KA220001 Econometrics

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School School of Informatics and Data Science

Lecture Code KA220001 Subject Classification Specialized Education

Subject Name 計量経済学

Subject Name
（Katakana）ケイリョウケイザイガク

Subject Name in
English Econometrics

Instructor 　

Instructor
(Katakana) 　

Campus Higashi-Hiroshima Semester/Term 3rd-Year, First Semester, Intensive

Days, Periods, and Classrooms (Int) Inte

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

Intensive course

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

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 03 : Economics

Eligible Students 3年次生

Keywords Correlation analysis, regression analysis, regression diagnostics

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.

Data Science Program
（Comprehensive Abilities）
・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.

Intelligence Science Program
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.

Class Objectives
/Class Outline This course is designed primarily for third-year students in the School of Informatics and provides instruction on statistical analysis methods based on economic data.

Class Schedule Lecture 1: Course overview
Lecture 2: Review of linear algebra
Lecture 3: Review of statistics
Lecture 4: Correlation analysis
Lecture 5: Simple regression analysis and the method of least squares
Lecture 6: Hypothesis testing and estimation for regression coefficients
Lecture 7: Residual analysis
Lecture 8: Simple regression analysis with repeated observations
Lecture 9: Multiple regression analysis and the method of least squares
Lecture 10: Interpretation of partial regression coefficients and the multiple correlation coefficient
Lecture 11: Hypothesis testing and estimation for partial regression coefficients
Lecture 12: Selection of explanatory variables
Lecture 13: Regression diagnostics
Lecture 14: Various regression models
Lecture 15: Course review

Text/Reference
Books,etc. Textbook:
- Handouts will be provided.

References:
- Hitoshi Kume and Yoshinori Iizuka, Regression Analysis. Iwanami Shoten, 1987.
- Yasushi Nagata and Masahiko Munechika, Introduction to Multivariate Analysis. Science-sha, 2001.
- Chihiko Minotani, Linear Regression Analysis. Asakura Publishing, 2015.

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

(More Details) Lecture materials will be displayed on a screen and explained during class. Assignments will be submitted via Moodle.
Practical exercises using MS Excel are planned; therefore, students should bring their own laptop computers.

Learning techniques to be incorporated

Suggestions on
Preparation and
Review Lectures 2–15: Students are encouraged to review the material before and after each class.

Requirements

Grading Method Grades will be based on in-class exercises (50%) and short quizzes (50%).

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other Announcements related to the course will be provided through Moodle.

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	2026Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA220001	Subject Classification	Specialized Education
Subject Name	計量経済学
Subject Name （Katakana）	ケイリョウケイザイガク
Subject Name in English	Econometrics
Instructor
Instructor (Katakana)
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, Intensive
Days, Periods, and Classrooms	(Int) Inte
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
Intensive course
Credits	2.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	03 : Economics
Eligible Students	3年次生
Keywords	Correlation analysis, regression analysis, regression diagnostics
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. Data Science Program （Comprehensive Abilities）・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability. Intelligence Science Program （Comprehensive Abilities）・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.
Class Objectives /Class Outline	This course is designed primarily for third-year students in the School of Informatics and provides instruction on statistical analysis methods based on economic data.
Class Schedule	Lecture 1: Course overview Lecture 2: Review of linear algebra Lecture 3: Review of statistics Lecture 4: Correlation analysis Lecture 5: Simple regression analysis and the method of least squares Lecture 6: Hypothesis testing and estimation for regression coefficients Lecture 7: Residual analysis Lecture 8: Simple regression analysis with repeated observations Lecture 9: Multiple regression analysis and the method of least squares Lecture 10: Interpretation of partial regression coefficients and the multiple correlation coefficient Lecture 11: Hypothesis testing and estimation for partial regression coefficients Lecture 12: Selection of explanatory variables Lecture 13: Regression diagnostics Lecture 14: Various regression models Lecture 15: Course review
Text/Reference Books,etc.	Textbook: - Handouts will be provided. References: - Hitoshi Kume and Yoshinori Iizuka, Regression Analysis. Iwanami Shoten, 1987. - Yasushi Nagata and Masahiko Munechika, Introduction to Multivariate Analysis. Science-sha, 2001. - Chihiko Minotani, Linear Regression Analysis. Asakura Publishing, 2015.
PC or AV used in Class,etc.	Microsoft Teams, moodle
(More Details)	Lecture materials will be displayed on a screen and explained during class. Assignments will be submitted via Moodle. Practical exercises using MS Excel are planned; therefore, students should bring their own laptop computers.
Learning techniques to be incorporated
Suggestions on Preparation and Review	Lectures 2–15: Students are encouraged to review the material before and after each class.
Requirements
Grading Method	Grades will be based on in-class exercises (50%) and short quizzes (50%).
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	Announcements related to the course will be provided through Moodle.
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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