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online courses: GLM and Categorical Data Analysis

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Fri, 25 Feb 2011 16:59:14 +0000

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 Learning a new statistical technique can be like acquiring the proverbial hammer -- all problems then resemble nails. Lurking next is the most common statistical error: asking the wrong question (because you want to apply your newly-learned technique). Two antidotes: 1. Our course in Generalized Linear Models (GLM), which gives you a perspective on a number of different techniques in a single theoretical framework. April 1-29 with Joe Hilbe and James Hardin. 2. Our course in Categorical Data Analysis, which approaches analysis from the other end - recognize the type of data you have, and then assess what different techniques are appropriate. April 1-29 with Brian Marx. GLM: GLM extends ordinary least squares (OLS) regression to incorporate responses other than normal. This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis. Continuous response variables, the log normal, gamma, log-gamma (survival analysis), and inverse Gaussian cases are covered. Binomial (logit, probit, and others) as well as count models (poisson, negative binomial, geometric) are also touched. Joe Hilbe and James Hardin are the co-authors of “Generalized Linear Models and Extensions” (Stata Press) as well as “Generalized Estimating Equations” (CRC Press). They have lectured widely in these areas, and have been instrumental in developing computer routines for these methods – routines that have been incorporated into popular statistical software programs. Details are here http://www.statistics.com/courses/social-science/glm/ Categorical Data Analysis: This course will cover the analysis of contingency tables (where cells represent counts of subjects or items falling into certain categories). Topics include tests for independence (comparing proportions as well as chi-square), exact methods, and treatment of ordered data. Both 2-way and 3-way tables are covered. A modeling approach to categorical data analysis will also be presented, as special cases of the GLM, specifically Poisson regression for count responses and logistic/probit regression for binomial responses. The focus will be on interpretation of models rather than the theory behind them. Brian Marx is Professor of Statistics at Louisiana State University, and has taught Categorical Data Analysis for over ten years. He is currently serving as Chair of the Statistical Modeling Society and is the Coordinating Editor of “Statistical Modeling: An International Journal.” Details are here http://www.statistics.com/courses/stat-methods/categorical1/ Participants can ask questions and exchange comments with the instructors via a private discussion board throughout the period. Each course requires about 15 hours a week and there are no set times when you are required to be online. Hope to see you online at statistics.com! Janet Dobbins You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.