Oceanography calls for a wide variety of mathematical and
statistical techniques, and this accessible treatment provides the
basics every oceanographer needs to know, including
* Practical ways to deal with chemical, geological, and biological
oceanographic data
* Instructions on detecting the existence of patterns in what
appears to be noise
* Numerous examples from the field that highlight the application
of the methods presented
Written by an oceanographer and based on his successful course
at the University of Hawaii, the volume is well suited to a
two-semester course at the graduate level. The book reviews the
necessary calculus, clarifies statistical concepts, and includes
end-of-chapter problems that illustrate and expand the various
topics. Tips on using MATLAB(r) software in matrix operations
complement chapters that deal with the formulation of relationships
in terms of matrices.
The main body of the text covers the actual methods of dealing with
data--including least squares and linear regression, correlation
functions and analysis of variance, means and error bounds,
nonlinear techniques and weighted least squares, numerical
integration, and other modeling techniques. Unlike most
introductory texts, Mathematical Methods for Oceanographers
discusses regression methods in great detail, and includes an
analysis of why certain methods produce unbiased parameter
estimates. Finally, the chapter on time series analysis covers an
area of particular interest to physical oceanographers.
The numerous problems and solutions included in the book enable
readers to check their understanding of concepts and techniques as
well as their ability to apply what they have learned.
A must-read for students of oceanography, this text/reference is
also useful for professionals in the field, as well as for
fisheries scientists, biologists, and those in the environmental
sciences.
A systematic introduction to the mathematics oceanographers
need
Topics covered in Mathematical Methods for Oceanographers
include:
* A review of the necessary calculus
* Model I linear regression
* Correlation analysis
* Model II linear regression
* Polynomial curve fitting, linear multiple regression analysis,
and nonlinear least squares
* Numerical integration
* Box models
* Time series analysis
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