Resource Library

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 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 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 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.

This applet allows you to see the effect of various transformations on the relationship between two variables. The site lets you input your own data or allows you to choose from one of the given sets. The site also gives you instructions and excercises.

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

This article describes an interactive activity illustrating general properties of hypothesis testing and hypothesis tests for proportions. Students generate, collect, and analyze data. Through simulation, students explore hypothesis testing concepts. Concepts illustrated are: interpretation of p-values, type I error rate, type II error rate, power, and the relationship between type I and type II error rates and power. This activity is appropriate for use in an introductory college or high school statistics course. Key words: hypothesis test on a proportion, type I and II errors, power, p-values, simulation

This article describes an interactive activity illustrating sampling distributions for means, properties of confidence intervals, properties of hypothesis testing, confidence intervals for means, and hypothesis tests for means. Students generate and analyze data and through simulation explore these concepts. The activity is completed in three parts. The three parts of the activity can be used in sequence or they can be used individually as "stand alone" activities. This allows the educator flexibility in utilizing the activity. Part I illustrates the sampling distribution of the sample mean. Part II illustrates confidence intervals for the population mean. Part III illustrates hypothesis tests for the population mean. This activity is appropriate for use in an introductory college or high school AP statistics course. Key words: sampling distribution of a sample mean, confidence interval for a mean, hypothesis test on a mean, simulation, random rectangles