Resource Library

Using cooperative learning methods, this activity helps students develop a better intuitive understanding of what is meant by variability in statistics. Emphasis is placed on the standard deviation as a measure of variability. This lesson also helps students to discover that the standard deviation is a measure of the density of values about the mean of a distribution. As such, students become more aware of how clusters, gaps, and extreme values affect the standard deviation.

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 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.

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 tutorial explains the theory and use of the Wilcoxon Matched-Pairs Ranks test and demonstrates it with an example on project quality. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of Pearson's Product Moment Coefficient of Correlation and demonstrates it with an example on GPA and test scores. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of One-Way ANOVA and demonstrates it with an example on final exam scores. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of the Chi-Square Test for goodness of fit and demonstrates it with an example on mastery test scores. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of Multiple Regression and demonstrates it with an example on SAT scores and GPA. Data is given as well as SPSS and Minitab code.

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.