| 授業名 | Business Statistics |
|---|---|
| Course Title | Business Statistics |
| 担当教員 Instructor Name | Xinyang Wei |
| 授業形態 Class Type | 講義 Regular course |
| 授業形式 Class Format | On Campus |
| 単位 Credits | 2 |
| 言語 Language | EN |
| 科目区分 Course Category | 教養教育科目 / Liberal Arts |
| 学位 Degree | BBA |
| 開講情報 Terms / Location | 2023 UG Nisshin Term2 |
| コード Course Code | NUC405_N23A |
授業の概要 Course Overview
Mission Statementとの関係性 / Connection to our Mission Statement
Statistics involves the gathering, collection, and analysing of data in order to improve decision-making. Statistics is an important field because it enables us to comprehend general trends and patterns in a given data set. Statistics can be used to analyze data and draw conclusions. It can also make predictions about future events and behaviours.
For example, individuals can use statistics to make decisions in financial planning and budgeting, while organizations can be guided by statistics in financial policy decisions. In addition, business practitioners responsible for marketing, management, accounting, sales, or other business functions can also benefit from understanding statistical techniques.
This course provides a statistical foundation for the other courses in the Global BBA program, which educates future innovative and ethical business leaders with a "Frontier Spirit" and creates knowledge that advances modern business and society.
For example, individuals can use statistics to make decisions in financial planning and budgeting, while organizations can be guided by statistics in financial policy decisions. In addition, business practitioners responsible for marketing, management, accounting, sales, or other business functions can also benefit from understanding statistical techniques.
This course provides a statistical foundation for the other courses in the Global BBA program, which educates future innovative and ethical business leaders with a "Frontier Spirit" and creates knowledge that advances modern business and society.
授業の目的（意義） / Importance of this course
This is a methodology course intended to serve as a foundation for other courses in the global BBA program. The course will cover fundamental statistical tools (descriptive statistics and inferential statistics including point and interval estimation of parameters, hypothesis testing) and involve case studies, discussions, and interactive activities.
学修到達目標 / Achievement Goal
Participants will develop an understanding of fundamental methods of statistics in the fields of economics and management and will be able to develop and apply these methods to analyze real-world economic and management problems.
本授業の該当ラーニングゴール Learning Goals
*本学の教育ミッションを具現化する形で設定されています。
LG1 Critical Thinking
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Managerial Perspectives (BBA)
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Managerial Perspectives (BBA)
受講後得られる具体的スキルや知識 Learning Outcomes
Students who successfully complete this course will be able to
1. Use fundamental statistics in other courses.
2. Solve statistical problems in an Excel spreadsheet environment.
3. Formulate and solve real-world problems amenable to statistical analysis using data from economics and business, employing methods appropriate to the problem and available data.
4. Develop and strengthen critical and logical thinking as well as problem-solving skills.
1. Use fundamental statistics in other courses.
2. Solve statistical problems in an Excel spreadsheet environment.
3. Formulate and solve real-world problems amenable to statistical analysis using data from economics and business, employing methods appropriate to the problem and available data.
4. Develop and strengthen critical and logical thinking as well as problem-solving skills.
SDGsとの関連性 Relevance to Sustainable Development Goals
Goal 4 質の高い教育をみんなに（Quality Education）
教育手法 Teaching Method
| 教育手法 Teaching Method | % of Course Time | |
|---|---|---|
| インプット型 Traditional | 30 % | |
| 参加者中心型 Participant-Centered Learning | ケースメソッド Case Method | 70 % |
| フィールドメソッド Field Method | 0 % | |
| 合計 Total | 100 % | |
事前学修と事後学修の内容、レポート、課題に対するフィードバック方法 Pre- and Post-Course Learning, Report, Feedback methods
Each lecture is divided into two sections: theory and practice. The theory section helps students establish relevant statistical concepts and explore statistical methods for problem-solving; the practice section requires students to solve practical problems based on the theoretical knowledge they have learned. Active participation in class discussions is expected and required.
Students should spend no less than 3 hours preparing and reviewing each lecture.
Students should spend no less than 3 hours preparing and reviewing each lecture.
