Limit Theorems

  • A useful site for instructors to learn how to create and incorporate guided notes into their classroom.
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  • This site gives an explanation of, a definition for and an example of sample means. Topics include mean, variance, distribution, and the Central Limit Theorem.
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  • This online, interactive lesson on games of chance provides examples, exercises, and applets which include Poker, Poker dice, Chuck-a-Luck, Craps, Roulette, The Monty Hall Problem, lotteries, and Red and Black.
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  • Illustrates the central limit theorem by allowing the user to increase the number of samples in increments of 100, 1000, or 10000.

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  • This page will perform basic multiple regression analysis for the case where there are several independent predictor variables, X1, X2, etc., and one dependent or criterion variable, Y. Requires import of data from a spreadsheet.

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  • Part of an online statistics textbook. Topics include: (1) Law of Large Numbers for Discrete Random Variables, (2) Chebyshev Inequality, (3) Law of Averages, (4) Law of Large Numbers for Continuous Random Variables, (5) Monte Carlo Method. There are several examples and exercises that accompany the material.
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  • This resource briefly explains what a significance level is and how they are used in hypothesis testing. It also includes other links related to significance level such as "Type I error" and "significance test".
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  • This applet simulates rolling dice to illustrate the central limit theorem. The user can choose between 1, 2, 6, or 9 dice to roll 1, 5, 20, or 100 times. The distribution is graphically displayed. This applet needs to be resized for optimal viewing.

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  • For n = 50 to 400, in steps of size 5, this program computes and displays (1) the exact probability P(|A_n - p| >= epsilon), where A_n is the average outcome of n Bernoulli trials with probability p of success, and (2) the Chebyshev estimate p(1-p)/(n(epsilon^2)) for this probability. You can specify p and epsilon.
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  • This page will calculate the intercorrelations (r) for any number of variables (V1, V2, V3, etc.) and for any number of observations per variable.

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