# Lecture Examples

• ### Partitioning Chi Squares and Residual Analysis

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.

• ### Ordinal Associations of I × J Contingency Tables

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: linear association, correlation coefficient, ridits/modified ridits, nonparametric methods, Cochran-Armitage Trend test,

• ### Summary Measures

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: uncertainty coefficient, ordinal trends, the gamma statistic and linear association, conditional independence, marginal independence, and Simpson's Paradox.

• ### Testing Independence

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: generalized IxJ contingency tables, degrees of freedom, Fisher's exact test, and generalized odds ratio.

• ### Contingency Tables Part II

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Pearson's chi-square; the empirical logit; and prospective, case-control, and cross-sectional studies.

• ### Contigency Tables Part II

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Pearson's chi-square; the empirical logit; and prospective, case-control, and cross-sectional studies.

• ### Contingency Tables Part I

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: 2x2 contingency tables, fixing columns and rows, MLE, and previous topics within the context of contingency tables (variance, confidence intervals, standard error approximation, likelihood ratio, etc.).

• ### Association Measures and Variance Estimation

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: absolute/relative measures, number needed to treat (NNT), relative risk, odds ratio, the delta method (with a multivariate extension), and a variance covariance matrix.

• ### Random Effects Model (and more) - Part II

This presentation discusses modeling cluster correlation explicitly through random effects, yielding a generalized linear mixed effects models (GLMM). Part II contains many examples of application to different studies.

• ### Random Effects Model - Part I

This presentation discusses modeling cluster correlation explicitly through random effects, yielding a generalized linear mixed effects models (GLMM).