Java Applet

  • This applet demonstrates how the reliability of X and Y affect various aspects of the regression of Y on X.
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  • This applet demonstrates how a histogram is affected by bin width and starting point of first bin. It also illustrates cross-validation criterion for assessing histograms.
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  • This is a simulation illustrating the regression toward the mean phenomenon.
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  • This simulation shows recorded response times on a simple motor task under two conditions. Various statistics and graphs used to compare the distributions are presented.
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  • This simulation illustrates types of sums of squares in a 2 x 3 ANOVA.
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  • This applet lets you explore the effect of violations of the assumptions of normality and homogeneity of variance on the type I error rate and power of t tests (and two-group analysis of variance).
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  • This case study assesses the question, "Can the application of magnetic fields be an effective treatment for pain?" It addresses concepts including: boxplots, stem and leaf displays, correlated t-test, two-sample t-test, repeated measures analysis of variance, and analysis of covariance.
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  • In this free online video program, "students will understand inference for simple linear regression, emphasizing slope, and prediction. This unit presents the two most important kinds of inference: inference about the slope of the population line and prediction of the response for a given x. Although the formulas are more complicated, the ideas are similar to t procedures for the mean sigma of a population."

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  • This free online video program "marks a transition in the series: from a focus on inference about the mean of a population to exploring inferences about a different kind of parameter, the proportion or percent of a population that has a certain characteristic. Students will observe the use of confidence intervals and tests for comparing proportions applied in government estimates of unemployment rates."
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  • This demonstration allows you to view the binomial distribution and the normal approximation to it as a function of the probability of a success on a given trial and the number of trials. It can be used to compute binomial probabilities and normal approximations of those probabilities.
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