This online, interactive lesson on hypothesis testing provides examples, exercises, and applets which includes tests in the normal model, Bernoulli Model, and two-sample normal model as well as likelihood ratio and goodness of fit tests.
This online, interactive lesson on hypothesis testing provides examples, exercises, and applets which includes tests in the normal model, Bernoulli Model, and two-sample normal model as well as likelihood ratio and goodness of fit tests.
This online, interactive lesson on geometric models provides examples, exercises, and applets which include Buffon's Problems, Bertrand's Paradox, and Random Triangles.
This online, interactive lesson on finite sampling models provides examples, exercises, and applets that include hypergeometric distribution, multivariate hypergeometric distribution, order statistics, the matching problem, the birthday problem, and the coupon collector problem.
This online, interactive lesson on the renewal processes provides examples, exercises, and applets which include renewal equations and renewal limit theorems.
This online, interactive lesson on probability spaces provides examples, exercises, and applets that cover conditional probability, independence, and several modes of convergence that are appropriate for random variables. This section also covers probability space, the paradigm of a random experiment and its mathematical model as well as sample spaces, events, random variables, and probability measures.
This site is a collection of Web-enabled scientific services & applications including Equation Plotter Software, Scientific Forecasting Software, Multiple Regression Software, Descriptive Statistics Software, Statistical Hypothesis Testing Software, Sample Size Software, and XML-RPC PHP client.
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.
Illustrates the central limit theorem by allowing the user to increase the number of samples in increments of 100, 1000, or 10000.