シラバス Syllabus

授業名 Data Analysis
Course Title Data Analysis
担当教員 Instructor Name Xinyang Wei
コード Couse Code GLP260_G22N
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
学位 Degree MSc in Management
開講情報 Terms / Location 2022 GSM Nagoya Fall

授業の概要 Course Overview


Aligning with the NUCB Business School's mission, this course will assist participants in acquiring creative thinking, an exploratory attitude, and data analytical skills to solve practical economic and management problems.
In this course, students will learn how to apply linear regression models to explore and estimate economic and managerial relationships. Course topics include statistics review, simple linear regression, multiple regression estimation, multiple regression inference. Extensions covering statistical complications such as incorporating nonlinearities in linear regression and qualitative information: binary (or dummy) variables will also be included. Students will learn to solve data analysis problems in a Stata environment.
Participants will deepen their understanding of the fundamental methods of quantitative analysis 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
LG7 Global Perspective (GLP)

受講後得られる具体的スキルや知識 Learning Outcomes


After completing the course, participants are expected to demonstrate
• analytical skills through using real data and information and applying appropriate quantitative analysis methods.
• the capability to establish quantitative analysis frameworks and use Stata to analyze data.
• the ability to construct logical and professional written work relevant to data analysis and communicate ideas in a succinct and clear manner.

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 %

学習方法、レポート、課題に対するフィードバック方法 Course Approach, Report, Feedback methods

Course Prerequisites
• It is desirable that participants have a basic understanding of statistic topics, such as normal distribution, distribution of the sample mean, Central Limit Theorem, confidence interval, hypothesis testing and conditional expectation. These can be found in the corresponding sections of Math Refresher B & C in the appendix of the textbook.
• A laptop computer with Stata installed is required for data analysis and case studies throughout the course. Stata 14 or above editions are acceptable. BE, SE and MP editions can all satisfy the requirements of the course. Introductory support will be provided by the instructor for those students who have not used Stata before.
• It is recommended that participants spend at least 3 hours of preparation time on each case, including the fundamental knowledge provided in the casebook. Participants who wish to gain more in-depth insights may read the relevant chapters of the textbook, but the course will be based on the content in the casebook.

Class Discussion
• Through class discussions and the instructor's comments, participants will understand the principles of quantitative analysis methods. In the subsequent case study, participants will be able to apply these methods to solve real-world economic and management problems.
• The key points and difficulties in the fundamental knowledge and case study will be fully discussed, and the steps of case analysis and data analysis will also be discussed in detail.
• Please note that the purpose of this course is to develop the participants' data analysis and hands-on skills. The focus of this course is not on mathematical derivations, although they may appear in the casebook.

Case Exam
• The cases and exam questions will be provided in class on Day 4.
• Duration: 3 hours
• Submission Deadline: Before the end of the fourth day of the course
• Submission Method: Upload the report in pdf format via google classroom
• Note: Screenshots of the data analysis process and results of Stata need to be attached and submitted together with the analysis of the cases.

授業スケジュール Course Schedule

第1日(Day1)

Morning: The Simple Linear Regression Model
Afternoon: Multiple Regression Analysis: Estimation


●使用するケース
Morning: Marketing Strategies in Charity Management (Wooldridge, J. M., 2019. Introductory econometrics: A modern approach. Cengage learning.)
Afternoon: Housing Price Regression (Wooldridge, J. M., 2019. Introductory econometrics: A modern approach. Cengage learning.)

第2日(Day2)

Morning: Multiple Regression Analysis: Inference
Afternoon: Incorporating Nonlinearities in Linear Regression: Logarithmic Transformation


●使用するケース
Morning: CEO Salary and Return on Equity (Wooldridge, J. M., 2019. Introductory econometrics: A modern approach. Cengage learning.)
Afternoon: Wage and Education (Wooldridge, J. M., 2019. Introductory econometrics: A modern approach. Cengage learning.)

第3日(Day3)

Morning: Incorporating Nonlinearities in Linear Regression: Quadratic Term
Afternoon: Multiple Regression Analysis with Qualitative Information: A Single Dummy Independent Variable and Interaction Involving Dummy Variables


●使用するケース
Morning: Wage and Experience (Wooldridge, J. M., 2019. Introductory econometrics: A modern approach. Cengage learning.)
Afternoon: Gender Pay Gap Assessment (Wooldridge, J. M., 2019. Introductory econometrics: A modern approach. Cengage learning.)

第4日(Day4)

Morning: Multiple Regression Analysis with Qualitative Information: Using Dummy Variables for Multiple Categories
Afternoon: Case Exam


●使用するケース
Morning: Marriage and Gender: The Impacts on Wages (Wooldridge, J. M., 2019. Introductory econometrics: A modern approach. Cengage learning.)

Note: This is a tentative list and cases to be used might vary.

第5日(Day5)



第6日(Day6)



第7日(Day7)



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。
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 0 %
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 50 %
最終レポート Final Report 0 %
期末試験 Final Exam 0 %
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

The distribution of grades will comply with the school's curve grading policy.

使用ケース一覧 List of Cases

    ケースは使用しません。

教科書 Textbook

  • Jeffrey M. Wooldridge「Introductory Econometrics: A Modern Approach」Cengage learning(2020)

参考文献・資料 Additional Readings and Resource

Previous versions of the textbook may also be used for this course.

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 an Associate Professor at NUCB with a PhD in Economics from the University of New South Wales, Sydney. His research delves into intricate aspects of energy and environmental economics, with a focus on policy evaluation, climate change dynamics, and the pursuit of low-carbon development. Recognised for his exemplary research, he was granted the Herbert Smith Freehills Law and Economics Higher Degree Research Award. His scholarly contributions are reflected in publications across renowned academic journals, including Energy Economics, Energy, Renewable Energy, Renewable and Sustainable Energy Reviews, International Journal of Energy Research, and the Journal of Industrial Ecology.

(実務経験 Work experience)


Before joining NUCB, he accumulated enriching teaching and research experiences at both the University of New South Wales and the Macau University of Science and Technology. He possesses a profound background in supervising undergraduate, master's, and PhD theses, and has a versatile teaching portfolio spanning courses like Business Statistics, Data Analysis, Financial Data Analysis, Econometrics, Intermediate Econometrics, Financial Statistics and Econometrics, Financial Risk Management and Research Methodology. His dedication to excellence in education was recognised in Macau with the First Prize in the University Teaching Achievement Award.

Refereed Articles

  • (2023) Study on the spatial spillover effect and path mechanism of green finance development on China's energy structure transformation. Journal of Cleaner Production
  • (2023) Effect of green finance reform and innovation pilot zone on improving environmental pollution: an empirical evidence from Chinese cities. Environmental Science and Pollution Research
  • (2023) The Impact of Fintech Development on Air Pollution. International Journal of Environmental Research and Public Health
  • (2022) Multi-scenario simulation on reducing CO2 emissions from China's major manufacturing industries targeting 2060. Journal of Industrial Ecology
  • (2022) Evaluation of contagious effects of China's wind power industrial policies. Energy






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