This book, *Statistics for Biology and Health: Mixed Effects Models and Extensions in Ecology with R*, is a comprehensive guide to analyzing ecological data using mixed effects models and extensions. The authors, Alain F. Zuur, Elena N. Ieno, Neil J. Walker, Anatoly A. Saveliev, and Graham M. Smith, provide detailed explanations and practical examples of various statistical methods, including generalized linear models (GLMs), generalized additive models (GAMs), and mixed effects models (MEMs). The book covers topics such as data exploration, model selection, and validation, with a focus on handling common issues in ecological data, such as nested data, heterogeneity of variance, spatial and temporal correlation, and zero-inflated data. Each chapter includes case studies and R code, making it a valuable resource for both students and researchers in ecology and environmental science. The book also introduces Bayesian Monte-Carlo Markov-Chain applications in generalized linear modeling, providing an introduction to this advanced statistical technique. The authors thank various contributors for their support and feedback, and the book includes a preface, acknowledgements, and a detailed table of contents.This book, *Statistics for Biology and Health: Mixed Effects Models and Extensions in Ecology with R*, is a comprehensive guide to analyzing ecological data using mixed effects models and extensions. The authors, Alain F. Zuur, Elena N. Ieno, Neil J. Walker, Anatoly A. Saveliev, and Graham M. Smith, provide detailed explanations and practical examples of various statistical methods, including generalized linear models (GLMs), generalized additive models (GAMs), and mixed effects models (MEMs). The book covers topics such as data exploration, model selection, and validation, with a focus on handling common issues in ecological data, such as nested data, heterogeneity of variance, spatial and temporal correlation, and zero-inflated data. Each chapter includes case studies and R code, making it a valuable resource for both students and researchers in ecology and environmental science. The book also introduces Bayesian Monte-Carlo Markov-Chain applications in generalized linear modeling, providing an introduction to this advanced statistical technique. The authors thank various contributors for their support and feedback, and the book includes a preface, acknowledgements, and a detailed table of contents.