| 授業名 | Introduction to BBA Spring |
|---|---|
| Course Title | Introduction to BBA Spring |
| 担当教員 Instructor Name | Xinyang Wei |
| コード Couse Code | NUC439_N24A |
| 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 | 2024 UG Nisshin Term1 |
授業の概要 Course Overview
Misson Statementとの関係性 / Connection to our Mission Statement
In the ever-evolving landscape of business and society, the role of statistics transcends mere data analysis; it embodies the critical process of gathering, organizing, and interpreting data to enhance decision-making and predictive accuracy. This pivotal field stands at the core of understanding complex patterns and trends within datasets, empowering us to make informed predictions about future events and behaviors. Our course is meticulously designed to intertwine with our Mission Statement, aiming to nurture future business leaders who are not only innovative and ethical but also equipped with a robust "Frontier Spirit." By embedding statistical foundations into the Global BBA program, we endeavor to create a knowledge base that propels modern business and society forward, fostering a new generation of leaders ready to navigate the challenges of tomorrow with confidence and acumen.
授業の目的（意義） / Importance of this course
This course is crafted as a vital methodology pillar within the Global BBA program, laying down the statistical bedrock upon which other courses will build. Through an immersive journey into both descriptive and inferential statistics, we delve into the core of statistical tools, encompassing point and interval estimation, hypothesis testing, and more. The curriculum is enriched with real-world case studies, engaging discussions, and dynamic interactive activities, ensuring a deep and practical understanding of statistical applications in business. Our goal is to equip students with not just theoretical knowledge, but also the practical skills to apply statistical analysis effectively in their future endeavors.
到達目標 / Achievement Goal
Our ambition extends beyond the confines of traditional education. We aim for participants to emerge with a profound grasp of statistical methods as they apply to economics and management. This course is designed to empower students to not only comprehend but also to apply these methods in dissecting economic and management issues. By the end of this journey, participants will be adept at leveraging statistical tools to unravel complex problems, craft innovative solutions, and make decisions grounded in solid empirical evidence. Through this transformative experience, we are committed to molding individuals who are not just spectators but active contributors to the advancement of their fields, embodying the true spirit of innovative and ethical leadership in the face of global challenges.
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 successful completion of this course, students will be able to:
1 - Understand and Apply Descriptive Statistics: Students will gain a comprehensive understanding of descriptive statistics, including the creation and interpretation of tables, graphs, and Excel applications. They will be proficient in summarizing and describing the essential features of a dataset.
2 - Master Numerical Measures and Excel for Descriptive Statistics: Participants will learn to calculate and interpret numerical measures of data distribution such as mean, median, mode, variance, and standard deviation, using Excel as a primary tool.
3 - Analyze Probability Distributions for Discrete Random Variables: Learners will understand and apply probability distributions, including the computation of means and standard deviations for discrete random variables.
4 - Conduct Probability Computations for General Normal Random Variables: Students will be adept at performing probability computations using the normal distribution.
5 - Apply the Central Limit Theorem: Participants will comprehend and utilize the Central Limit Theorem in understanding sample variability.
6 - Construct and Interpret Large Sample Confidence Intervals: Learners will be equipped to construct and interpret confidence intervals for population means.
7 - Conduct Large Sample Hypothesis Tests: Students will develop the skill to perform hypothesis testing concerning a single population mean.
8 - Critical Thinking and Decision Making: Through the application of statistical methods, students will enhance their critical thinking and decision-making skills, learning to make informed decisions based on statistical analysis.
9 - Statistical Software Proficiency: Participants will gain hands-on experience with Excel and other statistical software, enabling them to perform complex statistical analyses efficiently.
10 - Ethical Consideration in Data Analysis: Students will understand the ethical considerations in gathering, analyzing, and interpreting data, ensuring responsible use of statistics in business and research.
By the end of this course, participants will be well-prepared to leverage statistical tools and concepts to address challenges in business, economics, and management, aligning with the mission to educate future leaders with a pioneering spirit.
1 - Understand and Apply Descriptive Statistics: Students will gain a comprehensive understanding of descriptive statistics, including the creation and interpretation of tables, graphs, and Excel applications. They will be proficient in summarizing and describing the essential features of a dataset.
2 - Master Numerical Measures and Excel for Descriptive Statistics: Participants will learn to calculate and interpret numerical measures of data distribution such as mean, median, mode, variance, and standard deviation, using Excel as a primary tool.
3 - Analyze Probability Distributions for Discrete Random Variables: Learners will understand and apply probability distributions, including the computation of means and standard deviations for discrete random variables.
4 - Conduct Probability Computations for General Normal Random Variables: Students will be adept at performing probability computations using the normal distribution.
5 - Apply the Central Limit Theorem: Participants will comprehend and utilize the Central Limit Theorem in understanding sample variability.
6 - Construct and Interpret Large Sample Confidence Intervals: Learners will be equipped to construct and interpret confidence intervals for population means.
7 - Conduct Large Sample Hypothesis Tests: Students will develop the skill to perform hypothesis testing concerning a single population mean.
8 - Critical Thinking and Decision Making: Through the application of statistical methods, students will enhance their critical thinking and decision-making skills, learning to make informed decisions based on statistical analysis.
