Nonlinear Regression With R

Nonlinear Regression with R (Use R!)  eBooks & eLearning

Posted by advisors at Jan. 21, 2015
Nonlinear Regression with R (Use R!)

Nonlinear Regression with R (Use R!) By Christian Ritz, Jens Carl Streibig
2008 | 150 Pages | ISBN: 0387096159 | PDF | 4 MB

A Modern Approach to Regression with R (Repost)  eBooks & eLearning

Posted by step778 at Jan. 18, 2016
A Modern Approach to Regression with R (Repost)

Simon Sheather, "A Modern Approach to Regression with R"
2009 | pages: 397 | ISBN: 0387096078 | PDF | 12,4 mb

A Modern Approach to Regression with R by Simon Sheather [Repost]  eBooks & eLearning

Posted by BUGSY at May 18, 2015
A Modern Approach to Regression with R by Simon Sheather [Repost]

A Modern Approach to Regression with R (Springer Texts in Statistics) by Simon Sheather
English | Mar 11, 2009 | ISBN: 0387096078, 0387096086 | 397 Pages | PDF | 12 MB

This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models.

A Modern Approach to Regression with R (Springer Texts in Statistics)  eBooks & eLearning

Posted by AlenMiler at Oct. 13, 2014
A Modern Approach to Regression with R (Springer Texts in Statistics)

A Modern Approach to Regression with R (Springer Texts in Statistics) by Simon Sheather
Springer; 2009 edition | March 11, 2009 | English | ISBN: 0387096078, 0387096086 | 393 pages | PDF | 8 MB

This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models.

Uncertainty Analysis of Experimental Data with R  eBooks & eLearning

Posted by AlenMiler at July 10, 2017
Uncertainty Analysis of Experimental Data with R

Uncertainty Analysis of Experimental Data with R by Benjamin David Shaw
English | 6 July 2017 | ASIN: B073RLXQW3 | 206 Pages | AZW3 | 5.09 MB

Nonlinear Regression Modeling for Engineering Applications  eBooks & eLearning

Posted by Underaglassmoon at Sept. 2, 2016
Nonlinear Regression Modeling for Engineering Applications

Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments
Wiley | Mechanical Engineering | Aug 29 2016 | ISBN-10: 1118597966 | 400 pages | pdf | 7.42 mb

by R. Russell Rhinehart (Author)

Stochastic Approximation and NonLinear Regression  eBooks & eLearning

Posted by step778 at June 6, 2016
Stochastic Approximation and NonLinear Regression

Arthur E. Albert, Leland A. Gardner Jr., "Stochastic Approximation and NonLinear Regression"
2003 | pages: 211 | ISBN: 0262511487 | PDF | 7,8 mb

Nonlinear Regression Analysis and Its Applications (Repost)  eBooks & eLearning

Posted by step778 at Dec. 22, 2015
Nonlinear Regression Analysis and Its Applications (Repost)

Douglas M. Bates, Donald G. Watts, "Nonlinear Regression Analysis and Its Applications"
1988 | pages: 371 | ISBN: 0471816434 | DJVU | 3,4 mb
Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples by Sylvie Huet [Repost]

Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples (Springer Series in Statistics) by Sylvie Huet
English | Sep. 12, 2003 | ISBN: 0387400818 | 241 Pages | PDF | 1 MB

Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition.
Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples by Sylvie Huet

Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples (Springer Series in Statistics) by Sylvie Huet
Springer; 2nd edition | September 12, 2003 | English | ISBN: 0387400818 | 241 pages | PDF | 1 MB

Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap.