Posted by **FenixN** at Nov. 23, 2016

HDRips | MP4/AVC, ~58 kb/s | 1280x720 | Duration: 02:03:25 | English: AAC, 128 kb/s (2 ch) | 297 MB

A practical guide that will give you hands-on experience with the popular Python data mining algorithms.

Posted by **interes** at Dec. 27, 2015

English | 2014 | ISBN: 1606499505 | 220 pages | PDF | 9 MB

Posted by **BUGSY** at Aug. 31, 2015

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.

Posted by **interes** at Feb. 20, 2015

English | 2004-05-27 | ISBN: 0195171802 | 351 pages | PDF | 2.8 mb

Posted by **tanas.olesya** at Dec. 7, 2014

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.

Posted by **tanas.olesya** at Oct. 15, 2014

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.

Posted by **interes** at Aug. 29, 2014

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.

Posted by **interes** at March 27, 2014

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 booksâ€™ webpage via the SAGE homepage.

Posted by **interes** at Dec. 26, 2013

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.

Posted by **interes** at Aug. 2, 2013

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.