Resource Library

September 14, 2010 T&L webinar presented by Thomas Moore(Grinnell College) and hosted by Jackie Miller(The Ohio State University). Permutation tests and randomization tests were introduced almost a century ago, well before inexpensive, high-speed computing made them feasible to use. Fisher and Pitman showed the two-sample t-test could approximate the permutation test in a two independent groups experiment. Today many statistics educators are returning to the permutation test as a more intuitive way to teach hypothesis testing. In this presentation, I will show an interesting teaching example about primate behavior that illustrates how simple permutation tests are to use, even with a messier data set that admits of no obvious and easy-to-compute approximation.

October 12, 2010 T&L webinar presented by George Cobb(Mount Holyoke College) and hosted by Leigh Slauson (Capital University). What's the best way to introduce students of mathematics to statistics? Tradition offers two main choices: a variant of the standard "Stat 101" course, or some version of the two-semester sequence in probability and mathematical statistics. I hope to convince participants to think seriously about a third option: the theory and applications of linear models as a first statistics course for sophomore math majors. Rather than subject you to a half-hour polemic, however, I plan to talk concretely about multiple regression models and methodological challenges that arise in connection with AAUP data relating faculty salaries to the percentage of women faculty, and to present also a short geometric proof of the Gauss-Markov Theorem.

November 9, 2010 T&L webinar presented by Jiyoon Park and Audbjorg Bjornsdottir (University of Minnesota) and hosted by Jackie Miller (The Ohio State University). This webinar presents the development of a new instrument designed to assess the practices and beliefs of teachers of introductory statistics courses. The Statistics Teaching Inventory (STI) was developed to be used as a national survey to assess changes in teaching over time as well as for use in evaluating professional development activities. We will describe the instrument and the validation process, and invite comments and suggestions about its content and potential use in research and evaluation studies.

This issue contains articles on: The predictive model used by the website FiveThirtyEight.com during the 2008 Presidential election, the design and implementation of an election day exit poll by statistics students, a description of the randomization measures taken to ensure fairness and transparency in the awarding of development grants to farmers in the Republic of Georgia, an explanation of the Item-Matching problem and the Coupon-Collecting problem, together with R code for simulating both problems, and a review of the book, Applied Spatial Statistics for Public Health Data.

This site contains a small collection of videos about how to use Minitab.

This site contains several videos about how to use Mathematica and how to teach with Mathematica.

This site includes several short tutorials that showcase different features of JMP 7. There is also another site with JMP tutorials at http://stat.fsu.edu/tutorials/

September 28, 2010 Activity webinar presented by Carolyn Cuff, Westminster College and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. Students must confront their misconceptions before we can teach them new concepts. Naively, a census is an accurate method to quantify a population parameter. A very brief, memorable and easy to implement activity demonstrates that a census is at best difficult even for a small and easily enumerated population.

August 24, 2010 Activity Webinar presented by Jackie Miller, The Ohio State University and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. When Dr. Miller took a graduate course in College Teaching, she learned the jigsaw method. The jigsaw is a cooperative learning technique where students work together in a "home" group on a specific task and then are placed into "jigsaw" groups made up of one member from each home group. For example, if there are 25 students in the class, 5 students would be assigned to each of the A, B, C, D, E home groups, and each jigsaw group would each one member from A, B, C, D, and E. While in the jigsaw groups, the students teach each other what they learned in their home groups. Dr. Miller recalls bringing the idea back with her to one of the OSU elementary statistics courses where it has been used successfully since 1996. Recently a graduate teaching assistant (GTA) suggested to other GTAs that this might be good in another introductory statistics course, and the activity has been adopted successfully . As structured, the jigsaw can be used in an exam review in statistics by assigning students to, say, 5 exercises that they need to master before they go to their jigsaw groups to teach others about their exercise. During this webinar, the webinar presents how the jigsaw is done and address questions like: How do you budget your time for this class activity? How do you know that students are teaching the correct answer? How do you know that students are not just furiously writing down answers instead of listening to understand the concept? Can this work for you? By the end of the webinar, hopefully you will be as intrigued as Dr. Miller was to learn about the jigsaw method and will want to try it in your classroom.

July 27, 2010 Activity Webinar presented by Herle McGowan, North Carolina State University and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. In this webinar, the webinar discusses the end-of-semester project that is used in North Carolina State's introductory statistics course. This project supports statistical thinking by allowing students to apply knowledge accumulated throughout the semester. Students are presented with a research question and must design and carry out an experiment, analyze the resulting data and form a conclusion over the course of several class periods.