This set of pages is an introduction to Maximum Likelihood Estimation. It discusses the likelihood and log-likelihood functions and the process of optimizing.
This journal article gives examples of erroneous beliefs about probability. It specifically examines the belief that a random sample must be representative of the true population.
This journal article describes a set of experiments in which different methods of teaching Bayes' Theorem were compared to each other. The frequency representation of the rule was found to be easier to learn than the probability representation.
This site gives an explanation, a definition and an example of chi-square goodness of fit test. Topics include chi-square test statistics, tests for discrete and continuous distributions.
This is a virtual linear regression calculator. It allows students to enter data points, experiment with where the line of best fit might be, and then lets them see the correct line of best fit as well as the outliers. You can click through the introduction.
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.
