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  • This article, in a series, describes a game, which tests opposing strategies through aspects of experiemental design.
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  • This site provides an outline of an activity for introducing Bayes' Theorem and conditional probability.
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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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  • This site provides applets, lessons, and objectives for learning about conditional probability. The applet activity introduces multiple-outcomes events and computing probabilities.
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  • This is a collection of activities as Java applets that can be used to explore probability and statistics. Each activity is supplemented with background information, activity instructions, and a curriculum for the activity.
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  • This PowerPoint presentation evaluates type I errors in civil trials compared to criminal trials as well as provides an example of a hypothesis test and its components. The original presenation is available for download.
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  • This lesson plan uses the Birthday Paradox to introduce basic concepts of probability. Students run a Monte Carlo simulation using the TI-83 graphing calculator to generate random dates, and then search for matching pairs. Students also perform a graphical analysis of the birthday-problem function. Key Words: Permutations; Explicit Function; Recursive Function; Modeling.
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  • An independent, nonpartisan resource on trends in American public opinion. Gives examples of recent polls, margins of error, questions asked, and sample sizes.
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  • This general, introductory tutorial on mathematical modeling (in pdf format) is intended to provide an introduction to the correct analysis of data. It addresses, in an elementary way, those ideas that are important to the effort of distinguishing information from error. This distinction constitutes the central theme of the material described herein. Both deterministic modeling (univariate regression) as well as the (stochastic) modeling of random variables are considered, with emphasis on the latter. No attempt is made to cover every topic of relevance. Instead, attention is focussed on elucidating and illustrating core concepts as they apply to empirical data.

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  • This module contains discussions on t-test, ANOVA, correlation, two-way factorial ANOVA, regression, chi-squared, and distributions and provides links to a variety of activities relevant to the discussions.
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