Handbook of Nonlinear Regression Models

Handbook of Nonlinear Regression Models
Author: David A. Ratkowsky
Publisher:
Total Pages: 272
Release: 1990
Genre: Mathematics
ISBN:

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The background; An introduction to regression modeling; Nonlinear regression modeling; An illustrative example of regression modeling; The models; Models with one X variable, convex/concave curves; Models with one X variable, sigmoidally shaped curves; Models with one X variable, curves with maxima and minima; Models with more than one explanatory viariable; Other models and excluded models; Obtaining good initial parameter estimates; Summary; References; Table of symbols; Appendix; Author index; Subject index.


Handbook of Nonlinear Regression Models
Language: en
Pages: 272
Authors: David A. Ratkowsky
Categories: Mathematics
Type: BOOK - Published: 1990 - Publisher:

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The background; An introduction to regression modeling; Nonlinear regression modeling; An illustrative example of regression modeling; The models; Models with o
Handbook of Nonlinear Regression Models
Language: en
Pages: 259
Authors: Donald C Blaisdell
Categories:
Type: BOOK - Published: 1990-12-01 - Publisher:

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Handbook of Regression Methods
Language: en
Pages: 654
Authors: Derek Scott Young
Categories: Mathematics
Type: BOOK - Published: 2018-10-03 - Publisher: CRC Press

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Handbook of Regression Methods concisely covers numerous traditional, contemporary, and nonstandard regression methods. The handbook provides a broad overview o
Nonlinear Regression Modeling
Language: en
Pages: 296
Authors: David A. Ratkowsky
Categories: Mathematics
Type: BOOK - Published: 1983 - Publisher:

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Introduction to regression models; Assessing nonlinearity in nonlinear regression models; Yield-density models; Sigmoidal growth models; Asymptotic regression m
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