Lecture / 講義

Adv. lecture on System dynamics (2019, Spring semester, Friday, 10:30-12:00, Room 242, Dr. Shogo OKAMOTO)

システム・ダイナミクス特論(H31年,前期,金曜日10:30-12:00, 242講義室, 岡本 正吾 准教授)

Course purpose

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 how to apply each method of multivariate analysis on real data and interpret the results throughout the course.

What you will learn

Multi-variate analysis techniques


Based on 3 reports and one presentation.


Dr. Shogo OKAMOTO, shogo.okamoto (at) mae.nagoya-u dot jp

Matlab data file

baseball2013.mat   baseball2016.mat

Archive and schedule

1st class. (Introduction & Multiple regression analysis 1) Apr. 12th
Lecture note: Introduction.pdf MRA1.pdf
Matlab sample file. class1.m

2nd class. (Multiple regression analysis 2) Apr. 19th
Lecture note: MRA2.pdf
Matlab sample file. class2.m   baseball2016.mat

3rd class. (Multiple regression analysis 3) Apr. 26th
Lecture note: MRA3.pdf
Matlab sample file. class3.m

1st report. Multiple regression analysis, Due: May 17th @ the class
Analyse your own data by using multiple regression analysis. Your ~10-page presentation style report should include

4th class. (Principal component analysis 1) May 10th
Lecture note: PCA.pdf
Matlab sample file. class4.m

5th class. (Principal component analysis 2) May 17th
Matlab sample file. class51.m   class52.m

6th class. (Principal component analysis 3) May 24th
Lecture note: PCA.pdf
Matlab sample file. class6.m

2nd report. Principal component analysis, Due: Jun. 14th @ the class
Analyse your own data by using principal component analysis. Your ~10-page presentation-style report should include

7th class. (Structural equation modeling 1) May 31th
Lecture note: SEM.pdf

No class Jun. 7th
No class Jun. 14th (No class because of Univ. festival)

8th class. (Structural equation modeling 2) Jun. 21th
Lecture note: SEM.pdf
Matlab sample file.
Link model: link.m   mylink.m  
Latent factor model: fa.m   myfa.m  
Sequential model: sequential.m   mysequential.m  
Regression model: regress.m   myregress.m  
MIMIC model: mimic.m   mymimic.m   baseball_data.m

3rd report. Structural equation modeling, Due: Jul. 12th by e-mail.

9th class. (Covariance selection) Jun. 28th
Lecture note: CovarianceSelection.pdf
Matlab sample file. class10.m

Instructions for final presentation!
  • I randomly choose ~30 students.
  • Those who are selected will give their own presentations about multiple regression analysis, PCA, or SEM. You can choose.
  • Each has 10 min including Q&A. The main presentation should be as long as 7-8 min.
  • You may (recommended) update your presentation file for the final presentation.
  • Use your own laptop or my computer for the presentation. Bring your USB-flash memory or you may send me your presentation files beforehand. Bring the HDMI or DVI adaptors for your computers.

  • 10th class. (Student presentation) Jul. 5th
    Mr. Hasegawa, Mr. Schatz, Mr. Y. Song, Mr. K. Inoue, Mr. K. Iwata, Ms. 許, and Ms. Wu in the order of presentation.

    11th class. (Presentation) Jul. 12th
    Mr. Ujiie, Mr. Hara, Mr. Hongyuan, Mr. H. Iwai, Mr. Miao, and Mr. Qiu.

    12th class. (Presentation) Jul. 19th
    Ms. Luthardt, Mr. Kanada, Mr. R. Inoue, Mr. Naito, Ms. F. Li, Mr. T. Sato, Mr. Miyahara, and Ms. Sinha.

    13th class. (Presentation) Jul. 26th
    Ms. Ji, Mr. Sennin, Mr. Taga, Mr. Imagawa, Mr. Kito, Mr. Ura, Mr. Sumiya and Mr. Y. Yamamoto.