Webinars

Our presentation will highlight the needs in statistics education from the perspective of client disciplines based on the use of statistics in teaching and research in various academic affiliations. This information will cultivate discussion on how to use the information to guide curriculum development in introductory statistics. As a result of this study, a large data set was compiled that can be used in the classroom for students to explore. A demonstration of how to access and use the data set in class will be given.

Teaching Principles of One-Way Analysis of Variance Using M&M's Candy I present an active learning classroom exercise illustrating essential principles of one-way analysis of variance (ANOVA) methods. The exercise is easily conducted by the instructor and is instructive (as well as enjoyable) for the students. This is conducive for demonstrating many theoretical and practical issues related to ANOVA and lends itself to multiple possible configurations of ANOVA results, leading to rich classroom discussion and deeper student understanding of real-world applications of the methods. The Cleveland Clinic Statistical Education Dataset Repository: Examples and more Examples Examples are highly sought by both students and teachers. This is particularly true as many statistical instructors aim to engage their students and increase active participation. While simulated datasets are functional, they lack real perspective and the intricacies of actual data. Described is the creation of a new web-based statistical educational resource. This growing dataset repository presents raw data from real medical studies and offers (a) a vignette summarizing the study, research question and study design; (b) a data dictionary with clear documentation of variables and codes; (c) a complete citation for the associated study publication; and (d) a variety of data formats compatible with the majority of statistical packages.

Lisa Dierker will offer reflections on the pedagogical design and experience of teaching her NSF-funded, passion-driven, project-based introductory statistics course both on campus, at Wesleyan University, and within the Massive Open On-line Course (MOOC) environment. www.wesleyan.edu/qac/curriculum

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.

In this webinar I will discuss my Coursera class "Data Analysis" that was offered for free. I will discuss the course and educational objectives, the platform, and issues that arise when scaling statistics education to a large audience.

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.

We will briefly review the development and evolution of Stat 303: The Art and Practice of Teaching Statistics, a required year-long course for all entering Ph.D. students in the Department of Statistics at Harvard University. The course started in 2005-2006, and has been revised annually to address students' feedback and evolving goals, as listed in the title. Dr. Meng will talk from his syllabus, which he will also display on the screen. Participants can follow the talk/discussions based on the following handouts. Feel free to make copies for note taking.

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.

In this webinar, we will provide an overview of goals and methods of curriculum evaluation that are appropriate for use in statistics education projects, share details of newly developed instruments that may be used in evaluation of these projects, and provide an example of evaluation methods used in the CATALST project along with a summary of what was learned in this evaluation. Additional information on the NSF-funded eATLAS (Evaluation and Assessment of Teaching and Learning About Statistics, NSF DUE 1044812 & 1043141) project will be shared regarding collection of national data to use in future evaluations.

Lisa Green & Scott McDaniel, Middle Tennessee State University