9 - Statistical Software Proficiency: Participants will gain hands-on experience with Excel and other statistical software, enabling them to perform complex statistical analyses efficiently.
10 - Ethical Consideration in Data Analysis: Students will understand the ethical considerations in gathering, analyzing, and interpreting data, ensuring responsible use of statistics in business and research.
By the end of this course, participants will be well-prepared to leverage statistical tools and concepts to address challenges in business, economics, and management, aligning with the mission to educate future leaders with a pioneering spirit.
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. For the successful completion of this course, participants are required to have access to a computer with Microsoft Excel installed. Excel will be indispensable for engaging in hands-on statistical exercises, data analysis, and case study evaluations. Familiarity with Excel's basic functions is recommended to navigate through the course material effectively.
2. A commitment of at least three hours of pre-class preparation for each session is essential. This preparation includes reviewing assigned reading materials, completing any exercises provided before class, and familiarizing oneself with the statistical concepts and case studies that will be explored during the sessions.
Class Discussion
1. This course emphasizes a highly interactive approach, with class discussions playing a central role in deepening understanding of statistical principles. These discussions are designed to bridge the gap between theoretical knowledge and its application in analyzing real-world data and decision-making scenarios.
2. Key statistical concepts, methodologies, and the nuances of different case studies will be explored in detail through collaborative class discussions. The instructor will facilitate these discussions, guiding participants through the critical thinking and analytical processes necessary for effective data analysis and interpretation.
Feedback Methods
1. Regular quizzes will be an integral part of the course structure to assess participants' understanding of the material and track their progress. Feedback will be provided after these quizzes to highlight areas of strength and opportunities for improvement.
2. Constructive feedback sessions will form a key element of the course, enabling participants to engage in one-on-one discussions with the instructor. These sessions are an opportunity for participants to review their performance, clarify any uncertainties, and receive tailored advice to enhance their learning journey.
1. For the successful completion of this course, participants are required to have access to a computer with Microsoft Excel installed. Excel will be indispensable for engaging in hands-on statistical exercises, data analysis, and case study evaluations. Familiarity with Excel's basic functions is recommended to navigate through the course material effectively.
2. A commitment of at least three hours of pre-class preparation for each session is essential. This preparation includes reviewing assigned reading materials, completing any exercises provided before class, and familiarizing oneself with the statistical concepts and case studies that will be explored during the sessions.
Class Discussion
1. This course emphasizes a highly interactive approach, with class discussions playing a central role in deepening understanding of statistical principles. These discussions are designed to bridge the gap between theoretical knowledge and its application in analyzing real-world data and decision-making scenarios.
2. Key statistical concepts, methodologies, and the nuances of different case studies will be explored in detail through collaborative class discussions. The instructor will facilitate these discussions, guiding participants through the critical thinking and analytical processes necessary for effective data analysis and interpretation.
Feedback Methods
1. Regular quizzes will be an integral part of the course structure to assess participants' understanding of the material and track their progress. Feedback will be provided after these quizzes to highlight areas of strength and opportunities for improvement.
2. Constructive feedback sessions will form a key element of the course, enabling participants to engage in one-on-one discussions with the instructor. These sessions are an opportunity for participants to review their performance, clarify any uncertainties, and receive tailored advice to enhance their learning journey.
授業スケジュール Course Schedule
第1日（Day1）
Descriptive Statistics - Tables, Graphs, and Excel Applications●使用するケース
City Delights: A Comprehensive Exploration of Sales Data, Customer Preferences, and Pricing Strategies in Urban Retail Part 1第2日（Day2）
Descriptive Statistics - Numerical Measures and Excel Applications●使用するケース
City Delights: A Comprehensive Exploration of Sales Data, Customer Preferences, and Pricing Strategies in Urban Retail Part 2第3日（Day3）
Probability Distributions for Discrete Random Variables●使用するケース
Analyzing Daily Order Quantities of a Company第4日（Day4）
Probability Distributions for Continuous Random Variables●使用するケース
Variability in Beverage Canning - A Statistical Analysis第5日（Day5）
Sampling Distributions●使用するケース
Understanding Sample Variability in Grade Point Averages (GPAs) at Prestige College第6日（Day6）
Large Sample Confidence Interval for a Population Mean●使用するケース
Academic Performance Analysis using GPA as a Metric第7日（Day7）
Large Sample Hypothesis Tests Concerning a Single Population Mean●使用するケース
Recalibrating the Cosmetics Dispensing Machine: A Statistical Approach to Quality ControlNote: This list is provisional, and both the instructional materials 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 | 40 % |
| クラス貢献度合計 Class Contribution Total | 40 % |
| 予習レポート Preparation Report | 10 % |
| 小テスト Quizzes / Tests | 10 % |
| 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
- Sharpe, DeVeaux and Velleman「Business Statistics」Pearson（2015）
- Douglas S. Shafer and Zhiyi Zhang「Beginning Statistics」Phyllis Barnidge Publisher（2012）
参考文献・資料 Additional Readings and Resource
Previous versions of the textbooks are also acceptable for use in this course.
授業調査に対するコメント Comment on Course Evaluation
The course structure and content will be refined and updated based on feedback and recommendations from previous participants.
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