Contemporary statistical models for the plant and soil sciences
著者
書誌事項
Contemporary statistical models for the plant and soil sciences
CRC Press, c2002
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注記
System requirements: Windows 95/98/NT/2000 or Macintosh, Solaris, OS2; Web browser such as Microsoft Internet Explorer or Netscape Navigator
Includes bibliographical references (p. 703-720) and indexes
HTTP:URL=http://www.loc.gov/catdir/enhancements/fy0646/2001043254-d.html Information=Publisher description
内容説明・目次
内容説明
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 iwhyi a particular method works and ihowi 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.
目次
Statistical Models. Data Structures. Linear Algebra Tools. The Classical Linear Model: Least Squares and Alternatives. Nonlinear Models. Generalized Linear Models. Linear Mixed Models for Clustered Data. Nonlinear Models for Clustered Data. Statistical Models for Spatial Data. Bibliography.
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