| 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
Misson Statementとの関係性 / Connection to our Mission Statement
This course, aligned with the NUCB Business School's mission, is an intellectual journey designed to equip participants with creative thinking, an exploratory mindset, and robust quantitative analytical skills essential for tackling real-world economic and management challenges. Over the span of seven days, this scholarly odyssey unfolds, with numbers as narrators of reality, illuminating the intricate relationship between data and decision-making. From the foundational principles of covariance to the complex dynamics of multivariate regression, students will navigate through the rich landscape of statistical analysis. Our mission is to endow inquisitive individuals with the statistical prowess necessary to craft informed, data-driven choices across diverse realms such as global finance, educational excellence, and personal wealth and housing. This transformative experience promises not only mathematical rigor but also a profound integration of statistical insights with the narratives of daily life, marking a pivotal shift in the participants' academic and professional journey.
授業の目的（意義） / Importance of this course
The significance of this course lies in its rigorous and comprehensive exploration of statistical analysis within the context of economic and management challenges. Spanning seven weeks, it meticulously demystifies the quantitative methodologies that underpin sound decision-making in various sectors. Starting with the foundational understanding of data dynamics and covariance, students are led through progressively complex analyses, culminating in multivariate regression and joint hypothesis testing.
By examining case studies ranging from global finance to the intricacies of the housing market, participants gain not only theoretical knowledge but also practical insights into the data-driven mechanisms that govern these areas. This course is pivotal for students aiming to harness the power of statistical evidence in predicting outcomes and optimizing strategies. It equips future leaders with the analytical acuity to dissect and influence the economic and social fabrics of their respective fields, making it an indispensable part of their academic and professional arsenal.
By examining case studies ranging from global finance to the intricacies of the housing market, participants gain not only theoretical knowledge but also practical insights into the data-driven mechanisms that govern these areas. This course is pivotal for students aiming to harness the power of statistical evidence in predicting outcomes and optimizing strategies. It equips future leaders with the analytical acuity to dissect and influence the economic and social fabrics of their respective fields, making it an indispensable part of their academic and professional arsenal.
到達目標 / Achievement Goal
The Achievement Goal of this comprehensive course is to endow participants with a deep understanding and practical application of advanced statistical analysis. By the end of the course, students will be expected to possess the capability to apply statistical reasoning and techniques to a broad range of real-world challenges, enabling them to make data-driven decisions with confidence.
The course is designed not just to impart knowledge, but to foster a mindset of inquiry and the ability to think critically about information and trends. Students will learn to question, analyze, and draw meaningful conclusions from data, which is an invaluable skill in any professional or academic setting.
Ultimately, the course aims to produce proficient individuals who are not only adept at handling numerical data but also at synthesizing quantitative insights with qualitative context to inform strategy and policy. The successful participant will leave with a toolkit of skills that are applicable across various sectors and industries, prepared to make a significant impact in their chosen fields.
The course is designed not just to impart knowledge, but to foster a mindset of inquiry and the ability to think critically about information and trends. Students will learn to question, analyze, and draw meaningful conclusions from data, which is an invaluable skill in any professional or academic setting.
Ultimately, the course aims to produce proficient individuals who are not only adept at handling numerical data but also at synthesizing quantitative insights with qualitative context to inform strategy and policy. The successful participant will leave with a toolkit of skills that are applicable across various sectors and industries, prepared to make a significant impact in their chosen fields.
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
Upon completion of this comprehensive six-week course, participants will be expected to have honed a robust set of skills that merge theoretical understanding with practical application, leading to the following outcomes:
1. Integrated Data Analysis and Decision-Making: Participants will understand how strategic decisions are influenced by data analysis across sectors like finance, education, and personal wealth management, recognizing the interplay of covariance, conditional expectations, and other data dynamics.
2. Proficiency in Statistical Methods: Students will adeptly apply regression basics, Ordinary Least Squares (OLS) techniques, and hypothesis testing within multivariate regression frameworks, utilizing real-world data to interpret and validate economic and financial models.
3. Critical Evaluation of Data: Through the critical assessment of goodness-of-fit measures for linear regression models, participants will ensure the accuracy and relevance of data analysis outcomes, distinguishing effectively between correlation and causation.
4. Multidimensional Analysis Capabilities: The course will enable participants to conduct sophisticated multi-dimensional estimations in linear regression and joint hypothesis testing, accounting for complex variable interactions.
5. Quantitative Analysis Framework Development: Participants will be capable of establishing robust quantitative analysis frameworks and leveraging tools like Excel for detailed data examination and interpretation.
6. Synthesis of Quantitative and Qualitative Insights: Graduates of the course will integrate quantitative findings with real-world contexts to provide comprehensive insights into complex issues, sensitive to unique scenarios and nuances.
7. Analytical and Communication Skills: With enhanced analytical skills for practical economic and management problem-solving, participants will demonstrate the ability to construct logical, professional, and relevant written work, communicating ideas succinctly and clearly.
These outcomes will endow participants with a deep analytical acumen, not only in terms of mathematical rigor but also in the ability to connect statistical insights with the narratives of daily life, ultimately shaping their academic and professional trajectories.
1. Integrated Data Analysis and Decision-Making: Participants will understand how strategic decisions are influenced by data analysis across sectors like finance, education, and personal wealth management, recognizing the interplay of covariance, conditional expectations, and other data dynamics.
2. Proficiency in Statistical Methods: Students will adeptly apply regression basics, Ordinary Least Squares (OLS) techniques, and hypothesis testing within multivariate regression frameworks, utilizing real-world data to interpret and validate economic and financial models.
