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LISA Statistics Short Course: Generalized Linear Models (GLMs) and Categorical Data Analysis (CDA)
(Academic)
LISA SHORT COURSES IN STATISTICS
LISA (Virginia Tech's 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 2015 Schedule:
Wednesday, June 24: Designing Experiments;
Wednesday, July 1: Basics of R;
Wednesday, July 8: Generalized Linear Models (GLMs) and Categorical Data Analysis (CDA);
Wednesday, July 15: Graphics in R;
Wednesday, July 22: Multivariate Clustering in R;
Wednesday, July 29: Sample Size Calculations;
Wednesday, August 5: Using mixed effects models to quantify dependency among repeated measures;
Wednesday, July 8, 4:00-6:00 pm;
Location: 1080 Torgersen Hall;
Instructor: Lin Zhang;
Title: Generalized Linear Models (GLMs) and Categorical Data Analysis (CDA);
Generally speaking, there are two types of outcomes (i.e. response) in statistical analysis: continuous and categorical responses. Linear Models (LM) are one of the most commonly used statistical methods to analyze continuous outcomes. However, many studies in Engineering, Medical Study, Education, etc. involve categorical outcomes. In these cases, Generalized Linear Models (GLM) are a more appropriate choice for analysis.
This short course will introduce the concept, theory, and application of GLM. Moreover, we will discuss some techniques commonly used in categorical data analysis, such as contingency table analysis, measures of association, tests of independence, tests of symmetry. Class demonstrations will be conducted using three real-world data sets listed below. All analysis will be carried out in R (a free statistical software http://cran.rstudio.com) via the RStudio interface (http://www.rstudio.com/ products/rstudio/download).
Example 1:
Researcher A is interested in how variables, including GRE, GPA and prestige of the undergraduate institution, affect admission status into graduate school. (Binary response)
Data set link: www.ats.ucla.edu/stat/data/binary.csv
Example 2:
Researcher B wants to predict the number of awards that a newly admitted student will earn by looking at the type of program in which the student was enrolled (vocational, general or academic) and the score of their final math exam. (Count response)
Data set link: www.ats.ucla.edu/stat/data/poisson_sim.csv
Example 3:
A Physicians' Health Study Research Group at Harvard Medical School wants to study the relationship between aspirin use (Placebo/Aspirin) and heart attacks (Fatal Attack/Nonfatal Attack/No Attack).
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More information...
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540-231-8354