2020 Applied Statistical Analysis

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Academic unit or major
Graduate major in Industrial Engineering and Economics
Instructor(s)
Miyakawa Masami 
Course component(s)
Lecture
Mode of instruction
ZOOM
Day/Period(Room No.)
Tue5-6(W934)  Fri5-6(W934)  
Group
-
Course number
IEE.C431
Credits
2
Academic year
2020
Offered quarter
1Q
Syllabus updated
2020/3/24
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

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

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