2020 Faculty Courses School of Engineering Department of Industrial Engineering and Economics Graduate major in Industrial Engineering and Economics
Applied Statistical Analysis
- Academic unit or major
- Graduate major in Industrial Engineering and Economics
- Instructor(s)
- Masami Miyakawa
- Class Format
- Lecture (Zoom)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue (W934) / 5-6 Fri (W934)
- Class
- -
- Course Code
- IEE.C431
- Number of credits
- 200
- Course offered
- 2020
- Offered quarter
- 1Q
- Syllabus updated
- Jul 10, 2025
- Language
- English
Syllabus
Course overview and goals
Practical methods of advanced statistics are explained.
Course description and aims
To master the gramer of science for your research.
Keywords
Analysis of variance, Regression analysis, Analysis of interaction. Parameter design, Graphical modeling
Competencies
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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 |
Study advice (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
Evaluation methods and criteria
Evaluation of reports.
Related courses
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A205 ： Statistics for Industrial Engineering and Economics
Prerequisites
Elementary ｓtatistical methods