Statistical Models
What Is a Statistical Model?
Basics of Logistic Regression Modeling
The Bernoulli Distribution
Methods of Estimation
SAS Code
Stata Code
Logistic Models: Single Predictor
Models with a Binary Predictor
Predictions, Probabilities, and Odds Ratios
Basic Model Statistics
Models with a Categorical Predictor
Models with a Continuous Predictor
Prediction
SAS Code
Stata Code
Logistic Models: Multiple Predictors
Selection and Interpretation of Predictors
Statistics in a Logistic Model
Information Criterion Tests
The Model Fitting Process: Adjusting Standard Errors
Risk Factors, Confounders, Effect Modifiers, and Interactions
SAS Code
Stata Code
Testing and Fitting a Logistic Model
Checking Logistic Model Fit
Classification Statistics
Hosmer–Lemeshow Statistic
Models with Unbalanced Data and Perfect Prediction
Exact Logistic Regression
Modeling Table Data
SAS Code
Stata Code
Reference
Grouped Logistic Regression
The Binomial Probability Distribution Function
From Observation to Grouped Data
Identifying and Adjusting for Extra Dispersion
Modeling and Interpretation of Grouped Logistic Regression
Beta-Binomial Regression
SAS Code
Stata Code
References
Bayesian Logistic Regression
A Brief Overview of Bayesian Methodology
Examples: Bayesian Logistic Regression
SAS Code
Stata Code
Concluding Comments
References
Biography
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.
"The book presents many worked examples, and the choice of interesting data sets all of which are available to the reader is one of its greatest assets. Data availability makes it easy for readers to reproduce the examples from the book, and example code is available for R, SAS and Stata: R code is incorporated into the book chapters, and the end of each chapter gives SAS and Stata code."
—Ulrike Grömping, Beuth University of Applied Sciences Berlin, Journal of Statistical Software, July 2016"… this book is written in an exceptionally clear style … An additional selling point of this text is that it introduces new R functions, which can be applied in one’s own work, as well as equivalent SAS and Stata code. … the emphasis on understanding logistic regression modelling rather than on the mechanistic application of techniques is one of the great strengths of the book. Anyone who reads this book will therefore feel that they have a good understanding of this subject …"
—Significance Magazine, February 2016"Big Data is ascendant, but even the biggest data often boil down to a decision between two categories: survive or die, purchase or don’t purchase, click or don’t click, fraudulent or honest, default or pay. Logistic regression is the classic workhorse for this 0/1 data, and Joseph Hilbe’s new book presents a guide for the practitioner, chock full of useful R, Stata, and SAS code. Hilbe has worked with practitioners and aspiring practitioners in virtually every field that uses statistics, including for over a decade via his courses at Statistics.com. His new book is truly, in his own words, ‘a tutorial between you and me.’"
—Peter Bruce, Founder and President of the Institute for Statistics Education at Statistics.com
