This book covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. By harnessing the capabilities of the logistic model, analysts can better understand their data, make appropriate predictions and classifications, and determine the odds of one value of a predictor compared to another. Complete Stata, SAS, and R codes are available in the text and on the author's website, enabling analysts to adapt the code as needed.

Joseph M. Hilbe is a Solar System Ambassador with NASA's Jet Propulsion Laboratory at the California Institute of Technology, an adjunct professor of statistics at Arizona State University, and an emeritus professor at the University of Hawaii. He also teaches five web-based courses on statistical modeling at Statistics.com. He is president of the International Astrostatistics Association, elected fellow of the American Statistical Association, elected member of the International Statistical Institute, and full member of the American Astronomical Society.
Professor Hilbe is one of the world's leading statisticians in modeling discrete and longitudinal data. He has authored 16 books related to statistical modeling, including the best-selling Logistic Regression Models and Modeling Count Data.
During the late 1980s and 1990s, Professor Hilbe was a leading figure in the then new area of health outcomes research, serving as director of research at a national chain of hospitals and later CEO of a national health economics firm. He was also on the executive committee forming the Health Policy Statistics Section of the American Statistical Association.

Statistical Models. Logistic Models: Single Predictor. Logistic Models: Multiple Predictors. Testing and Fitting a Logistic Model. Grouped Logistic Regression. Bayesian Logistic Regression.