Logistic regression is one of the most widely used tools in statistical modelling, yet the gap between textbook theory and real-world practice remains a persistent challenge for students, researchers, and practitioners alike. This book bridges that gap with a comprehensive, hands-on guide to binary outcome modelling that goes well beyond the basics.
Built on a foundation of rigorous statistical theory, the book tackles the messy realities that applied analysts routinely face, including separation, rare events bias, overdispersion, and multicollinearity, offering clear and practical strategies for each. Rather than treating these as edge cases, the author positions them as central concerns deserving serious methodological attention. Modern variable selection strategies are examined in depth, contrasting traditional approaches with contemporary regularisation methods, while advanced topics such as Bayesian logistic regression and propensity score methods broaden the reader's analytical toolkit.
Throughout, statistical theory is integrated with computational methods and domain knowledge, grounded in reproducible R code, simulated examples, and real-world applications drawn from fields where the stakes of getting it wrong are high.
Whether you are an advanced undergraduate or graduate student studying regression modelling or applied statistics, a researcher navigating imbalanced outcomes in epidemiology or finance, or a data scientist seeking reliable methods for classification problems, this book offers the depth and practicality to meet you where you are and take your work further.