Practical Regression

Data Mining with Python: Implementing Classification and Regression  eBooks & eLearning

Posted by FenixN at Nov. 23, 2016
Data Mining with Python: Implementing Classification and Regression

Data Mining with Python: Implementing Classification and Regression
HDRips | MP4/AVC, ~58 kb/s | 1280x720 | Duration: 02:03:25 | English: AAC, 128 kb/s (2 ch) | 297 MB
Genre: Development / Programming

A practical guide that will give you hands-on experience with the popular Python data mining algorithms.
Regression Analysis: Unified Concepts, Practical Applications, Computer Implementation

Regression Analysis: Unified Concepts, Practical Applications, Computer Implementation by Bruce Bowerman and Professor Emeritus
English | 2014 | ISBN: 1606499505 | 220 pages | PDF | 9 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.
Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting (repost)

Harvey Motulsky, Arthur Christopoulos, "Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting"
English | 2004-05-27 | ISBN: 0195171802 | 351 pages | PDF | 2.8 mb
Local Regression and Likelihood (Statistics and Computing) by Clive Loader

Local Regression and Likelihood (Statistics and Computing) by Clive Loader
English | July 30, 1999 | ISBN: 0387987754 | 305 pages | PDF | 1 MB

Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT.
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.
Solutions Manual to Accompany Introduction to Linear Regression Analysis, 5 edition

Solutions Manual to Accompany Introduction to Linear Regression Analysis, 5 edition by Ann G. Ryan, Douglas C. Montgomery, Elizabeth A. Peck and G. Geoffrey Vining
English | 2013 | ISBN: 1118471466 | ISBN-13: 9781118471463 | 164 pages | PDF | 45,3 MB

As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research.
Modern Regression Techniques Using R: A Practical Guide for Students and Researchers (repost)

Modern Regression Techniques Using R: A Practical Guide for Students and Researchers by Daniel B. Wright and Kamala London
English | ISBN: 1847879020, 1847879039 | 2009 | 216 pages | PDF | 2,4 MB

In a way that is refreshingly engaging and readable, Daniel B. Wright and Kamala London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books’ webpage via the SAGE homepage.
Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting (repost)

Harvey Motulsky, Arthur Christopoulos, "Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting"
English | 2004-05-27 | ISBN: 0195171802 | 351 pages | PDF | 2.8 mb

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 the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

Handbook of Regression Analysis  

Posted by interes at Aug. 2, 2013
Handbook of Regression Analysis

Handbook of Regression Analysis by Samprit Chatterjee and Jeffrey S. Simonoff
English | 2012 | ISBN: 0470887168 | 240 pages | PDF | 3 MB

A Comprehensive Account for Data Analysts of the Methods and Applications of Regression Analysis.
Written by two established experts in the field, the purpose of the Handbook of Regression Analysis is to provide a practical, one-stop reference on regression analysis.