Limit Theorems

  • A cartoon to teach about the law of large numbers and the expected value under the assumption that future events are unknown to the betting strategy. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.
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  • This lesson introduces the Central Limit Theorem and discusses it in terms of the normal distribution, binomial distribution, and Poisson distribution.
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  • This applet relates the pdf of the Normal distribution to the cdf of the Normal distribution. The graph of the cdf is shown above with the pdf shown below. Click "Move" and the scroll bar will advance across the graph highlighting the area under the pdf in red. The z-score is shown as well as the probability less than z (F(z)) and the probability greater than z (1-F(z)).
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  • This website is a resource of teaching methods and approaches that instructors at all levels of statistics education can use to generate student interest in pursuing more study or a career in the field of statistics.
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  • This applet demonstrates the Central Limit Theorem. First, select a distribution (Normal, Uniform, Skewed, Custom) and add or delete data points by clicking on the graph. Then, sample from the parent population and the distribution of the sample mean is shown. Users can also choose to see the distribution of the median, standard deviation, variance, and range.
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  • This page provides a z-table with alpha levels from .00 to .09.

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  • This calculator determines the level of significance for the Wilcoxon-Mann-Whitney U-statistic. Users can enter N1, N2, and U or simply enter the raw data.

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  • This page provides links to distribution calculators, conceptual demonstration applets, statistical tables, online data analysis packages, function and image-processing tools, and other online computing resources. Key Words: Binomial; Normal; Exponential; Chi-Square; Geometric; Hypergeometric; Negative Binomial; Poisson; Student's T; F-Distribution; Wilcoxon Rank-Sum; Central Limit Theorem; Regression; Normal Approximation to Poisson; Confidence Intervals; Hypothesis Tests; Power; Sample-Size; ANOVA; Galton's Board; Function Plots; Edge Detection; Image Warping & Stretching; Polynomial Model Fitting; Wilcoxon-Mann-Whitney Statistic.
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  • This collection of datasets comes from several phases of drug research. Each dataset comes with a full description and questions to answer from the data.
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  • It is a good morning exercise for a research scientist to discard a pet hypothesis every day before breakfast. It keeps him young. A quote of Austrian animal behaviorist Konrad Lorenz (1903 - 1989) in "On Aggression", (English translation: 1966, Harvest books) p. 12. Quote also found in "Statistically Speaking - a Dictionary of Quotations" compiled by Carl Gaither and Alma Cavazos-Gaither p. 119.
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