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.
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.
Generate a graphic and numerical display of the properties of the t-distribution for values of df between 4 and 200, inclusive.
This page generates a Poisson distribution, as approximated by the Binomial. After clicking continue, users must enter the sample size (n>39) and probability of success (between 0.0 and 0.2, inclusive). A graph of the Poisson distribution with mean=np is shown as well as a table of the Poisson probabilities. Key Word: Poisson Calculator.
This page generates a histogram of a Poisson distribution and the associated table of probabilities. Upon opening the page, users will be prompted to enter the mean of the distribution (between 0.01 and 20.0, inclusive). Key Word: Poisson Calculator.
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.
This page performs a Kolmogorov-Smirnov "Goodness of Fit" test for categorical data. Users enter observed frequencies and expected frequencies for up to 8 mutually exclusive categories. The applet returns the critical values for the .05 and .01 levels of significance.
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.