Aligned with the NUCB Business School's commitment to nurturing creative thinkers and explorative leaders, this course equips participants with robust data analytical skills essential for resolving practical challenges in economics and management. By embracing an analytical approach, students will hone their ability to dissect complex issues and craft innovative solutions, preparing them to lead with insight and strategic vision.

This course is designed to immerse students in the practical application of statistical and regression analysis within the context of economic and management decision-making. Throughout the course, participants will engage with a variety of topics starting from a foundational review of statistics, advancing through simple and multiple regression analysis, and exploring more complex statistical methods such as nonlinear transformations in regression models. The curriculum is crafted to ensure that students not only grasp theoretical concepts but also apply them practically using the Stata software to analyze real-world data.

By the end of this course, participants will have a profound understanding of the essential quantitative methods used in economics and management. They will develop the capability to construct and implement these methods to dissect and address real-world challenges effectively. This course aims to transform theoretical knowledge into practical skills, enabling students to conduct thorough data analyses and apply their findings to create actionable strategies for economic and management issues.

*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。

Upon successful completion of the course, participants are expected to demonstrate the following skills and knowledge:

• Analytical Proficiency: Participants will gain expertise in handling real-world data, applying rigorous quantitative methods to uncover economic and managerial insights. This includes the ability to dissect complex datasets and draw meaningful conclusions that are pertinent to business and economic strategies.

• Quantitative Analysis Frameworks: Students will develop the capability to design and implement robust quantitative frameworks for analysis. This involves using Stata, a powerful tool for data analysis, to manipulate data, perform regressions, and test hypotheses effectively, ensuring that their analyses are grounded in solid empirical evidence.

• Communication Skills: The ability to articulate findings and strategies clearly and professionally is crucial. Participants will learn to construct logical and impactful written reports and presentations that convey complex data in an accessible and actionable manner. This skill is vital for communicating analytical results to stakeholders who may not have a technical background but need clear, concise, and accurate information to make informed decisions.

These outcomes are designed to equip participants with the necessary tools to apply data-driven decision-making in real-world economic and management contexts, enhancing their ability to contribute effectively in various professional settings.

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 %

Course Prerequisites

• Statistical Background: It is highly recommended that participants possess a basic understanding of statistical concepts, including mean and variance, correlation coefficient, normal distribution, distribution of the sample mean, Central Limit Theorem, confidence intervals, and hypothesis testing. For those unfamiliar with these topics, detailed explanations are provided in the Math Refresher B & C sections of the appendix in the first textbook. Participants should review this material in advance to ensure a smooth start to the course.

• Technical Requirements: A laptop with Stata installed is required for data analysis and case studies throughout the course. Versions 14 or higher of Stata (BE, SE, and MP editions) are acceptable. Introductory support will be available for students new to Stata.

• Preparation Time: Participants are advised to allocate at least 3 hours of preparation time for each case. This preparation aims to familiarize students with the background, objectives, and significance of each case, as well as the foundational concepts and methods necessary for analysis.

Class Discussion

• Class discussions and instructor feedback will enhance participants' understanding of quantitative analysis methods, which they will then apply in case studies to tackle real-world economic and management issues. Key concepts, challenges, and procedural steps for case and data analysis will be thoroughly explored.

Case Exam

• Exam Schedule: Cases and exam questions will be distributed in class on Day 4.
• Duration: 2 hours.
• Submission Deadline: Before the end of Day 4.
• Submission Method: Submit reports in PDF format via Google Classroom.
• Additional Requirements: Submissions must include screenshots of the data analysis process and results from Stata, along with the case analysis.

*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 adhere to the school's curve grading policy.

• Jeffrey M. Wooldridge「Introductory Econometrics: A Modern Approach」Cengage learning（2020）
• Dimitrios Asteriou and Stephen G. Hall「Applied Econometrics」Bloomsbury Publishing（2021）

Textbooks provided are intended solely for reference purposes, and previous editions may also be utilized for this course.

The structure, contents, and methods of assessment will be refined based on feedback from previous participants.

（実務経験 Work experience）

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