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.

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.