Product Details
Contemporary Statistical Models for the Plant and Soil Sciences

Contemporary Statistical Models for the Plant and Soil Sciences
By Oliver Schabenberger, Francis J. Pierce

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Product Description

A pioneering text that integrates modern statistics with practical problems in soil and crop science, Contemporary Statistical Models for the Plant and Soil Sciences provides real-life examples that help students understand the material. Advances in technology have significantly altered the type, amount, quality, timing, format, and the source of data available. This book shows students how to apply these techniques to their work. The techniques presented include: mixed linear models, generalized linear models, classification models, non-linear regression, and spatial statistics. A CD-ROM is included that allows students to work problems on their own.


Product Details

  • Amazon Sales Rank: #1286271 in Books
  • Published on: 2001-11-13
  • Original language: English
  • Number of items: 1
  • Binding: Hardcover
  • 738 pages

Editorial Reviews

Review
"This text [presents] many of the newer statistical modeling techniques for data analysis using examples familiar to plant and soil scientistskeeping 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

From the Back Cover
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-ROM
The 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.