Resource Library

This tutorial opens with a survey on polling. Upon completing the survey, students are taken through an election example which uses polling to explain random sampling, bias, margin of error, and confidence intervals.

This page gives a history of notation and symbols and who developed them for combinatorial analysis, the normal distribution, probability, and statistics. Quotes from the first papers to use these symbols are also given.

CAST contains three complete introductory statistics courses, one advanced statistical methods course, and additional modules. Each introductory course presents the same topics, but with different applications. The first is a general version, the second is a biometric version with examples relating to biological, agricultural and health sciences, and the third is a business version. Each course comes in a student version and a lecture version. The additional modules cover Multiple and Nonlinear Regression, Quality Control, and Simulation. Registration is required, but free. Individuals or classes can register.

These pages from the University of Melbourne explain statistical concepts using various examples from medicine, science, sports, and finance. The intent is not computational skill but conceptual understanding. Some pages also contain data.

This glossary gives definitions for numerous statistical terms, concepts, methods, and rules.

This page uses Bayes' Theorem to calculate the probability of a hypothesis given a datum. An example about cancer is given to help users understand Bayes' Theorem and the calculator. Key Word: Conditional Probability.

This page will calculate the factorial of any number.

This tutorial introduces mean, median, mode, variance, and standard deviation using sports statistics from the Internet and class-generated statistics. Students should understand stem-and-leaf plots before using this tutorial. This material is intended for class use. Excel spreadsheets with sample data are also available for download. The relation links to a letter for teachers.

This article introduces Radial Basis Function (RBF) networks. These networks rely heavily on regression analysis techniques. Topics include Nonparametric Regression, Classification and Time Series Prediction, Linear Models, Least Squares, Model Selection Criteria, Ridge Regression, and Forward Selection.

This tutorial introduces the basic concepts of probability using various examples. Topics include interpreting probability, calibration experiments, interpreting odds, sample space, basic rules, equally likely outcomes, constructing probability tables, unions and complements, mean, and two-way probability tables. A link to activities is also given.