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eBook Multivariate Statistical Techniques in Environmental Science ePub

eBook Multivariate Statistical Techniques in Environmental Science ePub

by Martin,James N. James N.

  • ISBN: 1566704294
  • Category: Earth Sciences
  • Subcategory: Math Science
  • Author: Martin,James N. James N.
  • Publisher: CRC Press
  • ePub book: 1331 kb
  • Fb2 book: 1162 kb
  • Other: azw rtf doc mbr
  • Rating: 4.4
  • Votes: 668

Description

Explore the latest publications in Multivariate Techniques, and find Multivariate Techniques experts. Aim Spatial variations of environmental conditions translate into biogeographical patterns of tree growth.

Explore the latest publications in Multivariate Techniques, and find Multivariate Techniques experts. Publications (1,853). This fact is used to identify the origin of timber by means of dendroprovenancing. Yet, dendroprovenancing attempts are commonly only based on ringwidth measurements, and largely neglect additional tree–ring variables.

Multivariate statistics is used extensively in environmental science . In this chapter some important statistical methods such as Principal component analysis (PCA), Canonical correspondence analysis (CCA), Redundancy analysis (RDA), Cluster analysis, and Discriminate function analysis will be explained briefly. 2 540 Earth and Environmental Sciences Landscape ecology is perhaps best distinguished by its focus on: 1) spatial heterogeneity, 2) broader spatial extents than those traditionally studied in ecology, and 3) the role of humans in creating and affecting landscape patterns and process (Turner et al, 2001).

Multivariate statistical tools were applied to reduce the dimensionality of the data set and to evaluate the relative importance of combinations of environmental variables on sediment dynamics. The results showed that the variability of the hydrochemistry and the sediment chemistry is controlled by longitudinal and seasonal factors, whereas differences across the river section were nonsignificant

Before moving on with these 10 techniques, I want to differentiate between statistical learning and machine learning.

It is important to accurately assess the performance of a method, to know how well or how badly it is working. Before moving on with these 10 techniques, I want to differentiate between statistical learning and machine learning.

Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling. Another unique feature of this book is the discussion of database management systems. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required.

The goal is to present the current state of the art in multivariate analysis methods while attempting to place them on a firm statistical basis. The first half of the book would be suitable for an advanced undergraduate or graduate multivariate analysis course.

A complete introduction to multivariate statistics for environmental science. Introductory Multivariate Statistics for the Environmental Science is a targeted text for those who are dealing with data collected in the field from environmental sources. After first providing a broad overview of what multivariate statistics is, the book goes on to cover topics such as measurements of ecological diversity, linear regression, ordination, principal components analysis, correspondence analysis, classification, and various other multivariate techniques.

Автор: Shaw Название: Introductory Multivariate Statistics for the Environmental Science Издательство . Описание: This book presents the authors' personal selection of topics in multivariate statistical analysis with emphasis on tools and techniques.

Описание: This book presents the authors' personal selection of topics in multivariate statistical analysis with emphasis on tools and techniques.

Modern Multivariate Statistical Techniques. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Gareth James is a professor of data sciences and operations at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.