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.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.
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First Sentence:
A scientific model is the abstraction of a real phenomenon or process that isolates those aspects relevant to a particular inquiry. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
squares reduction test, expected value parameterization, poppy counts, broad inference space, converged iterate, narrow inference space, cumulative volume profiles, intermediate inference space, median polishing, granulated lime, isotropic semivariogram models, weighted nonlinear least squares analysis, integral approximation methods, factorial classification, nonlinear mixed models, estimate mgt, nonlinear mean function, geostatistical data, empirical semivariogram, perfect interpolator, lag classes, kriging neighborhood, wheat yield data, natural denominator, nun procedure Key Phrases - Capitalized Phrases (CAPs): (learn more)
Estimate Error, Negative Binomial, Corrected Total, Class Level Information Class Levels Values, Procedure Model Information Data Set, Squares Square, Observations Not Used, Max Obs Per Subject, Blup Farm, Turnip Greens, Dimensions Covariance Parameters, Observations Read, Sum of Mean Approx Source, Confidence Parameter Estimate Std Error Limits, Tests of Fixed Effects, Statistics For Type, Changing the Mindset, Residual Variance Method Profile Fixed Effects, Value Value, Approx Approximate, Pearson Chi-Square, Scaled Pearson, Diagnosing Regression Models, Itl Intercept, Michigan State University New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
A scientific model is the abstraction of a real phenomenon or process that isolates those aspects relevant to a particular inquiry. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
squares reduction test, expected value parameterization, poppy counts, broad inference space, converged iterate, narrow inference space, cumulative volume profiles, intermediate inference space, median polishing, granulated lime, isotropic semivariogram models, weighted nonlinear least squares analysis, integral approximation methods, factorial classification, nonlinear mixed models, estimate mgt, nonlinear mean function, geostatistical data, empirical semivariogram, perfect interpolator, lag classes, kriging neighborhood, wheat yield data, natural denominator, nun procedure Key Phrases - Capitalized Phrases (CAPs): (learn more)
Estimate Error, Negative Binomial, Corrected Total, Class Level Information Class Levels Values, Procedure Model Information Data Set, Squares Square, Observations Not Used, Max Obs Per Subject, Blup Farm, Turnip Greens, Dimensions Covariance Parameters, Observations Read, Sum of Mean Approx Source, Confidence Parameter Estimate Std Error Limits, Tests of Fixed Effects, Statistics For Type, Changing the Mindset, Residual Variance Method Profile Fixed Effects, Value Value, Approx Approximate, Pearson Chi-Square, Scaled Pearson, Diagnosing Regression Models, Itl Intercept, Michigan State University New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 75
books:
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2
books
cite this book:
- Animal Reproduction: Invited Papers (Beltsville Symposia in Agricultural Research, Vol. 3) by H.W. Hawk on 4 pages
- Nonlinear Regression (Wiley Series in Probability and Statistics) by George A. F. Seber on page 204, page 231, and Back Matter
- Statistics for Spatial Data (Wiley Series in Probability and Statistics) by Noel Cressie on page 650, and Back Matter
- Modelling Longitudinal and Spatially Correlated Data (Lecture Notes in Statistics 122) (v. 122) by Timothy G. Gregoire in Back Matter (1), and Back Matter (2)
- Problems of Relative Growth (Foundations of Natural History) by Julian S. Huxley on page 85, and Back Matter
See all 75 books this book cites
- Statistical Methods for Spatial Data Analysis (Chapman & Hall/CRC Texts in Statistical Science) by Oliver Schabenberger in Back Matter
- SAS for Mixed Models, Second Edition by Ramon C. Littell in Back Matter
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