Hiroshima University Syllabus

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Academic Year 2022Year School/Graduate School School of Integrated Arts and Sciences Department of Integrated Arts and Sciences
Lecture Code ANM01001 Subject Classification Specialized Education
Subject Name データ解析序説
Subject Name
Subject Name in
Foundation of Date Analysis
ハシモト シンタロウ
Campus Higashi-Hiroshima Semester/Term 2nd-Year,  Second Semester,  4Term
Days, Periods, and Classrooms (4T) Thur5-8:IAS K205
Lesson Style Lecture Lesson Style
(More Details)
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 Students who are interested in data analysis
Keywords Mathematical statistics, Data analysis 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
This course is designed to allow students to understand the basics of mathematical statistics and to learn the meanings of the typical data analysis methods 
Criterion referenced
Integrated Arts and Sciences
(Knowledge and Understanding)
・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework.
(Abilities and Skills)
・The ability and skills to collect and analyze necessary literature or data among various sources of information on individual academic disciplines.
・The ability and skills to specify necessary theories and methods for consideration of issues. 
Class Objectives
/Class Outline
Various factors are intertwined in the natural and social phenomena observed in our daily lives. To correctly understand these phenomena, it necessary to summarize the observed data in a simplified form or to examine the relationship between the factors that produce these phenomena. Data analysis is an important tool for this. In this series of lectures, I will introduce the basics of data analysis techniques. 
Class Schedule Lesson 1: Introduction
Lesson 2: Correlation and regression
Lesson 3: Definition of probability
Lesson 4: Random variable
Lesson 5: Probability distribution
Lesson 6: Multivariate normal distribution
Lesson 7: Random sampling
Lesson 8: Limit theorem
Lesson 9: Point estimation and interval estimation
Lesson 10: Hypothesis testing
Lesson 11: Linear regression model(I)
Lesson 12: Linear regression model(II)
Lesson 13: Logistic regression model
Lesson 14: Principle component analysis
Lesson 15: Clustering

Several quiz will be given in the class and a final report will be required. 
Some reference books will be introduced in the first class. 
PC or AV used in
(More Details) Projector (PC),your own PC 
Learning techniques to be incorporated  
Suggestions on
Preparation and
In order to adequately understand the data analysis techniques, it is important to apply them to real data. 
Requirements Prior knowledge of the basics of mathematics (Linear algebra and differential and integral calculus) and statistics is assumed. 
Grading Method Class quiz 50% and final report 50%. 
Practical Experience  
Summary of Practical Experience and Class Contents based on it  
Message Please bring your own PC in the class. In the exercise using R programming language, the multivariate data analysis methods are applied to some data. 
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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