**Contemporary Statistical Models for the Plant and Soil Sciences** by Oliver Schabenberger and Francis J. Pierce is a comprehensive textbook that provides an in-depth exploration of statistical methods relevant to plant and soil sciences. The book is structured into nine main chapters, each focusing on different aspects of statistical modeling, including linear models, nonlinear models, generalized linear models, mixed models, spatial data analysis, and more. It also includes appendices with mathematical details and special topics, as well as a CD-ROM containing additional resources such as data sets, SAS and S-PLUS code, and tutorials. The book begins with an introduction to statistical models, discussing the difference between mathematical and statistical models, the functional aspects of models, and the inferential steps involved in estimation and testing. It then delves into various classes of statistical models, including linear and nonlinear models, regression and analysis of variance models, univariate and multivariate models, fixed, random, and mixed effects models, generalized linear models, and errors in variable models. The text emphasizes the importance of statistical software in applying these models, with a focus on the SAS system. It provides detailed explanations of statistical methods, along with practical examples and applications in plant and soil sciences. The book also addresses the challenges of analyzing clustered, spatial, and non-normal data, and discusses the use of mixed models and spatial data analysis techniques. The authors highlight the need for modern statistical methods in plant and soil sciences, emphasizing that traditional methods are often insufficient for the complex data structures encountered in these fields. The book is written for students and researchers in the life sciences, providing a balance between theoretical foundations and practical applications. It includes numerous case studies and examples, making it a valuable resource for both academic and applied settings. The CD-ROM accompanying the book offers additional support, with data sets, code, and tutorials to facilitate learning and application of the statistical methods discussed.**Contemporary Statistical Models for the Plant and Soil Sciences** by Oliver Schabenberger and Francis J. Pierce is a comprehensive textbook that provides an in-depth exploration of statistical methods relevant to plant and soil sciences. The book is structured into nine main chapters, each focusing on different aspects of statistical modeling, including linear models, nonlinear models, generalized linear models, mixed models, spatial data analysis, and more. It also includes appendices with mathematical details and special topics, as well as a CD-ROM containing additional resources such as data sets, SAS and S-PLUS code, and tutorials. The book begins with an introduction to statistical models, discussing the difference between mathematical and statistical models, the functional aspects of models, and the inferential steps involved in estimation and testing. It then delves into various classes of statistical models, including linear and nonlinear models, regression and analysis of variance models, univariate and multivariate models, fixed, random, and mixed effects models, generalized linear models, and errors in variable models. The text emphasizes the importance of statistical software in applying these models, with a focus on the SAS system. It provides detailed explanations of statistical methods, along with practical examples and applications in plant and soil sciences. The book also addresses the challenges of analyzing clustered, spatial, and non-normal data, and discusses the use of mixed models and spatial data analysis techniques. The authors highlight the need for modern statistical methods in plant and soil sciences, emphasizing that traditional methods are often insufficient for the complex data structures encountered in these fields. The book is written for students and researchers in the life sciences, providing a balance between theoretical foundations and practical applications. It includes numerous case studies and examples, making it a valuable resource for both academic and applied settings. The CD-ROM accompanying the book offers additional support, with data sets, code, and tutorials to facilitate learning and application of the statistical methods discussed.