Resource Library

This introductory probability textbook, freely available here in pdf format, emphasizes the use of computing to simulate experiments and make computations. A set of programs that go with the book and the answers to the odd-numbered problems are also available from this site. Chapter headings include: Discrete Probability, Continuous Probability Densities, Combinatorics, Conditional Probability, Distributions and Densities, Expected Value and Variance, Sums of Random Variables, Law of Large Numbers, Central Limit Theorem, Generating Functions, Markov Chains, and Random Walks.

The Decision Bonsai are a hybrid of concept maps and decision trees. They were originally developed to give introductory statistics students a map to inference procedures but have evolved to be used for other topics. The tree is 'grown' during the semester so that students build a picture of the relationships in their mind. Recent work is moving toward the development of more complete concept maps for introductory statistics, statistical quality methods and probability and stochastic processes courses. These Decision Bonsai would be then pointed to at appropriate points in the concept maps.

This website serves as an online textbook for introductory statistics, covering topics such as summarizing and presenting data, producing data, variation and probability, statistical inference, and control charts.

This tutorial exposes students to conducting multiple comparisons in SPSS. This html based tutorial provides extensive screen shots and an example data set. Topics covered in the tutorial include one way ANOVA, preplanned contrasts, Bonferroni, Post Hoc Tukey's HSD, and Scheffe's multiple contrasts.

This tutorial exposes students to conducting chi-square tests in SPSS. This html based tutorial provides extensive screen shots and an example data set.

This article, in a series, describes a game, which tests opposing strategies through aspects of experiemental design.

This article describes a method to calculate the least squares line algebraically. First, the author uses a numeric example, which uses calculus, then describes a simpler algebraic method.

The following exercise can illustrate the problem of bias in estimators to students in statistics courses. In some advanced courses an alternative estimator may be presented and properties of this estimator may be investigated via Monte Carlo studies.

This article provides the example of student form orders to demonstrate the unreliability of combining data from two different distributions (or subjects).

A specially-designed statistical literacy course is needed for college students in majors that don't require statistics or mathematics. This paper suggests that key topics in conditional probability, multivariate regression and the vulnerability of statistical significance to confounding should be included and presents some new ways to teach these ideas.