Japanese

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 （Katakana）	データカイセキジヨセツ
Subject Name in English	Foundation of Date Analysis
Instructor	HASHIMOTO SHINTARO
Instructor (Katakana)	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)
			Lecture
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 Program	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 Evaluation	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.
Text/Reference Books,etc.	Some reference books will be introduced in the first class.
PC or AV used in Class,etc.
	(More Details)	Projector (PC)，your own PC
Learning techniques to be incorporated
Suggestions on Preparation and Review	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.
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.