# Lecture/Presentation

• ### Statistics: Model Hypothesis Testing for Astronomers

This presentation was given by Aneta Siemiginowska at the 4th International X-ray Astronomy School (2005), held at the Harvard-Smithsonian Center for Astrophysics in Cambridge, MA.

• ### Statistics: Basic Concepts for Astronomers

This presentation was given by Aneta Siemiginowska at the 4th International X-ray Astronomy School (2005), held at the Harvard-Smithsonian Center for Astrophysics in Cambridge, MA.

• ### Introduction to Correlated Binary Data

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).

• ### Conditional Logistic Models for Matched Pairs

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.

• ### Models for Matched Pairs

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).

• ### Log-linear Models - Sparse Tables

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).

• ### Log-linear Models for Multidimensional Contingency Tables

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.

• ### Introduction to Log-linear Models

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.

• ### Logit Models for Multinomial Responses Part II

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.

• ### Logit Models for Multinomial Responses Part I

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: generalized odds ratio, collapsed categories, polytomous (or multinomial) logistic regression, and maximum likelihood using the multinomial.