G6005411 Statistics 2

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School School of Economics Economics Day Course

Lecture Code G6005411 Subject Classification Specialized Education

Subject Name 統計学２

Subject Name
（Katakana）トウケイガク２

Subject Name in
English Statistics 2

Instructor To be announced.

Instructor
(Katakana) タントウキョウインミテイ

Campus Higashi-Hiroshima Semester/Term 1st-Year, Second Semester, 4Term

Days, Periods, and Classrooms (4T) Thur1-4：ECON B257

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive)

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

Course Level 1 : Undergraduate Introductory

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 03 : Economics

Eligible Students Anyone

Keywords Descriptive Statistics, Probability Theory, Mathematical Statistics, Statistical Inference, Economic Statistics

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)

Class Objectives
/Class Outline Upon completion, students will have the knowledge and skills to:
1. Summarize and graph data appropriately
2. Work with random variables and probability distributions
3. Describe and use the normal distribution appropriately
4. Identify when and how to carry out basic statistical inference such as estimation and hypothesis testing
5. Identify contexts in which particular statistical methods may be inappropriate

Class Schedule 1. Purpose and framework of inferential statistics
2. How data are obtained and the limitations of inference
3. Basic sampling methods
4. Sampling distributions and standard error
5. Properties of statistics and estimators
6. Confidence intervals: concepts and interpretation
7. One-parameter inference for a mean and a proportion
8. The framework of hypothesis testing (Homework 1 due)
9. Comparing two means: independent vs paired designs
10. Comparing two independent proportions
11. Chi-square methods: independence, goodness-of-fit, and a normal variance
12. One-way ANOVA: comparing three or more independent means
13. Comprehensive practice: method selection, computation, and writing up results
14. Power and sample size planning
15. Review and synthesis (Homework 2 due)

Grade for this course is determined by two homework assignments and a final exam. The course schedule is intended as a guideline; depending on the pace of the class and students’ understanding, some parts of the course content may be adjusted.

Text/Reference
Books,etc. Instead of relying on a particular textbook, the course will be conducted based on the lecture notes distributed in class.
References:
• Moore, D. S., McCabe, G. P., & Craig, B. A. “Introduction to the Practice of Statistics (9th ed.)”
• Ott, R. L., & Longnecker, M. T. “An Introduction to Statistical Methods and Data Analysis (7th ed.)”
• Bertsekas, D. P., & Tsitsiklis, J. N. “Introduction to Probability (2nd ed.)”
• Blitzstein, J. K., & Hwang, J. “Introduction to Probability (2nd ed.)"
• 大屋幸輔（著）「コア・テキスト統計学（第3版）」
• 森棟公夫・照井伸彦・中川満・西埜晴久・黒住英司（著）「統計学改訂版」
• 豊田利久・大谷一博・小川一夫・長谷川光・谷崎久志（著）「基本統計学（第3版）」
• 尾畑伸明（著）「数理統計学の基礎」

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

(More Details)

Learning techniques to be incorporated

Suggestions on
Preparation and
Review • This course is designed on the assumption that students are comfortable with probability and statistics at the high school level. We will review relevant mathematics when introducing new concepts, but students are encouraged to review relevant high school textbooks and the supplementary materials provided outside of class as necessary.
• University lectures move at a fast pace. If you can understand the lecture notes, attending every lecture is not required. However, if you find it difficult to keep up with the material, please take initiative in your learning by reviewing the material with classmates and working collaboratively to deepen your understanding.

Requirements Requirements There are no formal prerequisites, but this course assumes familiarity with the material covered in Statistics I, as it is designed as a natural continuation of that course. Students who wish to deepen their understanding of statistics are encouraged to take or audit courses related to statistics from other departments.

Grading Method Homework assignments 1 & 2 combined: approximately 40%
Final exam: approximately 60%
N.B. weights on homework assignments and exam are subject to change.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message You may often hear terms such as data science, machine learning, and AI, which are transforming our everyday lives. Statistics provides the foundation for these fields. Because Statistics I and II focus on basic concepts, the importance of what you learn here and its connection to applications may not be immediately obvious. However, the material covered in this course forms the foundation for more advanced subjects you may take later, such as Econometrics. I encourage you to engage actively in the course and develop the ability to interpret and use data correctly.

We will occasionally conduct demonstrations of practical problems using the statistical software R. These exercises will not directly affect your course grade, but students who are interested in learning how to code or program are encouraged to bring a laptop computer.

