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  • December 11, 2007 Teaching and Learning webinar presented by Mark L. Berenson, Montclair State University, and hosted by Jackie Miller, he Ohio State University. As we consider how we might improve our introductory statistics courses, we are constrained by a variety of environmental/logistical and pedagogical issues that must be addressed if we want our students to complete the course saying it was useful, it was relevant and practical, and that it increased their communicational, computational, technological and analytical skills. If not properly considered, such issues may result in the course being considered unsatisfying, incomprehensible, and/or unnecessarily obtuse. This Webinar focuses on key course content concerns that must be addressed and engages participants in discussing resolutions. Participants also had the opportunity to describe and discuss other content barriers to effective statistical pedagogy.

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  • April 8, 2008 Teaching and Learning webinar presented by Beth Chance and Allan Rossman, Cal Poly - San Luis Obispo and hosted by Jackie Miller, The Ohio State University. Math majors, and other mathematically inclined students, have typically been introduced to statistics through courses in probability and mathematical statistics. We worry that such a course sequence presents mathematical aspects of statistics without emphasizing applications and the larger reasoning process of statistical investigations. This webinar describes and discusses a data-centered course that we have developed for mathematically inclined undergraduates.

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  • May 13, 2008 Teaching and Learning webinar presented by Joy Jordan, Lawrence University and hosted by Jackie Miller, The Ohio State University. Writing can be an effective instrument for students learning new concepts, and there is a plethora of writing-to-learn research. This Webinar summarizes important findings from the writing literature, as well as providing specific writing-assignment examples for the introductory statistics classroom.

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  • July 8, 2008 Teaching and Learning webinar presented by Shonda Kuiper, Grinnell College and hosted by Jackie Miller, The Ohio State University. Many instructors use projects to ensure that students experience the challenge of synthesizing key elements learned throughout a course. However, students can often have difficulty adjusting from traditional homework to a true research project that requires searching the literature, transitioning from a research question to a statistical model, preparing a proposal for analysis, collecting data, determine an appropriate technique for analysis, and presenting the results. This webinar presents multi-day lab modules that bridge the gap between smaller, focused textbook problems to large projects that help students experience the role of a research scientist. These labs can be combined to form a second statistics course, individually incorporated into an introductory statistics course, used to form the basis of an individual research project, or used to help students and researchers in other disciplines better understand how statisticians approach data analysis.

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  • February 10, 2009 Teaching and Learning webinar presented by Andrew Zieffler, Bob delMas, and Joan Garfield, University of Minnesota, and hosted by Jackie Miller, The Ohio State University. This webinar presents an overview of the materials and research-based pedagogical approach to helping students reason about important statistical concepts. The materials presented were developed by the NSF-funded AIMS (adapting and Implementing Innovative Materials in Statistics) project at the University of Minnesota (www.tc.umn.edu/~aims).

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  • May 12, 2009 Teaching and Learning hour-long webinar panel discussion presented by Laura Kubatko, The Ohio State University; Danny Kaplan, Macalester College; and Jeff Knisley, East Tennessee State University, and hosted by Jackie Miller, The Ohio State University. 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 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 contrasts a comprehensive "team" approach (at ETSU) with stand-alone courses (at Macalester and at OSU) and refers to the institutional opportunities and constraints that have shaped the programs at their different institutions.

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  • A computational tool that runs the one-way ANOVA by the user inputing individual data or by copying and pasting a delimitted data set.

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  • Here one finds a collection of applets and famous problems in probability (as well as other areas of mathematics such as calculus and geometry). Some of the topics/problems include: Bertrand's Paradox, Birthday Coincidence, Buffon's Needle (Noodle), Lewis Carroll's Problem, Monty Hall Dilemma, Parrondo Paradox, and Three pancakes problem.

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  • The BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. This site is primarily concerned with the stand-alone WinBUGS 1.4.1 package, which has a graphical user interface and on-line monitoring and convergence diagnostics. This program can be downloaded for free from the site.

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  • Everyday we have specific routines we engage in. Many of these routines are tailored to preventing us from becoming victims of crime. We do things like lock our doors, watch where we walk at night, or avoid walking alone. We take these actions because at some level we are afraid of the possibility of being a victim of crime. Although we may not consciously think about it, these routines may be influenced by a variety of factors. What factors might make some individuals more afraid than others?

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