Contemporary Statistical Models for the Plant and Soil Sciences

摘要: Statistical Models Mathematical and Functional Aspects of The Inferential Steps o Estimation Testing t-Tests in Terms Embedding Hypotheses Hypothesis Significance Interpretation the p-Value Classes Data Structures Introduction Classification by Response Type Study Clustered Autocorrelated From Independent to Spatial A Progression Clustering Linear Algebra Tools Matrices Vectors Basic Matrix Operations Inversion Regular Generalized Inverse Mean, Variance, Covariance Random Trace Expectation Quadratic Forms Multivariate Gaussian Distribution Vector Differentiation Using Specify Classical Model: Least Squares Alternatives Partitioning Variation Factorial Diagnosing Regression Robust Nonparametric Nonlinear as Laws or Polynomials Approximate Fitting a Model Tests Confidence Intervals Transformations Parameterization Applications Components Grouped Ungrouped Parameter Inference Modeling an Ordinal Overdispersion Mixed for Laird-Ware Choosing Space Correlations Towards Objective Function Changing Mindset Semivariogram Analysis Prediction Kriging Paradigm Autoregressive Lattice Analyzing Mapped Point Patterns Bibliography