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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
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- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Industrial Engineering and Economics
- Instructor(s)
- Miyakawa Masami
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue5-6() Fri5-6()
- Group
- -
- Course number
- IEE.C431
- Credits
- 2
- Academic year
- 2021
- Offered quarter
- 1Q
- Syllabus updated
- 2021/4/6
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
Practical methods of advanced statistics are explained.
Student learning outcomes
To master the gramer of science for your research.
Keywords
Analysis of variance, Regression analysis, Analysis of interaction. Parameter design, Graphical modeling
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Exercise is performed in every class. PC or EC are necessary.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Orientation, Buffon needle | Estimation of dintance |
| Class 2 | One-way layout: anaysisi of variance and orthogonal polynomial | Application of orthogonal polinomial |
| Class 3 | Analysis of three-way contingency table | Application of Mntel and Hentzel Test |
| Class 4 | corelation, multiple cprelation, partial corelation | Analysis of partial corelation |
| Class 5 | path analysisl | Application of path analysis |
| Class 6 | Interaction analysis for two-way data Application of orthogonal polynomial | Application of orthogonal for two-way data |
| Class 7 | Interaction analysis for two-way data Application of FANOVA model | Application of FANOVA model |
| Class 8 | Principal component analysis | Analysis with principal component analysis |
| Class 9 | Correspondence Analysis | Applicatiopn of correspondence analysis |
| Class 10 | Multiple correspondence analysis | Application of multiple correspondence analysis |
| Class 11 | Analysis of covariance and intermediate variable | Application of analysis of variance |
| Class 12 | Metric multi-dimensional scaling | Application of metric multi-dimensional scaling |
| Class 13 | Discriminant analysis | Analysis with asymmetric discrimminant analysis |
| Class 14 | Graphicak modeling: Covariance selection | Application of covariance selection |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Nothing
Reference books, course materials, etc.
Enkawa,T. and Miyakawa,M. SQC Theoey and Practice
Miyakawa,M. Statistical Technology
Miyakawa,M. Graphical MOdelong
Miyakawa,M. Technology for Getting Quality
Assessment criteria and methods
Evaluation of reports.
Related courses
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A205 ： Statistics for Industrial Engineering and Economics
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Elementary ｓtatistical methods
