This site gives an explanation, a definition and an example of sampling in statistical inference. Topics include parameters, statistics, sampling distributions, bias, and variability.
This site gives an explanation, a definition and an example of probability models. Topics include components of probability models and the basic rules of probability.
This site gives an explanation, a definition and an example of conditional probability. Topics include the probabilities of intersections of events and Bayes' formula.
This Java based applet gives students an opportunity to work through confidence interval problems for the mean. The material provides written word problems in which an individual must be able to correctly identify the given parts for a confidence interval calculation, and then be able to use this information to find the confidence interval. It gives step by step prompts to encourage students to choose the correct numbers and "cast of characters".
This site provides a collection of applets and their descriptions. Some of the titles include the Monte Carlo Estimation of Pi, Can You Beat Randomness?, One-Dimensional Random Walk, Two-Dimensional Random Walk, The Anthill and Molecular Motion, Diffusion Limited Aggregation, The Self-Avoiding Walk, Fractal Coastlines, and Forest Fires and Percolation.
This is the description and instructions for the One-Dimensional Random Walk applet. This Applet relates random coin-flipping to random motion. It strives to show that randomness (coin-flipping) leads to some sort of predictable outcome (the bell-shaped curve).
This is the description and instructions for the Two-Dimensional Random Walk applet. This Applet relates random coin-flipping to random motion but in more than one direction (dimension). It covers mean squared distance in the discussion.
This site provides the description and instructions for as well as the link to The Self-Avoiding Random Walk applet. In the SAW applet, random walks start on a square lattice and then are discarded as soon as they self-intersect. If a random walk survives after N steps, we compute the square of the distance from the origin, sum it up, and divide by the number of survivals. This variable is plotted on the vertical axis of the graph, which is plotted to the right of the field where random walks travel.
