**Summary:** "Statistical Methods for Spatial Data Analysis" by Oliver Schabenberger and Carol A. Gotway is a comprehensive textbook that introduces the theory and methods of spatial statistics. The book covers a wide range of topics, including spatial data types, autocorrelation, spatial regression models, and simulation techniques. It is designed for graduate-level courses and provides a thorough treatment of spatial data analysis, emphasizing model-based and frequentist approaches. The text also discusses Bayesian hierarchical models, though they are introduced later in the book. The authors highlight the importance of spatial autocorrelation in spatial data analysis and provide detailed explanations of various statistical methods, such as kriging, spatial regression, and non-stationary covariance models. The book includes examples and applications from various fields, such as environmental science, epidemiology, and agriculture. It is structured into chapters that build upon each other, starting with an introduction to spatial data and progressing to more advanced topics. The authors also emphasize the importance of simulation in spatial data analysis and provide practical guidance on implementing statistical methods. The book is accompanied by a website that provides additional resources, data sets, and software code for the methods discussed. Overall, the text serves as an essential resource for students and researchers interested in spatial statistics and its applications.**Summary:** "Statistical Methods for Spatial Data Analysis" by Oliver Schabenberger and Carol A. Gotway is a comprehensive textbook that introduces the theory and methods of spatial statistics. The book covers a wide range of topics, including spatial data types, autocorrelation, spatial regression models, and simulation techniques. It is designed for graduate-level courses and provides a thorough treatment of spatial data analysis, emphasizing model-based and frequentist approaches. The text also discusses Bayesian hierarchical models, though they are introduced later in the book. The authors highlight the importance of spatial autocorrelation in spatial data analysis and provide detailed explanations of various statistical methods, such as kriging, spatial regression, and non-stationary covariance models. The book includes examples and applications from various fields, such as environmental science, epidemiology, and agriculture. It is structured into chapters that build upon each other, starting with an introduction to spatial data and progressing to more advanced topics. The authors also emphasize the importance of simulation in spatial data analysis and provide practical guidance on implementing statistical methods. The book is accompanied by a website that provides additional resources, data sets, and software code for the methods discussed. Overall, the text serves as an essential resource for students and researchers interested in spatial statistics and its applications.