A Project-Driven Second Course

Abstract

I trace the development of a new course in modern data analysis involving a wide spectrum of statistical techniques. Because the course is based entirely on case studies, real-data settings, and student projects and is computer-intensive, a series of challenges facing many instructors are addressed. In a single semester, students explore data using tools from EDA, multiple regression, analysis of variance, time series analysis, and categorical data analysis. The focus is on understanding and forecasting in a variety of data settings, learning how to summarize relationships and measure how well these relationships fit data, and how to make meaningful statistical inferences when the usual assumptions do not hold. The course emphasizes what the statistical process is all about: how to conduct studies, what the results mean, and what can be inferred about the whole from pieces of evidence.

• Active learning
• Data analysis
• Data collection
• Problem-based learning

Acknowledgments

An earlier draft of some of the material in this paper was presented to the Section on Statistical Education of the American Statistical Association at the 1996 Joint Statistical Meetings in Chicago. The author wishes to thank the editor and three anonymous referees for their comments which improved the presentation of this paper.