Contemporary Statistical Models for the Plant and Soil Sciences

Despite its many origins in agronomic problems, statistics today is often unrecognizable in this context. Numerous recent methodological approaches and advances originated in other subject-matter areas and agronomists frequently find it difficult to see their immediate relation to questions that their disciplines raise. On the other hand, statisticians often fail to recognize the riches of challenging data analytical problems contemporary plant and soil science provides.The first book to integrate modern statistics with crop, plant and soil science, Contemporary Statistical Models for the Plant and Soil Sciences bridges this gap. The breadth and depth of topics covered is unusual. Each of the main chapters could be a textbook in its own right on a particular class of data structures or models. The cogent presentation in one text allows research workers to apply modern statistical methods that otherwise are scattered across several specialized texts. The combination of theory and application orientation conveys ìwhyî a particular method works and ìhowî it is put in to practice.About the CD-ROMThe accompanying CD-ROM is a key component of the book. For each of the main chapters additional sections of text are available that cover mathematical derivations, special topics, and supplementary applications. It supplies the data sets and SAS code for all applications and examples in the text, macros that the author developed, and SAS tutorials ranging from basic data manipulation to advanced programming techniques and publication quality graphics.Contemporary statistical models can not be appreciated to their full potential without a good understanding of theory. They also can not be applied to their full potential without the aid of statistical software. Contemporary Statistical Models for the Plant and Soil Science provides the essential mix of theory and applications of statistical methods pertinent to research in life sciences.

Statistical ModelsMathematical and Statistical ModelsFunctional Aspects of ModelsThe Inferential Steps ó Estimation and Testingt-Tests in Terms of Statistical ModelsEmbedding HypothesesHypothesis and Significance Testing ó Interpretation of the p-ValueClasses of Statistical ModelsData StructuresIntroductionClassification by Response TypeClassification by Study TypeClustered DataAutocorrelated DataFrom Independent to Spatial Data ó A Progression of ClusteringLinear Algebra ToolsIntroductionMatrices and VectorsBasic Matrix OperationsMatrix Inversion ó Regular and Generalized InverseMean, Variance, and Covariance of Random VectorsThe Trace and Expectation of Quadratic FormsThe Multivariate Gaussian DistributionMatrix and Vector DifferentiationUsing Matrix Algebra to Specify ModelsThe Classical Linear Model: Least Squares and AlternativesIntroductionLeast Squares Estimation and Partitioning of VariationFactorial ClassificationDiagnosing Regression ModelsDiagnosing Classification ModelsRobust EstimationNonparametric Regression Nonlinear ModelsIntroductionModels as Laws or ToolsLinear Polynomials Approximate Nonlinear ModelsFitting a Nonlinear Model to DataHypothesis Tests and Confidence IntervalsTransformationsParameterization of Nonlinear ModelsApplicationsGeneralized Linear ModelsIntroductionComponents of a Generalized Linear ModelGrouped and Ungrouped DataParameter Estimation and InferenceModeling an Ordinal ResponseOverdispersionApplicationsLinear Mixed Models for Clustered DataIntroductionThe Laird-Ware ModelChoosing the Inference SpaceEstimation and InferenceCorrelations in Mixed ModelsApplicationsNonlinear Models for Clustered DataIntroductionNonlinear and Generalized Linear Mixed ModelsTowards an Approximate Objective FunctionApplicationsStatistical Models for Spatial DataChanging the MindsetSemivariogram Analysis and EstimationThe Spatial ModelSpatial Prediction and the Kriging ParadigmSpatial Regression and Classification ModelsAutoregressive Models for Lattice DataAnalyzing Mapped Spatial Point PatternsApplicationsBibliography