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  • This page of Statistical Java describes 11 different probability distributions including the Binomial, Poisson, Negative Binomial, Geometric, T, Chi-squared, Gamma, Weibull, Log-Normal, Beta, and F. Each distribution has its own applet in which users can manipulate the parameters to see how the distribution changes. The parameters are described on the main page as well as situations that would use each distribution. The equations of the distributions are not given. To select between the different applets you can click on Statistical Theory, Probability Distributions and then the Main Page. At the bottom of this page you can make your applet selection. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/
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  • This collection of Analysis Tools can assist students and researchers with questions about study desgin, data analysis, and probability. Topics include sample size, power, survival, binomial probabilities, interaction, Fisher's exact test, one and two sample tests, and more.
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  • This applet allows students to manipulate a histogram and observe changes in the mean and median. The site includes links to exercises and descriptions of measures of center and spread.
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  • This entry in the online encyclopeida, Wikipedia, describes Markov Chains, their properties, discrete state spaces, and formulas for calculating probabilities using Markov Chains. Links to examples and scientific applications are also included.
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  • The Against All Odds video series provides an extensive introduction to statistics. It consists of 26 half hour video episodes that include lecturing on statistical topics, animations of statistical topics and video of real world examples. The series is available online or can be purchased on VHS video tape. The statistical material in the series was supervised by Dr. David Moore and accordingly much of the material echos the language used in Moore's textbooks. Topics covered include most topics from an introductory statistics course and slightly more advanced topics such as seasonal variation, blocking of experimental designs and even Chernof faces. The material is very well suited for students in undergraduate statistics classes.
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