Logistic regression is a commonly used statistical technique to understand data with binary outcomes (success-failure), or where outcomes take the form of a binomial proportion. The objective of logistic regression is to estimate the probability that an outcome will assume a certain value. This course covers the functional form of the logistics model and how to interpret model coefficients. R, Stata, and SAS code is provided for all examples used during the course.
Dr. James Hardin
Dr. James Hardin is an Associate Dean of Faculty Affairs and Curriculum Professor at the University of South Carolina. Co-author (with Joseph Hilbe) of Generalized Estimating Equations, Dr. Hardin is on the editorial board of The Stata Journal and is the developer of the Stata GEE command, and with Dr. Hilbe is the developer of the GLM command.