Resource Library

This website provides data files, examples, guides that are referenced in David Howell's textbook published in 2013. There is also a student manual and links to other useful websites.

This page provides survey data on the sexual activity of male and female subjects and discusses choosing appropriate statistics to describe the data as well as reporting bias. It also links to a Chance article about the same study.

Chance News reviews current issues in the news that use probability or statistical concepts. Its aim is to give the general public a better understanding of chance news as reported by the media and to allow teachers of probability and statistics courses to liven up their courses with current chance news.

This page was written as instructions for a SAS lab assignment, but the example can be used with other programs. The study compares three treatments for rape victims against each other and a control group to see which treatment is most effective at reducing Post Traumatic Stress Disorder symptoms.

As described in the web page itself: "This document was prepared as an illustration of the use of both t tests and correlation/regression analysis in drawing conclusions from data in an actual study." The study compares athletic performance of swimmers that are optimists vs. pessimists.

This correlation and regression example compares performance on reading comprehension questions to performace on the SAT. It also compares those who read the passage referred to by the questions to those who did not. Exercise questions and answers are also provided.

This example is designed to test whether religiosity is correlated with optimism. The page describes the study, has a link to the data set, and describes the method of analysis. Analysis includes ANOVA and regression.

Residual plots and other diagnostics are important to deciding whether or not linear regression is appropriate for a set of data. Many students might believe that if the correlation coefficient is strong enough, these diagnostic checks are not important. The data set included in this activity was created to lure students into a situation that looks on the surface to be appropriate for the use of linear regression but is instead based (loosely) on a quadratic function. Key words: regression, residuals

An important objective in hiring is to ensure diversity in the workforce. The race or gender of individuals hired by an organization should reflect the race or gender of the applicant pool. If certain groups are under-represented or over-represented among the employees, then there may be a case for discrimination in hiring. On the other hand, there may be a number of random factors unrelated to discrimination, such as the timing of the interview or competition from other employers, that might cause one group to be over-represented or under-represented. In this exercise, we ask students to investigate the role of randomness in hiring, and to consider how this might be used to help substantiate or refute charges of discrimination. Key words: Probability distribution, binomial distribution, computer simulation, decision rules

This activity leads students to appreciate the usefulness of simulations for approximating probabilities. It also provides them with experience calculating probabilities based on geometric arguments and using the bivariate normal distribution. We have used it in courses in probability and mathematical statistics, as well as in an introductory statistics course at the post-calculus level. Students are expected to approximate the solution through simulation before solving it exactly. They are also expected to employ graphical as well as algebraic problem-solving strategies, in addition to their simulation analyses. Finally, students are asked to explain intuitively why it makes sense for the probabilities to change as they do. Key words: simulation, probability, geometry, independence, bivariate normal distribution