Resource Library

This site links to the article "Use of R as a Toolbox for Mathematical Statistics Exploration," to activities demonstrating the use of R programming language, and to the site where users can download R. Activities cover the following topics: calculation of a running variance, maximization of a non-linear function, resampling of a statistic, simple Bayesian modeling, sampling from multivariate normal, and estimation of power.

This site give a definition and an example of boxplots. Topics include outliers, medians, as well as lower and upper quartiles.

This collection of applets simulate many different statistical concepts such as: distributions, correlation, hypothesis testing, regression, and ANOVA.

This site gives a definition and an example of scatterplots. Topics include positive and negative association.

The goal of this lesson is to introduce the concepts of mean, median and mode and to develop understanding and familiarity with these ideas. The activity lets students explore mean and median in an efficient way and the discussion helps them to formalize their knowledge of measures of center.

Develops the idea of the transition matrix and what it can tell you.

This lesson on observational studies discusses the nature of such studies, the relationships between various data sets, and regression. Graphs illustrate the relationships, and exercises at the end test the user's comprehension and understanding. It is taken from the online textbook for West. Mich. Univ. online introductory stats course.

The function of this site is to collect, compile, analyse, abstract and publish statistical information relating to the commercial, industrial, financial, social, economic and general activities and condition of the people.

A good resource for problems in statistics in engineering. Contains some applets, and good textual examples related to engineering. Some topics include Monte Carlo method, Central Limit Theorem, Risk, Logistic Regression, Generalized Linear .Models, and Confidence.

Discusses Markov chains, transition probabilities, and the transition probability matrix.