KA112001 Inferential Statistics

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Informatics and Data Science

Lecture Code KA112001 Subject Classification Specialized Education

Subject Name 推測統計学

Subject Name
（Katakana）スイソクトウケイガク

Subject Name in
English Inferential Statistics

Instructor YANAGIHARA HIROKAZU

Instructor
(Katakana) ヤナギハラ　ヒロカズ

Campus Higashi-Hiroshima Semester/Term 2nd-Year, First Semester, 1Term

Days, Periods, and Classrooms (1T) Tues5-8

Lesson Style Lecture Lesson Style
(More Details) 　

Lecture, Note-taking, Teams

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

Keywords Random variable, probability distribution, point estimation, interval estimation

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Intelligence Science Program
（Knowledge and Understanding）
・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Class Objectives
/Class Outline We study elementary inference statistics

Class Schedule lesson1 Characteristic value of data 1: mean
lesson2 Characteristic value of data 2: dispersion
lesson3 Description of data distribution: histogram and box plot
lesson4 Population and characteristic value of population
lesson5 Variation in characteristic values of data and the bootstrap method
lesson6 Interval estimation of characteristic values using bootstrap method
lesson7 Idea of hypothesis testing
lesson8 Hypothesis testing of characteristic values using bootstrap method
lesson9 Population and statistical model
lesson10 Point estimation
lesson11 Sample distribution and interval estimation
lesson12 Interval estimation using sample distribution
lesson13 Various interval estimation
lesson14 Hypothesis testing using sample distribution
lesson15 Various hypothesis testing

There might be some small changes on lessons

Text/Reference
Books,etc. Not specified

PC or AV used in
Class,etc. 　

(More Details) Hand-out, PC

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Please do not hesitate to ask a question if you have dubious points

Requirements

Grading Method final report and Tasks

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

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.

Academic Year	2024Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA112001	Subject Classification	Specialized Education
Subject Name	推測統計学
Subject Name （Katakana）	スイソクトウケイガク
Subject Name in English	Inferential Statistics
Instructor	YANAGIHARA HIROKAZU
Instructor (Katakana)	ヤナギハラ　ヒロカズ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, First Semester, 1Term
Days, Periods, and Classrooms	(1T) Tues5-8
Lesson Style	Lecture	Lesson Style (More Details)
Lecture, Note-taking, Teams
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
Keywords	Random variable, probability distribution, point estimation, interval estimation
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Data Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Intelligence Science Program （Knowledge and Understanding）・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
Class Objectives /Class Outline	We study elementary inference statistics
Class Schedule	lesson1 Characteristic value of data 1: mean lesson2 Characteristic value of data 2: dispersion lesson3 Description of data distribution: histogram and box plot lesson4 Population and characteristic value of population lesson5 Variation in characteristic values of data and the bootstrap method lesson6 Interval estimation of characteristic values using bootstrap method lesson7 Idea of hypothesis testing lesson8 Hypothesis testing of characteristic values using bootstrap method lesson9 Population and statistical model lesson10 Point estimation lesson11 Sample distribution and interval estimation lesson12 Interval estimation using sample distribution lesson13 Various interval estimation lesson14 Hypothesis testing using sample distribution lesson15 Various hypothesis testing There might be some small changes on lessons
Text/Reference Books,etc.	Not specified
PC or AV used in Class,etc.
(More Details)	Hand-out, PC
Learning techniques to be incorporated
Suggestions on Preparation and Review	Please do not hesitate to ask a question if you have dubious points
Requirements
Grading Method	final report and Tasks
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
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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