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As one of the classical statistical regression techniques, and
often the first to be taught to new students, least squares fitting
can be a very effective tool in data analysis. Given measured data,
we establish a relationship between independent and dependent
variables so that we can use the data predictively. The main
concern of "Least Squares Data Fitting with Applications" is how to
do this on a computer with efficient and robust computational
methods for linear and nonlinear relationships. The presentation
also establishes a link between the statistical setting and the
computational issues. In a number of applications, the accuracy and
efficiency of the least squares fit is central, and Per Christian
Hansen, Victor Pereyra, and Godela Scherer survey modern
computational methods and illustrate them in fields ranging from
engineering and environmental sciences to geophysics. Anyone
working with problems of linear and nonlinear least squares fitting
will find this book invaluable as a hands-on guide, with accessible
text and carefully explained problems. Included are: an overview of
computational methods together with their properties and
advantages; topics from statistical regression analysis that help
readers to understand and evaluate the computed solutions; and many
examples that illustrate the techniques and algorithms. "Least
Squares Data Fitting with Applications" can be used as a textbook
for advanced undergraduate or graduate courses and professionals in
the sciences and in engineering.
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