Data Presentation

  • 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 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 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 addresses the question: "Does the mere presence of a weapon increase the accessibility of aggressive thoughts?" It concerns the following concepts: quantile and box plots, stem and leaf displays, one-sample t test, confidence interval, within-subjects ANOVA, and consequences of violation of normality assumption.
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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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  • 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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