This applet is a probabilistic study of picking fortunes from a limited supply of fortune cookies. The student will try to answer how many cookies he/she has to eat to have a 50/50 chance of reading all the fortunes.
The page will calculate the following: Exact binomial probabilities, Approximation via the normal distribution, Approximation via the Poisson Distribution. This page will calculate and/or estimate binomial probabilities for situations of the general "k out of n" type, where k is the number of times a binomial outcome is observed or stipulated to occur, p is the probability that the outcome will occur on any particular occasion, q is the complementary probability (1-p) that the outcome will not occur on any particular occasion, and n is the number of occasions.
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.
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.