Univariate Distributions

  • The applet in this section allows you to see how the T distribution is related to the Standard Normal distribution by calculating probabilities. The T distribution is primarily used to make inferences on a Normal mean when the variance is unknown. If the variance is known inference on the mean can be done using the Standard Normal. The user has a choice of three different probability expressions, then can change the degrees of freedom and the limits of probability. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/TNormal.html
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  • In this demonstration a scatterplot is displayed and you draw in a regression line by hand. You can then compare your line to the best least squares fit. You can also try to guess the value of Pearson's correlation coefficient.
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  • This applet shows how the correlation between two variables is affected by the range of the variable plotted on the X-axis.
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  • This applet demonstrates that even a "small" effect can be important under some circumstances. Applicants from two groups apply for a job. The user manipulates the mean and the cut-off score in order to see the effects the small changes has on the number of people hired in each group. The effects on the proportion of hired applicants from each group are displayed.(Requires a browser that supports Java).
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  • This simulation illustrates types of sums of squares in a 2 x 3 ANOVA.
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  • This case study addresses the question: "Will a smiling person accused of a crime be treated more leniently than one who is not smiling? If so, does the type of smile make a difference?" It concerns the following concepts: quantile/boxplots, contrasts among means, Dunnett's test, and Bonferroni correction.
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  • This interactive tutorial on Expectations helps students understand the concept of expectations, recognize and use variance and standard deviation, understand the method of moments, recognize and use co-variance, and solve exercise problems using expectations.
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  • This tutorial on Distributions helps students understand the basic concept of probability distributions, recognize and use Binomial, Normal, Poisson, and Uniform Distributions, and solve exercise problems using probability distributions.
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  • In this free online video program, "students will learn the distinction between deterministic phenomena and random sampling. This program introduces the concepts of sample space, events, and outcomes, and demonstrates how to use them to create a probability model. A discussion of statistician Persi Diaconis's work with probability theory covers many of the central ideas about randomness and probability."
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  • This free online video program, "demonstrates how to determine the probability of any number of independent events, incorporating many of the same concepts used in previous programs. An interview with a statistician who helped to investigate the space shuttle accident shows how probability can be used to estimate the reliability of equipment."
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