This site gives an explanation, a definition and an example of inference in linear regression. Topics include confidence intervals for intercept and slope, significance tests, mean response, and prediction intervals.
This site is a glossary of statisical terms searchable by topic or in alphabetical order. Topics include: Basic Definitions, Presenting Data, Sampling, Probability, Confidence Intervals, Hypothesis Testing, Paired Data, Correlation and Regression, Design of Experiments and ANOVA, Categorical Data, Non-parametric Methods, and Time Series Data.
This online, interactive lesson on foundations provides examples, exercises, and applets which review the algebra of sets and functions, general relations with special emphasis on equivalence relations and partial orders, and some basic combinatorial structures such as permuations and combinations.
This online, interactive lesson on games of chance provides examples, exercises, and applets which include Poker, Poker dice, Chuck-a-Luck, Craps, Roulette, The Monty Hall Problem, lotteries, and Red and Black.
This online, interactive lesson on distributions provides examples, exercises, and applets which explore the basic types of probability distributions and the ways distributions can be defined using density functions, distribution functions, and quantile functions.
This online, interactive lesson on expected value provides examples, exercises, and applets in which students will explore relationships between the expected value of real-valued random variables and the center of the distribution. Students will also examine how expected values can be used to measure spread and correlation.
This site provides the description and instructions for as well as the link to the Diffusion Limited Aggregation: Growing Fractal Structures applet. This applet strives to describe, classify, and measure different random fractal patterns in nature.
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.
