Resource Library

This simulation shows recorded response times on a simple motor task under two conditions. Various statistics and graphs used to compare the distributions are presented.

This simulation illustrates types of sums of squares in a 2 x 3 ANOVA.

This applet lets you explore the effect of violations of the assumptions of normality and homogeneity of variance on the type I error rate and power of t tests (and two-group analysis of variance).

This case study addresses the question: "Does the mere presence of a weapon increase the accessibility of aggressive thoughts?" It concerns the following concepts: quantile and box plots, stem and leaf displays, one-sample t test, confidence interval, within-subjects ANOVA, and consequences of violation of normality assumption.

This case study assesses the question, "Can the application of magnetic fields be an effective treatment for pain?" It addresses concepts including: boxplots, stem and leaf displays, correlated t-test, two-sample t-test, repeated measures analysis of variance, and analysis of covariance.

This activity guides students through the process of checking the validity of data, performing summary analysis, constructing box plots, and determining whether significant differences exist. The data comes from a study of mineral levels in older adults and is available in Minitab, Excel, SAS, and text formats.

This online textbook provides information on the statistical analysis of nutritional data. Techniques covered include data cleaning, descriptive statistics, histograms, graphics, scatterplots, outlier identification, regression and correlation, confounding, and interactions. Each chapter includes exercises with real data and self-tests to be used with SPSS.

This site has Youtube video tutorials and screen capture tutorials about how to make graphical displays and perform statistical tests using JMP.

This online, interactive lesson on the Poisson process provides examples, exercises, and applets. Specific topics include the exponential distribution, gamma distribution, Poisson distribution, splitting a Poisson process, analogy with Bernoulli trials, and higher dimensional Poisson processes.

This material is a detailed exercise for students in introductory statistics. Students are asked to collect a random sample of data from a real estate website; conduct descriptive statistics (including confidence intervals); and write a report summarizing their dataset. The primary learning goals are to teach students 1) how to obtain a random sample; 2) how to interpret confidence intervals; 3) how to simulate and interpret a sampling distribution; and 4) how to communicate descriptive statistics.