CAUSE

Creative Commons License
The content of this website is available for use under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License unless otherwise noted.

NSF

NSDL

CWIS

 

CAUSEweb.org

 

Search:
Advanced Search: Resource Library or Literature Index  

 Navigation: CAUSEwebWorkshopsHesterberg


Workshop

 

Workshop on Bootstrap Methods and Permutation Tests for Teaching Statistics

Sponsored by the Consortium for the Advancement of Undergraduate Statistics Education (CAUSE)

Wednesday, May 16, 2007 • 8:30am - 4:30pm

Columbus, Ohio

A satellite workshop to the USCOTS Conference, May 17 through May 19, 2007



Overview | Agenda


About the Workshop

The Consortium for the Advancement of Undergraduate Statistics Education (CAUSE) will sponsor a 1-day workshop on bootstrap methods and permutation tests for teaching statistics.

In addition to meals during the day of the workshop, participants will receive a registration fee waiver for the United States Conference on Teaching Statistics (USCOTS), which will begin on the evening of Thursday, May 17 and run through Saturday, May 19, 2007.

Target Audience

Participants in this workshop should be teaching or planning to teach introductory level undergraduate statistics courses in a two-year or four-year college or university, or the Advanced Placement high school statistics course.

Abstract

Statistical concepts such as sampling distributions, standard errors, and P-values are difficult for many students. It is hard to get hands-on experience with these abstract concepts. In this workshop we'll learn how to use bootstrapping and permutation tests (BPT), for use in statistical practice and teaching. BPT provide output we may graph in familiar ways (like histograms) to help students and clients understand sampling variability, standard errors, p-values, and the Central Limit Theorem (CLT)-not just in the abstract, but for the data set and statistic at hand.

BPT also free students from dependence on formula-based methods. Early in Stat 101 we teach that robustness is important, and that students should look at medians, not just means. Yet later in the course, and too often in statistical practice, we ignore those lessons, and use simple means with Normal-based inferences, even though the corresponding assumptions are violated. BPT provide ways to calculate standard errors, confidence intervals, and hypothesis tests for a wide variety of statistics, without formulas. Or they can be used to check formulas, and help students gain intuition about whether an answer they calculate with a formula is reasonable.

Teaching materials using BPT are available for use with some introductory statistics texts, such as Moore and McCabe, Introduction to the Practice of Statistics, see www.insightful.com/Hesterberg/bootstrap.

Expectations

  • Participation in all sessions offered on Wednesday, May 16, 2007

  • Attendance at USCOTS

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.

About the Presenter

Tim Hesterberg

Tim Hesterberg

Tim Hesterberg taught at St. Olaf College and Franklin and Marshall College, then joined Insightful Corp. in 1996 to turn his research on bootstrap methods into widely usable statistical software. He has taught short courses on BPT in such exotic locations as Rochester MN and Little Rock. Oh yes, also Albuquerque, San Francisco, Boston, Chicago, L.A., Washington D.C., Minneapolis, Cincinnati, Portland, Seattle, Toronto, London, Manchester, Basingstoke UK, Bedford UK, Zurich, Basel, and Montpellier. His web site is www.insightful.com/Hesterberg. He enjoys teaching water bottle rockets, and works on environmental preservation, fighting global warming, and scientific integrity in public policy, home.comcast.net/~timhesterberg.