout the course. |
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授業の目的 【英語】
Goals of the Course | | | Substantial difficulties of dynamic systems in the real world 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 the methods of multivariate analysis on real data and interpret the results throughout the course. |
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到達目標 【日本語】
Objectives of the Course(JPN)) | | | Substantial difficulties of dynamic systems in the real world 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 the methods of multivariate analysis on real data and interpret the results throughout the course. |
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到達目標 【英語】
Objectives of the Course | | | Substantial difficulties of dynamic systems in the real world 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 the methods of multivariate analysis on real data and interpret the results throughout the course. |
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バックグラウンドとなる科目【日本語】
Prerequisite Subjects | | | Mathematics, especially, linear algebra and statistics of undergraduate level. |
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バックグラウンドとなる科目【英語】
Prerequisite Subjects | | | Mathematics, especially, linear algebra and statistics of undergraduate level. |
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授業の内容【日本語】
Course Content | | 1-2 h: Multivariate regression analysis
3 h: Outlier analysis
4-5 h: Principal component analysis
6 h: Factor analysis
7-8 h: Discrimination analysis
9-10 h: Structural equation modeling
11 h: Covariance selection
12 h: Time-series analysis
13 h: Preparation of final presentation
14 h: Youtube presentation and marking by all students
15 h: Honorable presentations by selected speakers |
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授業の内容【英語】
Course Content | | 1-2 h: Multivariate regression analysis
3 h: Outlier analysis
4-5 h: Principal component analysis
6 h: Factor analysis
7-8 h: Discrimination analysis
9-10 h: Structural equation modeling
11 h: Covariance selection
12 h: Time-series analysis
13 h: Preparation of final presentation
14 h: Youtube presentation and marking by all students
15 h: Honorable presentations by selected speakers |
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成績評価の方法と基準【日本語】
Course Evaluation Method and Criteria | | | Three reports (60%) and one presentation (40%) are collectively evaluated. All students have to prepare for the final presentation, for which real world data are examined with one of the analysis methods. |
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成績評価の方法と基準【英語】
Course Evaluation Method and Criteria | | | Three reports (60%) and one presentation (40%) are collectively evaluated. All or selected students have to prepare for the final presentation, for which real world data are examined with one of the analysis methods. |
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履修条件・注意事項【日本語】
Course Prerequisites / Notes | | | The lectures will be delivered on Youtube. The URLs will be announced every week by e-mails registered in NUCT. Final presentations will be held by Microsoft Teams. |
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履修条件・注意事項【英語】
Course Prerequisites / Notes | | | The lectures will be delivered on Youtube. The URLs will be announced every week by e-mails registered in NUCT. Final presentations will be held by Microsoft Teams. |
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教科書【日本語】
Textbook | | Available on the course website:
http://www.mech.nagoya-u.ac.jp/asi/ja/lecture/okamoto_system.html |
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教科書【英語】
Textbook | | Available on the course website:
http://www.mech.nagoya-u.ac.jp/asi/ja/lecture/okamoto_system.html |
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参考書【日本語】
Reference Book | | |
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参考書【英語】
Reference Book | | |
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授業時間外学習の指示【日本語】
Self-directed Learning Outside Course Hours | | |
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授業時間外学習の指示【英語】
Self-directed Learning Outside Course Hours | | |
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使用言語【英語】
Language used | | |
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使用言語【日本語】
Language used | | |
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授業開講形態等【日本語】
Lecture format, etc. | | |
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授業開講形態等【英語】
Lecture format, etc. | | |
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遠隔授業（オンデマンド型）で行う場合の追加措置【日本語】
Additional measures for remote class (on-demand class) | | |
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遠隔授業（オンデマンド型）で行う場合の追加措置【英語】
Additional measures for remote class (on-demand class) | | |
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