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  • StatKey is the analysis package to accompany the textbook "Statistics: Unlocking the Power of Data." StatKey includes interactive applets to describe and graph data, engage in bootstrapping and randomization tests, and explore sampling distributions and theoretical distributions.

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  • This YouTube channel includes a series of video interviews between John McGready and some of his colleagues from Johns Hopkins University. The videos are meant to highlight the importance of biostatistics as a core driver of public health discovery, the importance of statistical reasoning in the research process, and how the fundamentals that are covered in an introductory biostatistics course are the framework for more advanced methodology.

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  • This blog will be about access: access to data and access to analysis tools. This blog will be about data privacy, and data sharing. This blog will be about people who use data to better their lives and the lives of others. This blog is meant for anyone wishing to become a citizen statistician, but in particular for statistics teachers-those who help empower citizens to become citizen statisticians.

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  • The WISE Bootstrapping Applet can be used to demonstrate bootstrapping by creating a confidence interval for a population mean or median. The user can manipulate the population distribution, sample size, and number of resamples. An associated guide gives suggestions for teaching bootstrapping.
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  • This is an extensive collection (and a continuously expanding collection) of applets on topics that include probability, descriptive statistics, sampling distributions, Monte Carlo simulation, Buffon's coin problem, chi-square, p-values, correlation, and more. There is even a random number generator that is part of the collection.

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  • This simulation allows you to roll two dice and compare empirical and probability histograms for the sum or product of the two outcomes.

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  • This hour long radio podcast focuses on stochasticity, or randomness. According the website: "Stochasticity (a wonderfully slippery and smarty-pants word for randomness), may be at the very foundation of our lives. To understand how big a role it plays, we look at chance and patterns in sports, lottery tickets, and even the cells in our own body. Along the way, we talk to a woman suddenly consumed by a frenzied gambling addiction, meet two friends whose meeting seems to defy pure chance, and take a close look at some very noisy bacteria." Several guests appear in this radio podcast, including Deborah Nolan.

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  • August 10, 2010 T&L webinar presented by Diane Fisher (University of Louisiana at Lafayette), Jennifer Kaplan (Michigan State University), and Neal Rogness (Grand Valley State University) and hosted by Jackie Miller(The Ohio State University). Our research shows that half of the students entering a statistics course use the word random colloquially to mean, "haphazard" or "out of the ordinary." Another large subset of students define random as, "selecting without prior knowledge or criteria." At the end of the semester, only 8% of students we studied gave a correct statistical definition for the word random and most students still define random as, "selecting without order or reason." In this session we will present a classroom approach to help students better understand what statisticians mean by random or randomness as well as preliminary results of the affect of this approach.
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  • September 14, 2010 T&L webinar presented by Thomas Moore(Grinnell College) and hosted by Jackie Miller(The Ohio State University). Permutation tests and randomization tests were introduced almost a century ago, well before inexpensive, high-speed computing made them feasible to use. Fisher and Pitman showed the two-sample t-test could approximate the permutation test in a two independent groups experiment. Today many statistics educators are returning to the permutation test as a more intuitive way to teach hypothesis testing. In this presentation, I will show an interesting teaching example about primate behavior that illustrates how simple permutation tests are to use, even with a messier data set that admits of no obvious and easy-to-compute approximation.
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  • October 12, 2010 T&L webinar presented by George Cobb(Mount Holyoke College) and hosted by Leigh Slauson (Capital University). What's the best way to introduce students of mathematics to statistics? Tradition offers two main choices: a variant of the standard "Stat 101" course, or some version of the two-semester sequence in probability and mathematical statistics. I hope to convince participants to think seriously about a third option: the theory and applications of linear models as a first statistics course for sophomore math majors. Rather than subject you to a half-hour polemic, however, I plan to talk concretely about multiple regression models and methodological challenges that arise in connection with AAUP data relating faculty salaries to the percentage of women faculty, and to present also a short geometric proof of the Gauss-Markov Theorem.
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