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# Lecture/Presentation

• ### Conditional Logistic Regression

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.

• ### Logistic Regression

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.

• ### Logistic Regression: Testing Homogeneity of the Odds Ratio

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: testing for homogeneity of the odds ratio across strata, test statistics for homogeneity (Wald, score, or likelihood ratio statistics), test statistics for homogeneity with ordinal data, logistic regression, and logit for selected sampling.

• ### Logistic Regression & Common Odds Ratios Part II (with Simulations)

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.

• ### Logistic Regression & Common Odds Ratios Part I

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: logistic regression on 3-dimensional table; estimating a common odds ratio; the Cochran, Mantel-Haenzel test; and confounding in logistic regression.

• ### Generalized Linear Model Estimation and Logistic Regression

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: iterative solutions to non-linear equations, score equations for exponential class variables, Newton-Raphson vs. Fisher’s Scoring, Logistic Regression for an R × 2 tables, saturated model, odds ratios when rows are not ordinal, goodness of fit, likelihood ratio statistic for nested models, and residuals.

• ### Generalized Linear Models for Poisson Data

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.

• ### Generalized Linear Models for Binary Data

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: linear probability model, non-constant variance, logistic model, logit transformation, and probit link.

• ### Introduction to Generalized Linear Models

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: linear regression, generalized linear models, link function, deviance, and modeling.

• ### 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.