### 2020　Applied Statistical Analysis

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Graduate major in Industrial Engineering and Economics
Instructor(s)
Miyakawa Masami
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Tue5-6(W934)  Fri5-6(W934)
Group
-
Course number
IEE.C431
Credits
2
2020
Offered quarter
1Q
Syllabus updated
2020/9/18
Lecture notes updated
-
Language used
English
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### 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.

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