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2019　Applied Statistical Analysis

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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(W934)  Fri5-6(W934)  
Group
-
Course number
IEE.C431
Credits
2
Academic year
2019
Offered quarter
1Q
Syllabus updated
2019/4/11
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 and area
Class 2 One-way layout: anaysisi of variance and regression analysis 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 Interaction analysis for two-way data Utilization of orthogonal polinomial Application of orthogonal polinomial for two-way data
Class 6 Interaction analysis for two-way data Utilization of FANOVA modal Application of FANOVA model
Class 7 Correspondence anaysis Application of cprrespondence analysis
Class 8 Role of statistical methods Nothing
Class 9 Analysis of covariance Applicatiopn of analysis of covariance
Class 10 Ketric MDS Application of metric MDS
Class 11 Discriminant analysis Application of discriminant analysis
Class 12 Robust parameter design: static characteristics Robust parameter design for static characteristics
Class 13 Robust parameter design: dynamic characteristics Robust parameter design for dynamic chracteristics
Class 14 Graphicak modeling: Covariance selection Application of covariance selection
Class 15 Graphical modeling: Log-linear model Application of log-linear model

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

  • Statistical Engineering

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Elementary ｓtatistical methods

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