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LISA Statistics Short Course: Generalized Linear Models
LISA SHORT COURSES IN STATISTICS
LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data. See www.lisa.stat.vt.edu/?q=short_courses for instructions on how to REGISTER and to learn more.
Summer 2011 Schedule:
June 13: What LISA Can Do for You and a Tutorial in T-Tests and ANOVA*;
June 21 & 22: Introduction to R;
June 28 & 29: Linear Regression and Structural Equation Models;
July 11: Mixed Models and Random Effects*;
July 19 & 20: Generalized Linear Models;
*Course to be held in Fralin Auditorium. Other courses will be in 3060 Torgerson Hall
Wednesday, July 20;
Instructor: Nels Johnson;
Title: Generalized Linear Models;
Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with a certain chemical; and modeling the number of insects caught by a certain kind of trap. These types of situations can often be modeled well by a large class of regression models called generalized linear models (GLM). We will go over some of the basic statistical concepts of GLM and how it is relates to regression using normal errors. We will also go through some data analysis examples of GLMs in popular software such as R and SAS (possibly JMP if time allows) and explain how we interpret some of the output from each software. If time allows, the Bayesian approach to GLM will also be discussed.
This course will also be taught on 7/19/11.
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