Resource Library

A short discussion of what outliers are and their helpfulness in analyzing data.

This exercise uses descriptive statistics to analyze a data set about how rats respond to rock music vs. classical music.

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.

This set of exercises asks students to model relationships and test them based on the chi-square distribution. The data used is based on testosterone levels and delinquency rate of American military men.

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), that not all histograms are easy to classify, that there is a difference between models (normal, uniform) and characteristics (skewness, symmetry, etc.). Key words: Histogram, shape, normal, uniform, skewed, symmetric, bimodal

This site offers links to a multitude of data tables in PDF format. Topics include national trends in injury hospitalizations, trends in health and aging, summary health statistics for the U.S. population, trends in health insurance and access to medical care for children under age 19 years, and many more.

This site provides links to tests and quizzes for the Statistics and Data Analysis for Public Policy and Sociology course at Duke University for 1992 through 1995.

In this handout, students are asked to compare the ages of terminated employees to the ages of retained employees. Students will use the comparison to decide if the data supports the conclusion of age discrimination.

The datasets on this page are classified by analysis technique (ANOVA, Linear Regression, Markov Chain Monte Carlo, Nonlinear Regression, and Univariate Summary Statistics) and by level of difficulty (lower, average, higher). They were originally intended to test statistical software.