Other Language of instruction: The course will be conducted primarily in Japanese. However, since some of the reference materials are written in English, technical terms may occasionally be presented in both Japanese and English.

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 Economics Economics Day Course
Lecture Code	G6005411	Subject Classification	Specialized Education
Subject Name	統計学２
Subject Name （Katakana）	トウケイガク２
Subject Name in English	Statistics 2
Instructor	To be announced.
Instructor (Katakana)	タントウキョウインミテイ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Thur1-4：ECON B257
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)

Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
Course Level	1 : Undergraduate Introductory
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	03 : Economics
Eligible Students	Anyone
Keywords	Descriptive Statistics, Probability Theory, Mathematical Statistics, Statistical Inference, Economic Statistics
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)
Class Objectives /Class Outline	Upon completion, students will have the knowledge and skills to: 1. Summarize and graph data appropriately 2. Work with random variables and probability distributions 3. Describe and use the normal distribution appropriately 4. Identify when and how to carry out basic statistical inference such as estimation and hypothesis testing 5. Identify contexts in which particular statistical methods may be inappropriate
Class Schedule	1. Purpose and framework of inferential statistics 2. How data are obtained and the limitations of inference 3. Basic sampling methods 4. Sampling distributions and standard error 5. Properties of statistics and estimators 6. Confidence intervals: concepts and interpretation 7. One-parameter inference for a mean and a proportion 8. The framework of hypothesis testing (Homework 1 due) 9. Comparing two means: independent vs paired designs 10. Comparing two independent proportions 11. Chi-square methods: independence, goodness-of-fit, and a normal variance 12. One-way ANOVA: comparing three or more independent means 13. Comprehensive practice: method selection, computation, and writing up results 14. Power and sample size planning 15. Review and synthesis (Homework 2 due) Grade for this course is determined by two homework assignments and a final exam. The course schedule is intended as a guideline; depending on the pace of the class and students’ understanding, some parts of the course content may be adjusted.
Text/Reference Books,etc.	Instead of relying on a particular textbook, the course will be conducted based on the lecture notes distributed in class. References: • Moore, D. S., McCabe, G. P., & Craig, B. A. “Introduction to the Practice of Statistics (9th ed.)” • Ott, R. L., & Longnecker, M. T. “An Introduction to Statistical Methods and Data Analysis (7th ed.)” • Bertsekas, D. P., & Tsitsiklis, J. N. “Introduction to Probability (2nd ed.)” • Blitzstein, J. K., & Hwang, J. “Introduction to Probability (2nd ed.)" • 大屋幸輔（著）「コア・テキスト統計学（第3版）」 • 森棟公夫・照井伸彦・中川満・西埜晴久・黒住英司（著）「統計学改訂版」 • 豊田利久・大谷一博・小川一夫・長谷川光・谷崎久志（著）「基本統計学（第3版）」 • 尾畑伸明（著）「数理統計学の基礎」
PC or AV used in Class,etc.	Handouts, moodle
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	• This course is designed on the assumption that students are comfortable with probability and statistics at the high school level. We will review relevant mathematics when introducing new concepts, but students are encouraged to review relevant high school textbooks and the supplementary materials provided outside of class as necessary. • University lectures move at a fast pace. If you can understand the lecture notes, attending every lecture is not required. However, if you find it difficult to keep up with the material, please take initiative in your learning by reviewing the material with classmates and working collaboratively to deepen your understanding.
Requirements	Requirements There are no formal prerequisites, but this course assumes familiarity with the material covered in Statistics I, as it is designed as a natural continuation of that course. Students who wish to deepen their understanding of statistics are encouraged to take or audit courses related to statistics from other departments.
Grading Method	Homework assignments 1 & 2 combined: approximately 40% Final exam: approximately 60% N.B. weights on homework assignments and exam are subject to change.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	You may often hear terms such as data science, machine learning, and AI, which are transforming our everyday lives. Statistics provides the foundation for these fields. Because Statistics I and II focus on basic concepts, the importance of what you learn here and its connection to applications may not be immediately obvious. However, the material covered in this course forms the foundation for more advanced subjects you may take later, such as Econometrics. I encourage you to engage actively in the course and develop the ability to interpret and use data correctly. We will occasionally conduct demonstrations of practical problems using the statistical software R. These exercises will not directly affect your course grade, but students who are interested in learning how to code or program are encouraged to bring a laptop computer.
Other	Language of instruction: The course will be conducted primarily in Japanese. However, since some of the reference materials are written in English, technical terms may occasionally be presented in both Japanese and English.
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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