授業スケジュール Course Schedule
第1日（Day1）
Descriptive Statistics - Tables, Graph and Numerical Measures●使用するケース
Case discussion: Descriptive Statistics - Tables, Graph and Numerical Measures (from the instructor)第2日（Day2）
Random Variables and Their Distributions●使用するケース
Case discussion: Random Variables and Their Distributions (from the instructor)第3日（Day3）
Numerical Characteristics of Random Variables●使用するケース
Case discussion: Numerical Characteristics of Random Variables (from the instructor)第4日（Day4）
Normal Distribution and T Distribution●使用するケース
Case discussion: Normal Distribution and T Distribution (from the instructor)第5日（Day5）
Central Limit Theorem●使用するケース
Case discussion: Central Limit Theorem (from the instructor)第6日（Day6）
Confidence Interval●使用するケース
Case discussion: Confidence Interval (from the instructor)第7日（Day7）
Hypothesis Testing●使用するケース
Case discussion: Hypothesis Testing (from the instructor)Note: This is a tentative list, and the teaching content and progress as well as the cases to be used may be adjusted according to the actual situation.
成績評価方法 Evaluation Criteria
*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
| 講師用内規準拠 Method of Assessment | Weights |
|---|---|
| コールドコール Cold Call | 0 % |
| 授業内での挙手発言 Class Contribution | 50 % |
| クラス貢献度合計 Class Contribution Total | 50 % |
| 予習レポート Preparation Report | 10 % |
| 小テスト Quizzes / Tests | 0 % |
| シミュレーション成績 Simulation | 0 % |
| ケース試験 Case Exam | 0 % |
| 最終レポート Final Report | 0 % |
| 期末試験 Final Exam | 40 % |
| 参加者による相互評価 Peer Assessment | 0 % |
| 合計 Total | 100 % |
評価の留意事項 Notes on Evaluation Criteria
教科書 Textbook
- Sharpe, DeVeaux and Velleman「Business Statistics」Pearson（2015）
参考文献・資料 Additional Readings and Resource
Notes and case studies will be provided. Textbooks are for reference only.
授業調査に対するコメント Comment on Course Evaluation
This will be the first time the instructor teaches this course at NUCB.
担当教員のプロフィール About the Instructor
Dr. Xinyang Wei is a Professor at Nagoya University of Commerce and Business (NUCB) and holds a Ph.D. in Economics from the University of New South Wales, Sydney. His research focuses on energy and environmental economics, with particular interests in policy evaluation, climate policy, and low-carbon development. His research has been recognized through several awards, including the Herbert Smith Freehills Law and Economics Higher Degree Research Award and the Outstanding Research Award from the Kurimoto Educational Institute. His work has been published in leading international journals, including Energy Economics, Renewable and Sustainable Energy Reviews, Journal of Environmental Management, International Review of Economics and Finance, Energy, Renewable Energy, and Economic Analysis and Policy.
（実務経験 Work experience）
Before joining Nagoya University of Commerce and Business, Dr. Wei held academic positions at the University of New South Wales and the Macau University of Science and Technology, where he developed extensive experience in teaching and research. He has supervised undergraduate, master's, and doctoral students and taught courses in Business Statistics, Data Analysis, Financial Data Analysis, Econometrics, Financial Econometrics, Financial Risk Management, and Research Methodology. During his appointment in Macau, he received First Prize in the University Teaching Achievement Award in recognition of his teaching excellence.
Refereed Articles
- (2026) How renewable energy innovation policies reshape industrial systems: Cross-sectoral dynamics in China. Energy Strategy Reviews
- (2026) The Global Spillover Effects of China’s Renewable Energy Policies: Evidence from International Energy Stock Markets. Economic Analysis and Policy
- (2026) An enterprise information systems perspective on live-streaming mode selection: virtual vs. human influencers. Enterprise Information Systems
- (2026) Energy Technology Innovation Policies and Sectoral Performance in China: Evidence from Financial Market Transmission. International Review of Economics & Finance
- (2026) Spillover Effects of EU Climate Policy on Global Financial Systems: Evidence from the Emissions Trading Scheme. Sustainable Development