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Central Limit Theorem

  • 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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  • This FLASH based applet illustrates the sampling distribution of the mean. This applet allows the user to pick a population from over 2000 pre-defined populations. The user can then choose size of the random sample to select. The applet can produce random samples in one, 10, 100, or 1000 at a time. The resulting means are illustrated on a histogram. The histogram has an outline of the normal distribution and vertical lines at 1, 2, and 3 standard deviations. The applet can be viewed at the original site or downloaded to the instructors machine.
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  • Song incorporates various terms from areas such as experimental design, graphing, and hypothesis testing. May be sung to the tune of "Desperado" (The Eagles). Musical accompaniment realization are by Joshua Lintz and vocals are by Mariana Sandoval from University of Texas at El Paso.

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  • During this simulation activity, students generate sampling distributions of the sample mean for n = 5 and n = 50 with Fathom 2 and use these distributions to confirm the Central Limit Theorem. Students sample from a large population of randomly selected pennies. Given that the variable of interest is the age of the pennies, which has a geometric distribution, this is a particularly convincing demonstration of the Central Limit Theorem in action. This activity includes detailed instructions on how to use Fathom to generate sampling distributions. The author will provide the Fathom data file upon request.
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  • Using a parameter it's possible to represent a property of an entire population with a single number instead of millions of individual data points. There are a number of possible parameters to choose from such as the median, mode, or interquartile range. Each is calculated in a different manner and illuminates the data from a different point of view. The mean is one of the most useful and widely used and helps us understand populations. A population is simulated by generating 10,000 floating point random numbers between 0 and 10. Sample means are displayed in histograms and analyzed.
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  • In these activities designed to introduce sampling distributions and the Central Limit Theorem, students generate several small samples and note patterns in the distributions of the means and proportions that they themselves calculate from these samples. Outside of class, students generate samples of dice rolls and coin spins and draw random samples from small populations for which data is given on each individual. Students report their sample means and proportions to the instructor who then compiles the results into a single data file for in-class exploration of sampling distributions and the Central Limit Theorem. Key words: Sampling distribution, sample mean, sample proportion, central limit theorem

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  • This article describes an interactive activity illustrating general properties of hypothesis testing and hypothesis tests for proportions. Students generate, collect, and analyze data. Through simulation, students explore hypothesis testing concepts. Concepts illustrated are: interpretation of p-values, type I error rate, type II error rate, power, and the relationship between type I and type II error rates and power. This activity is appropriate for use in an introductory college or high school statistics course. Key words: hypothesis test on a proportion, type I and II errors, power, p-values, simulation
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  • This activity leads students to appreciate the usefulness of simulations for approximating probabilities. It also provides them with experience calculating probabilities based on geometric arguments and using the bivariate normal distribution. We have used it in courses in probability and mathematical statistics, as well as in an introductory statistics course at the post-calculus level. Students are expected to approximate the solution through simulation before solving it exactly. They are also expected to employ graphical as well as algebraic problem-solving strategies, in addition to their simulation analyses. Finally, students are asked to explain intuitively why it makes sense for the probabilities to change as they do. Key words: simulation, probability, geometry, independence, bivariate normal distribution
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