Jo Hardin, Pomona College
Tuesday, July 28, 2009 - 2:30pm ET
Based on an activity by John Spurrier, we use a baseball example to introduce students to Bayesian estimation. Students use prior information to determine prior distributions which lead to different estimators of the probability of a hit in baseball. We also compare our different Bayesian estimators and different frequentist estimators using bias, variability, and mean squared error. We can see the effect that sample size and dispersion of the prior distribution have on the estimator.
Margo Vreeburg Izzo, The Ohio State University Nisonger Center
Tuesday, July 14, 2009 - 2:00pm ET
Teaching a diverse college population is a challenge that most college faculty face each day. Universal Design for Learning is an approach to teaching that takes into consideration different student experiences, different cultures, and other issues such as disability. By examining curriculum and instruction through the context of universal design, you can engage as many students as possible in your college classroom and increase achievement by engaging students through a variety of methods ranging from electronic voting machines during class lectures to podcasts to deliver/reinforce essential course content.
Leigh Slauson, Otterbein College
Tuesday, June 23, 2009 - 2:30pm ET
This webinar will describe an activity that uses the playlist from an iPod music player to teach the concept of random selection, the various sampling techniques, and the use of simulation to estimate probability. The webinar will include a discussion of the background of this activity, the learning goals of the activity, how this activity can be adapted to different levels of technology, suggestions for assessment, and other supplemental reference materials.
Dalene Stangl, Duke University
Tuesday, June 9, 2009 - 2:00pm ET
This webinar will present the core materials I use to teach Bayesian inference in undergraduate service courses geared toward social science, natural science, pre-med, and/or pre-law students. During the semester this material is presented after completing all chapters of the book Statistics by Freedman, Pisani, and Purves. It uses math at the level of basic algebra.
Dennis Pearl, The Ohio State University
Tuesday, May 26, 2009 - 2:30pm ET
This webinar will describe a computer lab activity using the Flash-based applet at www.causeweb.org/mouse_experiment to teach key principles regarding the value of random assignment:
how it helps to eliminate bias when compared with a haphazard assignment process,
how it leads to a consistent pattern of results when repeated, and
how it makes the question of statistical significance interesting since differences between groups are either from treatment or by the luck of the draw.
In this webinar, the activity will be demonstrated along with a discussion of goals, context, background materials, class handouts, and assessments.
Laura Kubatko, The Ohio State University; Danny Kaplan, Macalester College; and Jeff Knisley, East Tennessee State University
Tuesday, May 12, 2009 - 2:00pm ET
National reports such as Bio2010 have called for drastic improvements in the quantitative education that biology students receive. The three panelists are involved in three differently structured integrative programs aimed to give biology students the statistics that are useful in learning and doing biology.
The three programs have some surprising things in common for teaching introductory statistics. All three involve connecting calculus and statistics. All three reach beyond the mathematical topics usually encountered in intro statistics in important ways. All three aim to keep the mathematics and statistics strongly connected to biology.
The panelists will describe their different approaches to teaching statistics for biology and discuss how and why an integrated approach gives advantages. Important issues are how to tie statistics advantageously with calculus, how to keep "advanced" mathematical and statistical topics accessible to introductory-level biology students, and how to employ computation productively. The discussion will contrast a comprehensive "team" approach (at ETSU) with stand-alone courses (at Macalester and at OSU) and will refer to the institutional opportunities and constraints that have shaped the programs at their different institutions.
Herbert Lee, University of California - Santa Cruz
Tuesday, April 28, 2009 - 2:30pm ET
Getting and retaining the attention of students in an introductory statistics course can be a challenge, and poor motivation or outright fear of mathematical concepts can hinder learning. By using an example as familiar and comforting as chocolate chip cookies, the instructor can make a variety of statistical concepts come to life for the students, greatly enhancing learning. Topics from variability and exploratory data analysis to hypothesis testing and Bayesian statistics can be illuminated with cookies.
Allan Rossman & Beth Chance, Cal Poly - San Luis Obispo; and John Holcomb, Cleveland State University
Tuesday, April 14, 2009 - 2:00pm ET
We present ideas and activities for helping students to learn fundamental concepts of statistical inference with a randomization-based curriculum rather than normal-based inference. We propose that this approach leads to deeper conceptual understanding, makes a clear connection between study design and scope of conclusions, and provides a powerful and generalizable analysis framework. During this webinar we present arguments in favor of such a curriculum, demonstrate some activities through which students can investigate these concepts, highlight some difficulties with implementing this approach, and discuss ideas for assessing student understanding of inference concepts and randomization procedures.
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Nicholas Horton, Smith College
Tuesday, March 24, 2009 - 2:30pm ET
Students have a hard time making the connection between variance and risk. To convey the connection, Foster and Stine (Being Warren Buffett: A Classroom Simulation of Risk and Wealth when Investing in the Stock Market (see materials) The American Statistician, 2006, 60:53-60) developed a classroom simulation. In the simulation, groups of students roll three colored dice that determine the success of three "investments". The simulated investments behave quite differently. The value of one remains almost constant, another drifts slowly upward, and the third climbs to extremes or plummets. As the simulation proceeds, some groups have great success with this last investment--they become the "Warren Buffetts" of the class. For most groups, however, this last investment leads to ruin because of variance in its returns. The marked difference in outcomes shows students how hard it is to separate luck from skill. The simulation also demonstrates how portfolios, weighted combinations of investments, reduce the variance. In the simulation, a mixture of two poor investments is surprisingly good.
In this webinar, the activity will be demonstrated along with a discussion of goals, context, background materials, class handouts, and references.
Jennifer Kaplan, Michigan State University
Tuesday, March 10, 2009 - 2:00pm ET
Central to the recommendations for teaching introductory statistics made by the GAISE committee were the following: foster active learning in the classroom, use assessment to improve and evaluate student learning, and use real data (GAISE, 2006). This session will illustrate how personal response systems (clickers) can be used to address the realization of these three recommendations in large lecture classes (over 70 students). The session will discuss general issues of the implementation of clickers and then provide an example of each of the following three uses of clickers in the classroom: 1) questions designed to highlight common conceptual misunderstandings in statistics, 2) questions designed as review questions for topics already addressed, and 3) questions that were part of a class activity to help students learn a concept.
Watch Webinar Recording (FLASH)