Resource Library

Using cooperative learning methods, this activity provides students with 24 histograms representing distributions with differing shapes and characteristics. By sorting the histograms into piles that seem to go together, and by describing those piles, students develop awareness of the different versions of particular shapes (e.g., different types of skewed distributions, or different types of normal distributions), and that not all histograms are easy to classify. Students also learn that there is a difference between models (normal, uniform) and characteristics (skewness, symmetry, etc.).

An interactive box plot applet that allows users to put in their own data that is part of a large collection of platform independent, interactive, java applets and activities for K-12 mathematics and teacher education.

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.

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 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 page will calculate the factorial of any number.

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 glossary defines and explains statistical terms for introductory students. The glossary can be shown in alphabetical order or in suggested learning order. Click on the topic of interest to see the definition. Use the arrows at the bottom to proceed to the next topic or click the blue dot to return to the contents page.

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

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.