Hiroshima University Syllabus

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Japanese
Academic Year 2022Year School/Graduate School School of Informatics and Data Science
Lecture Code KA122001 Subject Classification Specialized Education
Subject Name 多変量解析
Subject Name
(Katakana)
タヘンリョウカイセキ
Subject Name in
English
Multivariate Analysis
Instructor SUMIYA TAKAHIRO
Instructor
(Katakana)
スミヤ タカヒロ
Campus Higashi-Hiroshima Semester/Term 2nd-Year,  Second Semester,  3Term
Days, Periods, and Classrooms (3T) Thur7-10:ENG 218
Lesson Style Lecture Lesson Style
(More Details)
 
Lecture, Hands-on 
Credits 2.0 Class Hours/Week   Language of Instruction J : Japanese
Course Level 2 : Undergraduate Low-Intermediate
Course Area(Area) 25 : Science and Technology
Course Area(Discipline) 01 : Mathematics/Statistics
Eligible Students 2nd degree
Keywords  
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
Program
 
Criterion referenced
Evaluation
Informatics and Data Science Program
(Knowledge and Understanding)
・D1. Knowledge and skills required for understanding the theoretical system of statistics and data analysis, and for precisely and efficiently analyzing qualitative/quantitative information in big data.
 
Class Objectives
/Class Outline
The target of this class is to learn elementary methods on multivariate analysis, and obtain ability to apply them on real data. 
Class Schedule lesson 1: Introduction to statistical analysis software "R" (Data type and statistical functions)
lesson 2: Introduction to statistical analysis software "R" (function definition and control structure)
lesson 3: Analysis of variance 1 (description of method)
lesson 4: Analysis of variance 2 (data analysis exercise with R)
lesson 5: Regression analysis 1 (description of method)
lesson 6: Regression analysis 2 (data analysis exercise with R)
lesson 7: Principal components analysis 1  (description of method)
lesson 8: Principal components analysis 2 (data analysis exercise with R)
lesson 9: Linear discriminant analysis 1  (description of method)
lesson 10: Linear discriminant analysis 2 (data analysis exercise with R)
lesson 11: Cluster analysis 1  (description of method)
lesson 12: Cluster analysis 2 (data analysis exercise with R)
lesson 13: Visualization of multivariate data
lesson 14: Exercise 1 (group work)
lesson 15: Exercise 2 (presentation) 
Text/Reference
Books,etc.
Slide files on Bb9 
PC or AV used in
Class,etc.
 
(More Details) Text, Slides, Hikkei PC 
Learning techniques to be incorporated  
Suggestions on
Preparation and
Review
Try to apply the data analysis methods on your own data, or any data you can find. 
Requirements  
Grading Method assignment, short exam. 
Practical Experience  
Summary of Practical Experience and Class Contents based on it  
Message We will have hands-on using "R".  Install R and RStudio ( https://speakerdeck.com/gnutar/try-r-en ) on your laptop and bring it on every week. 
Other   
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. 
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