Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments / Edition 1

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Overview

The preferred method of data analysis of quantitative experiments is the method of least squares. Often, however, the full power of the method is overlooked and very few books deal with this subject at the level that it deserves. The purpose of Data Analysis Using the Method of Least Squares is to fill this gap and include the type of information required to help scientists and engineers apply the method to problems in their special fields of interest. In addition, graduate students in science and engineering doing work of experimental nature can benefit from this book. Particularly, both linear and non-linear least squares, the use of experimental error estimates for data weighting, procedures to include prior estimates, methodology for selecting and testing models, prediction analysis, and some non-parametric methods are discussed.

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Editorial Reviews

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Aus den Rezensionen:

"… Die … Methode der kleinsten Quadrate … ist … eines der am häufigsten angewendeten statistischen Verfahren zur Aus- und Bewertung der Daten. John Wolberg … bedauert, dass dieser Methode noch längst nicht die ihr zustehende Aufmerksamkeit … gewidmet wird … Die publizistische Lücke … möchte der Autor mit diesem Buch schließen. … Das Buch vermittelt das Wissen für eine höchst effiziente Methode zur Datenanalyse. Der Autor erläutert die Thematik in dem … Buch abstrakt konzentriert und untermauert sie durch einfache, überwiegend hypothetische Beispiele, die zum … Verständnis der Methode beitragen ..."

(Dr. Norbert Höfert, in: Gefahrstoffe - Reinhaltung der Luft, 2007, Vol. 67, Issue 4, S. 136)

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Product Details

  • ISBN-13: 9783540256748
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 2/10/2006
  • Edition description: 2006
  • Edition number: 1
  • Pages: 250
  • Product dimensions: 6.10 (w) x 9.30 (h) x 0.60 (d)

Table of Contents

The Method of Least Squares.- Model Evaluation.- Candidate Predictors.- Designing Quantitative Experiments.- Software.- Kernel Regression.

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