This is an extensive collection (and a continuously expanding collection) of applets on topics that include probability, descriptive statistics, sampling distributions, Monte Carlo simulation, Buffon's coin problem, chi-square, p-values, correlation, and more. There is even a random number generator that is part of the collection.
This applet displays various distributions and allows the user to experiment with the parameters to see the effects on the curve.
This applet simulates rolling dice to illustrate the central limit theorem. The user can choose between 1, 2, 6, or 9 dice to roll 1, 5, 20, or 100 times. The distribution is graphically displayed. This applet needs to be resized for optimal viewing.
This applet shades the graph and computes the probability of X, when X is between two parameters x1 and x2. The user inputs the mean, standard deviation, x1 and x2. This applet should be resized for optimal viewing.
This applet shows the normal or Gaussian distribution. The distribution has two parameters, the mean and the standard deviation. Click the draw button after filling in new values for the mean and the standard deviation to obtain a new diagram of the normal distribution.
A collection of applets addressing data analysis, sampling distribution simulations, and probability and inference. Some can be used individually, though others require context from the textbook.
This applet demonstrates the concept of power. Users select the hypothesized mean, the alternative mean, the sample size, and the number of samples. The applet shows the hypothesized histogram and the alternative histogram. Users then select either the level of significance and set alpha or the rejection region and set the test statistic. The applet then shows the p-value (in red) and power (in green). User can also determine the direction of the test by clicking the inequality sign.
This resource defines and explains standard deviation and the normal distribution.
A computational tool that runs the one-way ANOVA by the user inputing individual data or by copying and pasting a delimitted data set.
In this applet, we simulate a series of hypothesis of tests for the value of the parameter p in a Bernoulli random variable. Each column of red and green marks represents a sample of 30 observations. "Successes'' are coded by green marks and "failures'' by red marks.