MAA Ancillary Workshops
To register for either of the workshops described below, please visit www.causeweb.org/workshop. There is no registration fee, however, participants must register in advance; no walk-ins will be allowed. Registration for the JMM is not a prerequisite for participating in these sessions.
Interactive Probability Instruction, presented by Dennis Pearl, The Ohio State University, Kyle Siegrist, University of Alabama, and Ivo Dinov, University of California Los Angeles; Tuesday, 1:00 p.m.–4:30 p.m. This half-day workshop will introduce participants to novel web-based technologies for blended teaching of computational statistics and applied probability theory. Specifically, 50% of the time will be dedicated to training using the Probability Distributome webapps (www.Distributome.org), 25% for demonstrating the classroom use of the Virtual Laboratories in Probability and Statistics (www.math.uah.edu/stat), and 25% for exploratory data analysis using the Statistics Online Computational Resource (www.SOCR.ucla.edu). Participants should bring a laptop to this workshop to take part in hands-on demonstrations illustrating data modeling, exploring of properties of probability distributions and interdistributional relationships, resampling and simulation, dynamic data plots, and model fitting. These topics and techniques are suitable for introductory and cross-listed applied probability and statistical methods courses. The workshop is designed to be accessible to those with little or no computational background, and will provide you with skills, examples, and resources that you can use in your own teaching.
Teaching the Statistical Investigation Process with Randomization-Based Inference, presented by Nathan Tintle, Dordt College, Tuesday, 9:00 a.m.–4:30 p.m. This full day workshop is intended for faculty members who have experience with or soon will be teaching introductory statistics. The goals of this workshop are to help participants to revise their introductory statistics course in two ways: 1) Using randomization-based methods, as opposed to methods based on the normal distribution, to introduce concepts of statistical inference, and 2) Emphasizing the overarching process of conducting statistical investigations, from formulating a question and collecting data through exploring data and drawing inferences to communicating results, throughout the course.
This workshop will provide direct experience with hands-on activities designed to introduce students to fundamental concepts of inference using randomization-based methods. The learning activities involve using freely available applets to explore concepts and analyze real data from genuine research studies. Presenters will also offer implementation and assessment suggestions during these activity-based sessions and discussion sessions. More information about the project on which this workshop is based can be found at www.math.hope.edu/isi.