KA112001 Inferential Statistics

Hiroshima University Syllabus

Japanese

Academic Year 2025Year 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 IMORI SHINPEI

Instructor
(Katakana) イモリ　シンペイ

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

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

Lesson Style Lecture Lesson Style
(More Details) Online (simultaneous interactive)

Lecture-oriented, Note-taking, Teams

Credits 2.0 Class Hours/Week 4 Language of Instruction B : Japanese／English

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 statistical inference.

Class Schedule lesson1 Population and statistical model
lesson2 Random variable
lesson3 Expectation
lesson4 Various probability distributions
lesson5 Convergences of random variable
lesson6 Exercise
lesson7 Point estimation
lesson8 Unbiasedness, variance, mean square error
lesson9 Consistency
lesson10 Asymptotic normality
lesson11 Least square estimation
lesson12 Maximum likelihood estimation
lesson13 Interval estimation
lesson14 Exercise
lesson15 Conclusion

Examination

The class schedule may be changed due to the progress.

Text/Reference
Books,etc. Not specified

PC or AV used in
Class,etc. Microsoft Teams, moodle

(More Details) 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 Attitude (40%) and examination (60%).

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	2025Year	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	IMORI SHINPEI
Instructor (Katakana)	イモリ　シンペイ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, First Semester, 1Term
Days, Periods, and Classrooms	(1T) Tues5-8：Online
Lesson Style	Lecture	Lesson Style (More Details)	Online (simultaneous interactive)
Lecture-oriented, Note-taking, Teams
Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
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 statistical inference.
Class Schedule	lesson1 Population and statistical model lesson2 Random variable lesson3 Expectation lesson4 Various probability distributions lesson5 Convergences of random variable lesson6 Exercise lesson7 Point estimation lesson8 Unbiasedness, variance, mean square error lesson9 Consistency lesson10 Asymptotic normality lesson11 Least square estimation lesson12 Maximum likelihood estimation lesson13 Interval estimation lesson14 Exercise lesson15 Conclusion Examination The class schedule may be changed due to the progress.
Text/Reference Books,etc.	Not specified
PC or AV used in Class,etc.	Microsoft Teams, moodle
(More Details)	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	Attitude (40%) and examination (60%).
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.

Back to syllabus main page