Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000002137697 | S592.14 W42 1990 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
This book describes statistical methods suitable for analyzing variation in soil and for relating soil to its environment. The authors stress sound sampling technique and show how to use the results for estimation, prediction, and efficient design. They show how classification can enhance the utility of survey data and lead to economies in sampling. Optimal methods for creating classification are described, and alternative multivariate methods are set forth for identifying relations such as principal component and co-ordinate analysis. The book expands and revises the author's Quantitative and Numerical Methods in Soil Classification and Survey. It includes information on regression, as used in both statistics and natural science. Three new chapters devoted to geostatistics introduce regionalized variable theory, and cover such applications as the variogram, its modelling, kriging, and isorithmic mapping. As with the first edition, the book stresses the full quantitative survey of land resources, measurement, and estimation. Many simple illustrations and tables are included to clarify the text.
Reviews 1
Choice Review
Webster and Oliver address scientists in pedology, engineering, ecology, and geography who study natural resources by utilizing surveys. They describe methods for making surveys quantitative, stressing the need for sound sampling, sensible and efficient estimation, and proper planning. Because of the nature of the author's experience, the emphasis and examples throughout the book are on sampling soil and analyzing soil data. In a sense this is unfortunate, because the topics and methods discussed have much broader applicability. A very valuable resource is in danger of being missed by those looking only at the title or giving the contents a very cursory review. The authors present a thorough discussion of techniques for sampling properties that vary continuously in space, and a large selection of the modern methods of dealing with the resulting data, such as regression, correlation measures of similarity and distance, principal components, classification and clustering, spatial dependence, and kriging. They place many of the relatively complex statistical techniques well within the reach of advanced undergraduate students and scientists with modest training in statistics. F. Giesbrecht North Carolina State University
Table of Contents
| 1 Introduction |
| 2 Quantitative Description of Variable Material |
| 3 Sampling and Estimation |
| 4 Generalization, Prediction, and Classification |
| 5 Relations Between Variables: Covariance and Correlation |
| 6 Regression |
| 7 Relations Between Individuals: Similarity |
| 8 Ordination |
| 9 Analysis of Dispersion and Discrimination |
| 10 Numerical Classification: Hierarchical Systems |
| 11 Numerical Classification: Non-Hierarchical Methods |
| 12 Spatial Dependence |
| 13 Nested Sampling and Analysis |
| 14 Local Estimation: Kriging |
