Nonlinear Regression Analysis and Its Applications

Nonlinear Regression Analysis and Its Applications
Author: Douglas M. Bates
Publisher: Wiley-Interscience
Total Pages: 398
Release: 2007-04-23
Genre: Mathematics
ISBN:

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Provides a presentation of the theoretical, practical, and computational aspects of nonlinear regression. There is background material on linear regression, including a geometrical development for linear and nonlinear least squares.


Nonlinear Regression Analysis and Its Applications
Language: en
Pages: 398
Authors: Douglas M. Bates
Categories: Mathematics
Type: BOOK - Published: 2007-04-23 - Publisher: Wiley-Interscience

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Provides a presentation of the theoretical, practical, and computational aspects of nonlinear regression. There is background material on linear regression, inc
Nonlinear Regression
Language: en
Pages: 768
Authors: George A. F. Seber
Categories: Mathematics
Type: BOOK - Published: 2005-02-25 - Publisher: John Wiley & Sons

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WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an ef
Nonlinear Regression Modeling for Engineering Applications
Language: en
Pages: 402
Authors: R. Russell Rhinehart
Categories: Mathematics
Type: BOOK - Published: 2016-09-26 - Publisher: John Wiley & Sons

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Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which co
Fitting Models to Biological Data Using Linear and Nonlinear Regression
Language: en
Pages: 352
Authors: Harvey Motulsky
Categories: Mathematics
Type: BOOK - Published: 2004-05-27 - Publisher: Oxford University Press

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Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by th
Applied Statistics in Agricultural, Biological, and Environmental Sciences
Language: en
Pages: 672
Authors: Barry Glaz
Categories: Technology & Engineering
Type: BOOK - Published: 2020-01-22 - Publisher: John Wiley & Sons

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Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference