1st Edition
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 downloadable resources
The accompanying downloadable resources are 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.
Mathematical and Statistical Models
Functional Aspects of Models
The Inferential Steps ó Estimation and Testing
t-Tests in Terms of Statistical Models
Embedding Hypotheses
Hypothesis and Significance Testing ó Interpretation of the p-Value
Classes of Statistical Models
Data Structures
Introduction
Classification by Response Type
Classification by Study Type
Clustered Data
Autocorrelated Data
From Independent to Spatial Data ó A Progression of Clustering
Linear Algebra Tools
Introduction
Matrices and Vectors
Basic Matrix Operations
Matrix Inversion ó Regular and Generalized Inverse
Mean, Variance, and Covariance of Random Vectors
The Trace and Expectation of Quadratic Forms
The Multivariate Gaussian Distribution
Matrix and Vector Differentiation
Using Matrix Algebra to Specify Models
The Classical Linear Model: Least Squares and Alternatives
Introduction
Least Squares Estimation and Partitioning of Variation
Factorial Classification
Diagnosing Regression Models
Diagnosing Classification Models
Robust Estimation
Nonparametric Regression
Nonlinear Models
Introduction
Models as Laws or Tools
Linear Polynomials Approximate Nonlinear Models
Fitting a Nonlinear Model to Data
Hypothesis Tests and Confidence Intervals
Transformations
Parameterization of Nonlinear Models
Applications
Generalized Linear Models
Introduction
Components of a Generalized Linear Model
Grouped and Ungrouped Data
Parameter Estimation and Inference
Modeling an Ordinal Response
Overdispersion
Applications
Linear Mixed Models for Clustered Data
Introduction
The Laird-Ware Model
Choosing the Inference Space
Estimation and Inference
Correlations in Mixed Models
Applications
Nonlinear Models for Clustered Data
Introduction
Nonlinear and Generalized Linear Mixed Models
Towards an Approximate Objective Function
Applications
Statistical Models for Spatial Data
Changing the Mindset
Semivariogram Analysis and Estimation
The Spatial Model
Spatial Prediction and the Kriging Paradigm
Spatial Regression and Classification Models
Autoregressive Models for Lattice Data
Analyzing Mapped Spatial Point Patterns
Applications
Bibliography
Biography
Francis J. Pierce, Oliver Schabenberger
"This text [presents] many of the newer statistical modeling techniques for data analysis using examples familiar to plant and soil scientists…keeping the mathematical complexity to a minimum. I applaud the authors for their efforts to bring the current state of the area of statistical modeling into the realm of the plant and soil sciences."
--Clarence E. Watson, Experimental Statistics and Plant and Soil Sciences, Mississippi State University, USA
"My overall impression is that it is a superbly crafted text replete with many carefully chosen examples that instructively demonstrate contemporary models and modelling practices. The authors' attention to fine detail in the presentation of materials is evident in every chapter. Researchers, instructors, and students alike doubtlessly will find the snippets of SAS code and specially tailored macros to be of immense value when fitting data to the contemporary models described in this treatise."
--Timothy Gregoire, School of Forestry and Environmental Studies, Yale University, New Haven , USA
