Contemporary Statistical Models for the Plant and Soil Sciences [With CD-ROM] (Hardcover)

Statistical ModelsMathematical and Statistical ModelsFunctional Aspects of ModelsThe Inferential Steps o Estimation and Testingt-Tests in Terms of Statistical ModelsEmbedding HypothesesHypothesis and Significance Testing o Interpretation of the p-ValueClasses of Statistical ModelsData StructuresIntroductionClassification by Response TypeClassification by Study TypeClustered DataAutocorrelated DataFrom Independent to Spatial Data o A Progression of ClusteringLinear Algebra ToolsIntroductionMatrices and VectorsBasic Matrix OperationsMatrix Inversion o 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