Jeffrey Wilson is Professor of Statistics and Biostatistics, and Associate Dean of Research in W. P. Carey School of Business, Arizona State University, Tempe. He is the former Statistics Associate Editor for The Journal of Minimally Invasive Gynecology and the Faculty Athletics Representative for Arizona State University. He has published more than 90 articles in leading journals such as Statistics in Medicine, American Journal of Public Health, Journal of Royal Statistics Series C, Management Science, Journal of Business and Economic Statistics, Computational Statistics, and Australian Journal of Statistics, among others.Kent A. Lorenz is Associate Professor of Physical Education and Physical Activity in the Department of Kinesiology at San Francisco State University. He teaches courses in physical fitness, and elementary and secondary curriculum and instruction in the Integrated Teacher Education Program in Physical Education, and the introduction to statistics course for the Masters of Science in Kinesiology degree program. His research interests center on youth physical activity and physical fitness, with a particular emphasis on Comprehensive School Physical Activity Programs. Dr. Lorenz has published 25 peer-reviewed journal articles, contributed to various book chapters and the first edition of the Modeling Correlated Binary Data using SAS, SPSS and R.Lori P. Selby is a PhD candidate in the School of Mathematics and Statistics. She was a lecturer in Mathematics and Biostatistics at the University of Trinidad and Tobago. She is a member of the AmericanStatistical Association (ASA) and the Arizona Chapter of ASA. She is also a member of the American Public Health Association.

• Introduction to Binary logistic Regression.- Growth of the Logistic Regression Model.- Standard Binary Logistic Regression Model.- Overdispersed Logistic Regression Model.- Weighted Logistic Regression Model.- Generalized Estimating Equations Logistic Regression.- Generalized Method of Moments logistic regression Model.- Exact Logistic Regression Model.- Two-Level Nested Logistic Regression Model.- Hierarchical Logistic Regression models.- Fixed Effects Logistic Regression Model.- Heteroscedastic Logistic Regression Model.