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.

A sketch by Anastasia Mandel reinterpreting A Forest River by Isaac Ilyich Levitan (1890) with the statistical caption "A random regression forest; the statisticians are lost inside." This is part of a collection of sketches by Anastasia Mandel and their accompanying statistical captions discussed in the paper "How art helps to understand statistics" (Model Assisted Statistics and Applications, 2009) by Stan Lipovetsky and Igor Mandel in volume 4 pages 313-324. Free to use in classrooms and on course websites.

A song to be used in discussing the value of random selection in sampling and random assignment in experimentation. The lyrics were written by Mary McLellan from Aledo High School in Aledo, Texas as one of several dozen songs created for her AP statistics course. The song may be sung to the tune of the 2014 hit “All About that Bass,” by Meghan Trainor. Also, an accompanying video may be found at https://www.youtube.com/watch?v=br-5FtoYfkc

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: 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.

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: conditional independence, log-linear models for 2x2 tables, expected counts, logistic regression, odds ratio, parameters of interest for different designs and the MLEs, poisson log-linear model, double dichotomy, the multinomial, and the multinomial log-linear model.

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: ordinal regression models, cumulative probabilities, non-proportional odds, score stat for proportionl odds, MLEs, the adjacent categories logit, and proportional odds model.

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: unconditional likelihood, elimination of nuisance parameters, and Mantel-Haenzsel estimate.

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: the poisson log-linear model, poisson regression, estimated rate ratio, and negative binomial distribution.

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Pearson's residuals and rules for partitioning an I x J contingency tables as ways to determine association between variables.