KA114001 Statistical Test

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Informatics and Data Science

Lecture Code KA114001 Subject Classification Specialized Education

Subject Name 統計的検定

Subject Name
（Katakana）トウケイテキケンテイ

Subject Name in
English Statistical Test

Instructor YANAGIHARA HIROKAZU

Instructor
(Katakana) ヤナギハラ　ヒロカズ

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

Days, Periods, and Classrooms (Int) Inte

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Lecture, Quiz

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

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 Understand and use the statistical hypothesis test

Class Schedule lesson1  Population and characteristic values
lesson2  The variation in characteristic values of data and the bootstrap method
lesson3  Interval estimation of characteristic value via bootstrap method
lesson4  The idea of hypothesis testing
lesson5  Hypothesis testing of characteristic value via bootstrap method
lesson6  Population and statistical model
lesson7  Sample distribution and interval estimation
lesson8  Interval estimation using sample distribution
lesson9  Various interval estimation
lesson10  Expected values and variances of sample characteristic values
lesson11  Hypothesis testing using sample distribution
lesson12 Various hypothesis testing
lesson13 Erros of hypothesis testing and power
lesson14 Analysis of variance
lesson15  Multiple comparisons

Text/Reference
Books,etc. None

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

(More Details) Text

Learning techniques to be incorporated Quizzes/ Quiz format

Suggestions on
Preparation and
Review Understand every classes

Requirements It is desirable to have taken a course in inferential statistics.

Grading Method Reports

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other Please bring your own PC as we will be doing exercises.

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	KA114001	Subject Classification	Specialized Education
Subject Name	統計的検定
Subject Name （Katakana）	トウケイテキケンテイ
Subject Name in English	Statistical Test
Instructor	YANAGIHARA HIROKAZU
Instructor (Katakana)	ヤナギハラ　ヒロカズ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, First Semester, Intensive
Days, Periods, and Classrooms	(Int) Inte
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
Lecture, Quiz
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
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	Understand and use the statistical hypothesis test
Class Schedule	lesson1 Population and characteristic values lesson2 The variation in characteristic values of data and the bootstrap method lesson3 Interval estimation of characteristic value via bootstrap method lesson4 The idea of hypothesis testing lesson5 Hypothesis testing of characteristic value via bootstrap method lesson6 Population and statistical model lesson7 Sample distribution and interval estimation lesson8 Interval estimation using sample distribution lesson9 Various interval estimation lesson10 Expected values and variances of sample characteristic values lesson11 Hypothesis testing using sample distribution lesson12 Various hypothesis testing lesson13 Erros of hypothesis testing and power lesson14 Analysis of variance lesson15 Multiple comparisons
Text/Reference Books,etc.	None
PC or AV used in Class,etc.	Handouts, Microsoft Teams, moodle
(More Details)	Text
Learning techniques to be incorporated	Quizzes/ Quiz format
Suggestions on Preparation and Review	Understand every classes
Requirements	It is desirable to have taken a course in inferential statistics.
Grading Method	Reports
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	Please bring your own PC as we will be doing exercises.
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