Sampling & Survey Issues

  • This online, interactive lesson on random samples provides examples, exercises, and applets concerning sample mean, law of large numbers, sample variance, partial sums, central limit theorem, special properties of normal samples, order statistics, and sample covariance and correlation.
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  • A collection of links to video workshops for students in mathematics. Includes many topics from statistics to math and science to algebra.
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  • This site briefly defines several different types of sampling methods, contrasts probability and nonprobability sampling, and discusses target population. Part of a tutorial on questionnaire and survey design.
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  • Discusses Markov chains, transition probabilities, and the transition probability matrix.
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  • This is a basic web application that allows practice with matching points on a scatterplot to the appropriate correlation coefficient, r. Applet provides four scatterplots to match with four numeric correlations via radio buttons. After making selections, students click to see "correct" answers and keep a running total of proportion of correct matches, then may select four more plots.
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  • This Java based applet gives students an opportunity to work through confidence interval problems for the mean. The material provides written word problems in which an individual must be able to correctly identify the given parts for a confidence interval calculation, and then be able to use this information to find the confidence interval. It gives step by step prompts to encourage students to choose the correct numbers and "cast of characters".
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  • This online, interactive lesson on Bernoulli provides examples, exercises, and applets that cover binomial, geometric, negative binomial, and multinomial distributions.
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  • The page will calculate the following: Exact binomial probabilities, Approximation via the normal distribution, Approximation via the Poisson Distribution. This page will calculate and/or estimate binomial probabilities for situations of the general "k out of n" type, where k is the number of times a binomial outcome is observed or stipulated to occur, p is the probability that the outcome will occur on any particular occasion, q is the complementary probability (1-p) that the outcome will not occur on any particular occasion, and n is the number of occasions.

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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 generates a graph of the Chi-Square distribution and displays the associated probabilities. Users enter the degrees of freedom (between 1 and 20, inclusive) upon opening the page.

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