Beyond Traditional Statistical Methods

Abstract

Today's courses in statistical methods, for the most part, focus on the same methods that were taught 30 years ago. The actual practice of statistics has moved beyond these traditional statistical methods. Modern methods including dynamic graphics, nonlinear estimation, resampling and other simulation based inference methods are being used by many scientists and engineers. However, these methods generally are not included in courses in statistical methods, especially at the undergraduate level. This paper will discuss the development of a collection of instructional modules, built around actual applications from science and engineering. Each module is self-contained and includes instructional materials such as: objectives, examples, lecture materials, computer implementation of the methodology, homework, class/discussion exercises and assignments. The modules are intended as a resource for instructors to experiment with and explore the use of modern statistical methodology in undergraduate statistics methods courses. Two of the modules will be presented in some detail. We will also discuss the use of the modules in a new course that goes beyond our traditional methods courses.

Relative Risk Regression for Binary Outcomes: Methods and Recommendations

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