Teaching Introductory Data Analysis through Modeling

Intended for teachers of introductory statistics in colleges and universities, this one-day workshop is offered on Sunday January 4, 2009. This workshop is supported by SIGMAA-Stat Ed and has been selected as an MAA Ancillary Workshop. It will precede the Joint Mathematics Meeting in Washington, DC which begins the next day.

This hands-on workshop will present a new way of teaching introductory data analysis that gives a central role to modeling techniques. Modeling provides a strong unifying framework for statistics and at the same time ties statistics closely to the scientific method and the demands of realistic multi-variable data. The workshop will introduce the ways in which models can be used for description, the interpretation of models in terms of association, change, and partial change (that is, change in one variable while holding others constant). In place of the usual matrix-based theory of linear models, the workshop will present a geometrical approach to theory that is accessible to introductory students and fully illuminates important ideas in data analysis: fitting, confounding and Simpson's paradox, correlation and collinearity. Inference is introduced using resampling and simulation, from which it is straightforward to transition to a general framework for inference, analysis of covariance. Computation (using the free package R) will feature prominently in hands-on activities; participants should bring laptop computers if possible. Participants do NOT need to have previous experience with R or with statistical modeling. Our students can learn it and so can you! For more information, see www.macalester.edu/~kaplan/JMM2009

About the Presenters

Daniel (Danny) Kaplan teaches applied math, scientific computing, and statistics at Macalester College. He came to statistics late in his career, after graduate studies in economics and biomedical engineering and research work in mathematical physiology. Over the past five years, he has been developing new approaches to teaching introductory calculus and statistics based on the idea that mathematics can provide the most "value added" when it comes to multivariable ideas. In 2006, he won Macalester's annual Excellence in Teaching award. The approach featured in the workshop was developed with the support of the Howard Hughes Medical Institute and a major grant from the Keck Foundation.

Vittorio (Victor) Addona is an assistant professor in the Department of Mathematics and Computer Science at Macalester College in Saint Paul, Minnesota. He completed his PhD in statistics in 2005 at McGill University in Montreal, Canada. Vittorio's research interests are in survival analysis, other medical applications of statistics, and sports statistics. He has also recently become interested in statistically based post-election audit models. Vittorio teaches courses across the statistics curriculum, including the Introduction to Statistical Modeling course presented in this workshop. He helped establish a new major in Applied Mathematics and Statistics at Macalester College.

Workshop Logistics

Sunday, January 4, 2009, 8:30am - 5:00pm at the Marriott Wardman Park Hotel located at 2660 Woodley Road, NW Washington, District Of Columbia. There is no registration fee for this workshop. Workshop materials and lunch during the workshop will be provided. Workshop participants are encouraged to bring their own laptops. Workshop participants are responsible for their own transportation and lodging. Be sure to register for JMM and book your rooms early through their lodging service to obtain conference rates for your JMM stay.

Expectations

Please note: CAUSEway workshops receive principal funding from a National Science Foundation grant. As part of that award, Science and Mathematics Program Improvement (SAMPI) at Western Michigan University will be conducting an independent evaluation of all CAUSEway activities and workshop participants are expected to fully participate in this evaluation.