Illustrates the central limit theorem by allowing the user to increase the number of samples in increments of 100, 1000, or 10000.
Illustrates the central limit theorem by allowing the user to increase the number of samples in increments of 100, 1000, or 10000.
This page generates a graph of the Chi-Square distribution and displays the associated probabilities. Users enter the degrees of freedom (between 1 and 20, inclusive) upon opening the page.
This page calculates the standard error of a sampling distribution of sample means when users input the mean and standard deviation of the population and the sample size.
This applet generates a graph of the sampling distribution of sample means and displays the probabilities associated with that distribution. Users enter the mean and standard deviation of the source population and the size of the samples. The applet also calculates the standard error of the sample means.
This page generates a graph of the sampling distribution of the difference between two means and displays the probabilities associated with that distribution. Users enter the population standard deviation and the sample sizes, Na and Nb. The applet also calculates the standard error of the sample mean difference.
This page generates a graph of the sampling distribution of r, the Pearson correlation coefficient. Upon opening, the applet prompts for sample size greater than 6. The applet also displays the probabilities associated with the distribution.
Calculates the areas under the curve of the normal distribution falling to the left of -z, to the right of +z, and between -z and +z.
The page displays the sampling distribution and the standard error of the difference between two sample means. To calculate standard error, enter the standard deviation of the source population, along with the sample sizes, Na and Nb, and then click "Calculate".
Generate a graphic and numerical display of the properties of the F-Distributions, for any value of df_numerator and for values of df_denominator >= 5.
Given the population incidence of a certain disease, and the conditional probabilities of positive and negative test results, what are the probabilities for a particular test result of a true positive, true negative, false positive, and false negative? Adaptable to other kinds of conditional situations. Although this page is adaptable to a variety of backward probability situations, its exemplary case is the one in which one is seeking to make sense of the result of a medical test.