Resource Library

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.

This page contains information and links about statistical literacy. Some links are to textbooks, online articles, resources, and information about upcoming events.

This page discusses disadvantages of large datasets with regard to Simpson's Paradox.

This page has data sets used by UCLA statistics classes. The html files in the second column contain descriptions of a particular data set and a link to the data at the end of the file. There are also .dat and .dta files that contain just data, with no description.

This site gives the outlines and shows the lessons for psychology 340/341: Advanced Statistical Methods.

This page is a collection of examples, demonstrations, and exercises that can be used to motivate a lecture, demonstrate an important point, or create a laboratory exercise for students. Topics include the following: Descriptives, Normal Distribution, Sampling Distributions, Probability, Chi-Square, t tests, Power, Correlation/Regression, One-way Anova, Multiple Comparisons, Factorial Anova, Repeated Measures, Multiple Regression, General Linear Model, Log Linear Models, and Distribution-Free Tests.

Each dataset in this collection includes description of the study, description of the data file, statistical topic covered, and reference. Topics addressed include: correlation, one-way ANOVA, Bonferroni multiple comparison procedure, regression (simple, multiple, and loglinear), chi-square, and the t-test.

The applets in this section demonstrate basic issues of experimental design. The Poor Experimental Design ignores randomization rules and allows for increased experimental error. The Improved Experimental Design offers improvement over the first design by adding randomization and reducing experimental error. Both applets require the input of several participants. The purpose of the applets is to test the reaction times between a participant's dominant and non-dominant hand. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/ExpDesign.html

The applets in this section allow you to see how the common Xbar control chart is constructed with known variance. The Xbar chart is constructed by collecting a sample of size n at different times t. The process is considered to be out of control if the sample mean of the current sample falls above or below the control limits. The user has the options to change each parameter individually and all at once. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/ControlCharts.html