| 授業名 | Quantitative Analysis |
|---|---|
| Course Title | Quantitative Analysis |
| 担当教員 Instructor Name | Xinyang Wei |
| コード Couse Code | NUC439_N23B |
| 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 Term4 |
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 quantitative analytical skills to solve practical economic and management problems.
In this course, students will learn how to apply more advanced regression models to explore and estimate economic and managerial relationships. Course topics include statistical complications such as multicollinearity, incorporating nonlinearities in linear regression and qualitative information: binary (or dummy) variables and data issues such as regression with panel data will also be included. Practical computer applications feature throughout. 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
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
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.
• 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
1. It is desirable that participants have a basic understanding of statistical 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.
2. 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.
3. It is recommended that participants spend at least 3 hours of preparation time on each case, including the fundamental knowledge provided in the lecture notes. 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 lecture notes.
Class Discussion
1. 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.
2. 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.
3. 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 lecture notes.
1. It is desirable that participants have a basic understanding of statistical 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.
2. 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.
3. It is recommended that participants spend at least 3 hours of preparation time on each case, including the fundamental knowledge provided in the lecture notes. 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 lecture notes.
Class Discussion
1. 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.
2. 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.
3. 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 lecture notes.
授業スケジュール Course Schedule
第1日（Day1）
Covariance, Correlation Coefficient and Conditional Expectation●使用するケース
Case discussion: Covariance, Correlation Coefficient and Conditional Expectation (from the instructor)第2日（Day2）
Simple Linear Regress and Ordinary Least Squares (OLS) Method●使用するケース
Case discussion: Simple Linear Regress and Ordinary Least Squares (OLS) Method (from the instructor)第3日（Day3）
Goodness-of-fit Measure for Linear Regression Models●使用するケース
Case discussion: Goodness-of-fit Measure for Linear Regression Models (from the instructor)第4日（Day4）
Multiple Linear Regression Analysis: Estimation●使用するケース
Case discussion: Multiple Linear Regression Analysis: Estimation (from the instructor)第5日（Day5）
Multiple Linear Regression Analysis: Hypothesis Tests for a Single Coefficient●使用するケース
Case discussion: Multiple Linear Regression Analysis: Hypothesis Tests for a Single Coefficient (from the instructor)第6日（Day6）
Multiple Linear Regression Analysis: Joint Hypothesis Testing●使用するケース
Case discussion: Multiple Linear Regression Analysis: Joint Hypothesis Testing (from the instructor)第7日（Day7）
Review●使用するケース
Case discussion: Review Case (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 | 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 | 0 % |
| 最終レポート Final Report | 50 % |
| 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
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