Applied Multivariate Statistics in Geohydrology and Related Sciences / Edition 1

by Charles E. Brown

Overview

It has been evident from many years of research work in the geohydrologic sciences that a summary of relevant past work, present work, and needed future work in multivariate statistics with geohydrologic applications is not only desirable, but is necessary. This book is intended to serve a broad scientific audience, but more specifi­ cally is geared toward scientists doing studies in geohydrology and related geo­ sciences.lts objective is to address both introductory and advanced concepts and applications of the multivariate procedures in use today. Some of the procedures are classical in scope but others are on the forefront of statistical science and have received limited use in geohydrology or related sciences. The past three decades have seen a significant jump in the application of new research methodologies that focus on analyzing large databases. With more general applications being developed by statisticians in various disciplines, multivariate quantitative procedures are evolving for better scientific application at a rapid rate and now provide for quick and informative analyses of large datasets. The procedures include a family of statistical research methods that are alternatively called "multivariate analysis" or "multivariate statistical methods".

Product Details

ISBN-13:	9783642803307
Publisher:	Springer Berlin Heidelberg
Publication date:	12/16/2011
Edition description:	Softcover reprint of the original 1st ed. 1998
Pages:	248
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

I Introduction to General Statistical and Multivariate Concepts.- 1 General Concepts.- 2 Introduction to Multivariate Statistical Procedures.- II Variable-Directed Procedures Based on Normal Distribution Assumptions.- 3 Correlation.- 4 Factor Analysis.- 5 Canonical Correlation.- 6 Multiple Regression.- 7 Multivariate Analysis of Variance.- 8 Multivariate Analysis of Covariance.- III Variable-Directed Techniques not Based on Normal Distribution Assumptions.- 9 Principal Components.- IV Individual-Directed Techniques Based on Normal Distribution Assumptions.- 10 Multiple Discriminant Analysis.- V Individual-Directed Techniques not Based on Normal Distribution Assumptions.- 11 Cluster Analysis.- 12 Multiple Logistic Regression.- VI Other Approaches to Explore Multivariate Data.- 13 Coefficient of Variation.- 14 Correspondence Analysis.- 15 Multivariate Probit Analysis.- VII Multivariate Measures of Space, Distance, and Time.- 16 Multivariate Time Series Modeling.- 17 Multivariate Spatial Measures.- VIII Multivariate Data Preparation, Plotting, and Conclusions.- 18 Multivariate Data Preparation and Plotting.- 19 Summary and Generalizations of Multivariate Quantitative Procedures.- Appendix Introduction to Numerical Analysis.- A.1 General Concepts.- A.2 Solution of Simultaneous Linear Algebraic Equations.- A.2.1 Sets of Linear Equations.- A.2.2 Calculations in Matrix Algebra.- A.2.3 Definitions and Notation.- A.2.4 Basic Matrix Operations.- A.2.4.1 Comparison of Matrices.- A.2.4.2 Matrix Addition and Subtraction.- A.2.4.3 Matrix Multiplication.- A.2.4.4 Transposition.- A.2.4.5 Partitioning.- A.2.5 Matrix Inversion.- A.2.5.1 Inverse.- A.2.5.2 Computation of Inverse.- A.2.6 Eigenvalues and Eigenvectors of a Matrix.- A.2.7 Matrix Algebra and Solution of Simultaneous Linear Equations.- A.2.8 Direct Methods: Gaussian Elimination.- A.2.9 Indirect Methods: Gauss-Seidel Iteration.- A.3 Multivariate Normal Distribution in Matrix Form.- References.- Author Index.