Resource Library

Song covering a variety of statistical topics. May be sung to the tune of John Lennon's 1969 song "Give Peace a Chance." Lyrics by Armin Schwartzman (December, 2003). This song is part of the "Stanford Statistics Songbook" found at www.bscb.cornell.edu/~hooker/StanfordStatisticsSongbook.pdf Free to use for non-commercial educational purposes. Contact author to use in publications or for commercial purposes. Musical accompaniment realization and vocals are by Joshua Lintz from University of Texas at El Paso.

Song about the benefits of the Bayesian approach to statistics. May be sung to the tune of Sonny and Cher's 1965 song "I Got You Babe." Lyrics by Matthew Finkelman (December, 2003). This song is part of the "Stanford Statistics Songbook" found at www.bscb.cornell.edu/~hooker/StanfordStatisticsSongbook.pdf Free to use for non-commercial educational purposes. Contact author to use in publications or for commercial purposes. Musical accompaniment realization by Joshua Lintz male vocals by Joshua Lintz and female vocals by Marianna Sandoval from University of Texas at El Paso.

A joke about the tendency for Math and Statistics textbooks to have an abundance of homework style problems.

A cartoon to teach about the value of confidence intervals compared with just giving a point estimate. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.

A cartoon to teach about the interpretation of confidence statements. The cartoon plays on the idea of what would happen if the same process was repeated over-and-over again. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.

This presentation is a part of a series of lessons on the Analysis of Categorical Data.  This lecture overs the following: covariance patterns and generalized estimating equations (GEE). 

This presentation is a part of a series of lessons on the Analysis of Categorical Data.  This lecture overs the following: conditional logistic regression, conditional likelihood for matched pairs, the non-central hypergeometric, the conditional maximum likelihood estimator (CMLE), conditional confidence interval for odds ratios, and McNemar's statistic.

This presentation is a part of a series of lessons on the Analysis of Categorical Data.  This lecture overs the following:  odds ratio, dependent proportion, marginal homogeneity, McNemar's Test, marginal homogeneity for greater than 2 levels, measures of agreement, and the kappa coefficient (weighted vs. unweighted).

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: sparse tables, sampling zeros, structural zeros, and log-linear model (and limitations).

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: partial/conditional tables, confounding, types of independence (mutual, joint, marginal, and conditional), identifiability constraints, partial odds ratios, hierarchical log-linear model, pairwise interaction log-linear model, conditional independence log-linear model, goodness of fit, and model building.