Webinars

Randomization tests are growing in popularity as an alternative to traditional tests, but also as a way to help students to understand the logic of inference. In this webinar, we will use Fathom software and online applets to introduce inference for the slope of a least-squares regression line. Come find out if seat location affects performance in a statistics class and if adding additional Mentos to a bottle of Diet Coke makes a bigger mess.

Technological advances in recent years have changed the possibilities for incorporating non-traditional learning approaches into the classroom. In this webinar we will demonstrate use of a 3-D game, TigerStat, for teaching statistics. In addition to demonstrating the game, we will present the first investigative lab module (lab) developed for teaching simple linear regression in an introductory statistics course. The lab emphasizes statistical thinking and the process of scientific inquiry to students using the game as a part of the data collection effort. The game-based lab presents a research question in the context of a case study and encourages students to follow a complete process of statistical analysis. These labs are designed to 1) foster a sense of engagement, 2) have a low threat of failure early on but create a challenging environment that grows with the students' knowledge, 3) create realistic, adaptable, and straightforward models representing current research in a variety of disciplines, and 4) provide an intrinsic motivation for students to want to learn. The game and lab materials were developed as part of NSF grant TUES DUE #1043814 with co-PI Shonda Kuiper, Grinnell College, and software development by Tietronix Software.

This presentation shows how the application of simple statistical methods can reveal to students important insights from climate data. While the popular press is filled with contradictory opinions about climate science, teachers can encourage students to use introductory-level statistics to analyze data for themselves on this important issue in public policy. The detailed example in this presentation addresses the very important topic of the rate of decline of Arctic sea ice. Many climate scientists believe that Arctic sea ice melt is accelerating. The simple data analyses of this paper are meant to encourage students to examine the evidence themselves using tools they learn in their introductory statistics course.

Lisa Green & Scott McDaniel, Middle Tennessee State University

If the rate of cancer in your small town is three times the national average, should you be alarmed? A short and simple activity that allocates cancer cases to random locations, using a pair of dice, shows that a rate of 3 or even 4 times the national average is not surprising.

There is a need for modern, efficient, and engaging pedagogical techniques for enhancing the teaching of probability theory, and its applications, that leave lasting impressions on learners. The Probability Distributome project has developed portable, browser-accessible and extensible resources including: Computing probability and critical values for a wide array of distributions Exploring probability distribution properties and inter-distributional relations Fitting probability distribution models to data Virtual resampling and simulation experiments Integrated data, web-applications and learning-activities We will show some of the Distributome web-resources and discuss best practices for integrating these tools, web-applications, activities and learning materials in probability and statistics curricula.

In this webinar I'll give a nuts-and-bolts description of a fourth year capstone activity for students in statistics programs at the University of Toronto. The statistics students join research students from other disciplines as collaborators. I'll describe what takes place including the nature of the projects and the support provided, how we've structured the course and are evaluating the projects, who are the members of the six distinct groups of individuals at the university who are benefitting from the experience, and why we started the course and organized it the way we did.

Many ideas and recommendations for meeting the GAISE guidelines at the college level have targeted relatively small class sizes. This webinar will provide an overview of a suite of twelve simulation activities that were designed to develop student conceptual understanding of inference in large lecture classes using personal response systems (clickers) to collect data. Details will be provided for three of the activities, in which each student performs a simulation once using a calculator and the results are collected via clickers. The activities allow students to experience statistical concepts such as distributions or models, variability, and the Central Limit Theorem. The large class, therefore, becomes a learning asset, rather than a liability.

This presentation focuses on how to incorporate a service learning component into introductory statistics. Service-learning is a concrete application of statistical methods using real data with the analysis and interpretation that is useful to a community agency. Discussion will include how to locate an organization, the selection of appropriate content for the project with focus on understanding what questions need to be answered and how to do so, the grading rubric for the presentations or posters and the time line of formative evaluation as the project proceeds.

In the early 1990's, the National Science Foundation funded many research projects for improving statistical education. Many of these stressed the need for classroom activities that illustrate important issues of designing experiments, generating quality data, fitting models, and performing statistical tests. This webinar describes such an activity on logistic regression that is useful in second applied statistics courses. The activity involves students attempting to toss a ball into a trash can from various distances. The outcome is whether or not students are successful in tossing the ball into the trash can. This activity and the adjoining homework assignments illustrate the binary nature of a response variable, fitting and interpreting simple and multiple logistic regression models, and the use of odds and odds ratios. Trashball activity website