115年第1學期-2767 Business Statistics 課程資訊

評分方式

評分項目	配分比例	說明
Midterm Presentation	25	Group presentation interpreting a business dataset using statistical concepts, AI scaffolded analysis, and evidence-based explanation.
Final Data Analysis Project	30	Group project applying statistical methods to a business dataset and presenting practical business recommendations.
Learning Activities	25	Individual Excel practice, problem-solving tasks, AI scaffolded worksheets, interpretation logs, transfer tasks, and Moodle submissions.
Final Examination	20	Individual final exam covering key statistical concepts, calculations, interpretation, and business application questions.

教育目標

This course introduces students to the fundamental concepts and applications of business statistics for evidence-based decision-making. Students will learn how to collect, organize, summarize, analyze, interpret, and present data in business contexts. Major topics include data collection, data visualization, descriptive statistics, probability, probability distributions, normal distribution, sampling theory, sampling distributions, confidence interval estimation, and hypothesis testing. The course emphasizes both statistical understanding and practical interpretation rather than mechanical calculation alone. Through lectures, problem-solving exercises, data interpretation activities, AI scaffolded learning tasks, and applied business data projects, students will learn to use statistical reasoning to support managerial decisions. Students will also use AI tools responsibly to clarify statistical concepts, evaluate AI generated explanations, connect outputs to statistical rules, revise interpretations, and apply statistical thinking to real business problems.

基本資料

必修課，學分數：3-0
上課時間：四/2,3,4
修課班級：國經學程2
修課年級：2年級以上
選課備註：單號學生修習。

參考書目

Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. J., Fry, M. J., and Ohlmann, J. W. (2020). Statistics for Business and Economics (14th ed., Metric Version). Cengage Learning.

Supplementary materials: instructor-prepared business datasets, Excel-based practice files, AI scaffolded statistics worksheets, AI output evaluation sheets, data interpretation logs, business cases, and Moodle learning materials.