3. Critical Evaluation of Data: Through the critical assessment of goodness-of-fit measures for linear regression models, participants will ensure the accuracy and relevance of data analysis outcomes, distinguishing effectively between correlation and causation.
4. Multidimensional Analysis Capabilities: The course will enable participants to conduct sophisticated multi-dimensional estimations in linear regression and joint hypothesis testing, accounting for complex variable interactions.
5. Quantitative Analysis Framework Development: Participants will be capable of establishing robust quantitative analysis frameworks and leveraging tools like Excel for detailed data examination and interpretation.
6. Synthesis of Quantitative and Qualitative Insights: Graduates of the course will integrate quantitative findings with real-world contexts to provide comprehensive insights into complex issues, sensitive to unique scenarios and nuances.
7. Analytical and Communication Skills: With enhanced analytical skills for practical economic and management problem-solving, participants will demonstrate the ability to construct logical, professional, and relevant written work, communicating ideas succinctly and clearly.
These outcomes will endow participants with a deep analytical acumen, not only in terms of mathematical rigor but also in the ability to connect statistical insights with the narratives of daily life, ultimately shaping their academic and professional trajectories.
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
Course Prerequisites
1. Participants are encouraged to have a basic understanding of statistical concepts, including normal distribution, the distribution of the sample mean, the Central Limit Theorem, confidence intervals, hypothesis testing, and conditional expectation. These topics can be reviewed in the Math Refresher B & C sections found in the textbook's appendix.
2. A laptop computer with Microsoft Excel installed is required for data analysis and engaging in case studies throughout the course.
3. Participants are recommended to dedicate at least 3 hours to prepare for each case, which includes reviewing the fundamental knowledge provided in the lecture notes or other designated case materials.
Class Discussion
1. Class discussions and the instructor's feedback will facilitate participants' understanding of quantitative analysis methods. Participants will then apply these methods in case studies to address real-world economic and management challenges.
2. Fundamental knowledge, key points, and difficulties encountered in the case studies will be thoroughly discussed. The procedural steps for case and data analysis will also be discussed in detail.
Feedback Methods
1. The course will feature regular quizzes to assess participant progress, with feedback provided to improve learning outcomes and ensure mastery of concepts.
2. Constructive feedback will be a cornerstone of the learning experience, offering chances for one-on-one consultations with the instructor. During these sessions, participants can discuss their individual progress, seek clarification on any doubts, and obtain personalized guidance.
1. Participants are encouraged to have a basic understanding of statistical concepts, including normal distribution, the distribution of the sample mean, the Central Limit Theorem, confidence intervals, hypothesis testing, and conditional expectation. These topics can be reviewed in the Math Refresher B & C sections found in the textbook's appendix.
2. A laptop computer with Microsoft Excel installed is required for data analysis and engaging in case studies throughout the course.
3. Participants are recommended to dedicate at least 3 hours to prepare for each case, which includes reviewing the fundamental knowledge provided in the lecture notes or other designated case materials.
Class Discussion
1. Class discussions and the instructor's feedback will facilitate participants' understanding of quantitative analysis methods. Participants will then apply these methods in case studies to address real-world economic and management challenges.
2. Fundamental knowledge, key points, and difficulties encountered in the case studies will be thoroughly discussed. The procedural steps for case and data analysis will also be discussed in detail.
Feedback Methods
1. The course will feature regular quizzes to assess participant progress, with feedback provided to improve learning outcomes and ensure mastery of concepts.
2. Constructive feedback will be a cornerstone of the learning experience, offering chances for one-on-one consultations with the instructor. During these sessions, participants can discuss their individual progress, seek clarification on any doubts, and obtain personalized guidance.
授業スケジュール Course Schedule
第1日（Day1）
The Dialogue Within Data: A Deep Dive into Covariance and Correlation Coefficients●使用するケース
Analyzing Asset Relationships in Global Finance第2日（Day2）
The Practical Path of Conditional Expectation: From Theory to Data Analysis●使用するケース
Decoding Academic Excellence: The Interplay of SAT Scores and GPA at Prestige University第3日（Day3）
The Linear Tapestry: Regression Basics & the Elegance of OLS Techniques●使用するケース
Navigating Admissions: Unraveling the ACT-GPA Dynamic at Prestige University第4日（Day4）
Model Mastery: Goodness-of-fit Measure for Linear Regression Models●使用するケース
The 401(K) Conundrum: Employee Participation and Employer Generosity in America's Retirement Landscape第5日（Day5）
Beyond the Basics: Multi-dimensional Estimations in Linear Regression●使用するケース
Tales of Testing: Dissecting the Relationship Between SAT Scores, High School Performance, and College GPA第6日（Day6）
Deductive Analytics: Hypothesis Testing in Multivariate Regression●使用するケース
Wealth Dynamics in Solo Households: Analyzing the Interplay of Income and Age in the U.S. Financial Landscape第7日（Day7）
Summary and Review●使用するケース
Housing Valuation Dynamics: Deciphering the Rationality Behind Assessed Housing PricesNote: This list is provisional, and both the instructional material and pace, as well as the case studies utilized, are subject to change based on real-time circumstances.
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 | 40 % |
| 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）
- Dimitrios Asteriou and Stephen G. Hall「Applied Econometrics」Bloomsbury Publishing（2021）
参考文献・資料 Additional Readings and Resource
Previous versions of the textbook are also acceptable for use in this course.
授業調査に対するコメント Comment on Course Evaluation
This marks the instructor's inaugural teaching of 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