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  • Weapons and Aggression

    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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  • Robustness of t test and ANOVA

    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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  • Unequal n ANOVA and Types of Sums of Squares

    This simulation illustrates types of sums of squares in a 2 x 3 ANOVA.

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  • Regression to the Mean

    This is a simulation illustrating the regression toward the mean phenomenon.

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  • Histograms, Bin Widths, and Cross Validation

    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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  • Dataset Example: Reliability and Regression Analysis

    This applet demonstrates how the reliability of X and Y affect various aspects of the regression of Y on X.

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  • 2 x 2 Contingency Tables

    This applet simulates experiments using 2 x 2 contingency tables. You specify the population proportions and the sample size and examine the effects on the probability of rejecting the null hypothesis.

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  • Chi Square Test of Deviations from Expected Frequencies

    The applet allows users to sample from a normal distribution or from a uniform distribution. It shows the expected values and the observed values and computes the deviation. Then, a chi-square test shows if the deviations are significant for both the normal and uniform distributions.

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  • Data+Applet: Restriction of Range

    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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  • Components of r

    As described on the page itself: "The simulation shows a scatterplot of data from a bivariate distribution in which the relationship between the two variables is linear. You can change the "input" values of slope, standard error of the estimate, or standard deviation of X for this data sample, and see the effects of your change. "

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