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 TOMIOKA KAZUKI

Instructor
(Katakana) トミオカ　カズキ

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

Days, Periods, and Classrooms (4T) Weds5-8：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 Statistical inference, estimation, hypothesis testing, asymptotic theory, regression analysis

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) Statistics II is the second-term course that follows Statistics I in the first term. It covers how to draw inferences about a population from a sample, and bridges to econometrics and more advanced statistics.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Economic Analysis
（Knowledge and Understanding）
・Basic knowledge of mathematical analysis approach on economic issues

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 Statistics II
1. Course guidance and overview: the scope of Statistics II
2. Aims and framework of statistical inference: estimands, estimators, sampling distributions,
standard errors
3. Point estimation: bias, variance, mean squared error, consistency, method of moments and
maximum likelihood
4. Confidence intervals: concepts and interpretation, means and proportions
5. The framework of hypothesis testing: p-values, Type I and Type II errors, and the link to
confidence intervals
6. Power and sample size
7. Comparing means: one sample, two independent samples, paired samples
8. Proportions and categorical data: differences in proportions, goodness of fit, tests of
independence
9. Mid-term test (80 minutes), covering sessions 1-8
10. Asymptotic theory: consistency, asymptotic normality, the delta method
11. Simple regression I: least squares, coefficients, residuals, the coefficient of determination
12. Simple regression II: inference on the slope, robust standard errors, diagnostics
13. Multiple regression: dummy variables, control variables, confounding, omitted variable bias
14. Interactions, nonlinearity, and analysis of variance as a regression model
15. Review (homework due)

Grade for this course is determined by an 80-minute mid-term test in session 9, one homework assignment
due at the end of term, 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.)”
• Stock, J. H., & Watson, M. W. “Introduction to Econometrics, Global Edition (4th 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 Quizzes/ Quiz format, Post-class Report

Suggestions on
Preparation and
Review • Review the random variables, expectation, variance, conditional probability, the law of large
numbers, and the central limit theorem covered in Statistics I. The asymptotic theory in session
10 uses them directly.
• 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 There are no formal prerequisites. However, this course assumes the content of Statistics I and is designed as its continuation. Students who wish to deepen their understanding of statistics are encouraged to take or audit courses related to statistics from other departments.

Grading Method Mid-term test (session 9, 80 minutes): approximately 30%
Homework (due at the end of term): approximately 10%
Final exam: approximately 60%
N.B. weights on the test, homework and exam may change.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message Statistical inference is a tool for making claims about a population from a sample, together with the uncertainty that remains. To use such a claim in a decision, one must consider the size of the effect, the study design, and the assumptions, as well as the consequences of being wrong. 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.
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	TOMIOKA KAZUKI
Instructor (Katakana)	トミオカ　カズキ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Weds5-8：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	Statistical inference, estimation, hypothesis testing, asymptotic theory, regression analysis
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	Statistics II is the second-term course that follows Statistics I in the first term. It covers how to draw inferences about a population from a sample, and bridges to econometrics and more advanced statistics.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Economic Analysis （Knowledge and Understanding）・Basic knowledge of mathematical analysis approach on economic issues
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	Statistics II 1. Course guidance and overview: the scope of Statistics II 2. Aims and framework of statistical inference: estimands, estimators, sampling distributions, standard errors 3. Point estimation: bias, variance, mean squared error, consistency, method of moments and maximum likelihood 4. Confidence intervals: concepts and interpretation, means and proportions 5. The framework of hypothesis testing: p-values, Type I and Type II errors, and the link to confidence intervals 6. Power and sample size 7. Comparing means: one sample, two independent samples, paired samples 8. Proportions and categorical data: differences in proportions, goodness of fit, tests of independence 9. Mid-term test (80 minutes), covering sessions 1-8 10. Asymptotic theory: consistency, asymptotic normality, the delta method 11. Simple regression I: least squares, coefficients, residuals, the coefficient of determination 12. Simple regression II: inference on the slope, robust standard errors, diagnostics 13. Multiple regression: dummy variables, control variables, confounding, omitted variable bias 14. Interactions, nonlinearity, and analysis of variance as a regression model 15. Review (homework due) Grade for this course is determined by an 80-minute mid-term test in session 9, one homework assignment due at the end of term, 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.)” • Stock, J. H., & Watson, M. W. “Introduction to Econometrics, Global Edition (4th 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	Quizzes/ Quiz format, Post-class Report
Suggestions on Preparation and Review	• Review the random variables, expectation, variance, conditional probability, the law of large numbers, and the central limit theorem covered in Statistics I. The asymptotic theory in session 10 uses them directly. • 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	There are no formal prerequisites. However, this course assumes the content of Statistics I and is designed as its continuation. Students who wish to deepen their understanding of statistics are encouraged to take or audit courses related to statistics from other departments.
Grading Method	Mid-term test (session 9, 80 minutes): approximately 30% Homework (due at the end of term): approximately 10% Final exam: approximately 60% N.B. weights on the test, homework and exam may change.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	Statistical inference is a tool for making claims about a population from a sample, together with the uncertainty that remains. To use such a claim in a decision, one must consider the size of the effect, the study design, and the assumptions, as well as the consequences of being wrong. 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. 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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