Data analysis: An adjunct to mathematical statistics at Oberlin College

Witmer, J.

Are SAT scores useful predictors of success in college? I led a group of mathematics majors at Oberlin College in an exploration of this question as the core of a one-credit course, entitled Data Analysis, last year. This course, MATH 337, is an adjunct to the standard, junior-level, two semester sequence in probability and mathematical statistics that we offer each year at Oberlin. Unlike most statistics courses for mathematics majors, the Data Analysis course allows - indeed, it forces - students to "get their hands dirty" exploring real data and trying to answer real questions. Each year I select a set of data, such as the SAT data, to serve as the central focus of the course. I believe that it is imperative that student learn something of how statistical theory is applied in practice and I try to show this side of statistics in the courses I teach at all levels. However, it is particularly difficult to cover much material on applied statistics while at the same time covering the many important topics in the mathematical statistics course - probability, random variables, functions of random variables, expectation, the central limit theorem, estimators, confidence intervals, hypothesis testing, and others - that are fundamental to the discipline and are an essential foundation for advanced (graduate) training in statistics. The Data Analysis course provides a workable solution to this problem.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education