A cartoon to teach about the need for statistical techniques in drawing out the salient features in massive data sets. 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 "Boy Viewing Mount Fuji" by Katsushika Hokusai (1839) with the statistical caption "Laplace distribution in the Far East." This is part of a collection of sketches by Anastasia Mandel and their accompanying statistical captions written by Stan Lipovetsky and Igor Mandel that took first place in the cartoon & art category of the 2009 A-Mu-sing contest sponsored by CAUSE. The collection 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.
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: 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.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: maximum likelihood estimation for logistic regression, sample size requirements for approximate normality of the MLE’s, confidence intervals, likelihood ratio statistic, score test statistic, deviance, Hosmer-Lemeshow goodness-of-fit statistic, the Hosmer-Lemeshow statistic, parameter estimates, scaled/unscaled estimates, residuals, grouped binomials, and model building strategies.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Mantel-Haenszel estimator of common odds ratio, confounding in logistic regression, univariate/multivariate analysis, bias vs. variance, and simulations.
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.
A song for use in helping students explore Simpson’s paradox and recognize how a third variable might drive the relationship between two others. Lyrics & Music © 2016 Monty Harper.This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).