Regression Model Validation

Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments

R. Russell Rhinehart, "Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments"
ISBN: 1118597966 | 2016 | EPUB | 400 pages | 11 MB

Model Validation and Uncertainty Quantification, Volume 3  eBooks & eLearning

Posted by AvaxGenius at June 8, 2017
Model Validation and Uncertainty Quantification, Volume 3

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics 2017 By Robert BarthorpeRoland PlatzIsrael LopezBabak MoaveniCostas Papadimitriou
English | PDF | 2017 | 368 Pages | ISBN : 3319548573 | 21.5 MB

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics, 2017, the third volume of ten from the Conference brings together contributions to this important area of research and engineering.

Risk Model Validation (repost)  eBooks & eLearning

Posted by libr at April 30, 2017
Risk Model Validation (repost)

Risk Model Validation by Christian Meyer and Peter Quell
English | 2011 | ISBN: 1906348510 | ISBN-13: 9781906348519 | 124 pages | PDF | 2 MB

Model Validation and Uncertainty Quantification, Volume 3  eBooks & eLearning

Posted by ChrisRedfield at April 18, 2017
Model Validation and Uncertainty Quantification, Volume 3

H. Sezer Atamturktur, Babak Moaveni, Costas Papadimitriou, Tyler Schoenherr - Model Validation and Uncertainty Quantification, Volume 3
Published: 2015-04-25 | ISBN: 3319152238, 3319386077 | PDF | 372 pages | 22.91 MB
Solutions Manual to Accompany Introduction to Linear Regression Analysis, 5 edition (repost)

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

Model Validation and Uncertainty Quantification, Volume 3  eBooks & eLearning

Posted by roxul at July 25, 2016
Model Validation and Uncertainty Quantification, Volume 3

Sez Atamturktur and Tyler Schoenherr, "Model Validation and Uncertainty Quantification, Volume 3"
English | ISBN: 3319297538 | 2016 | 379 pages | PDF | 24 MB

Topics in Model Validation and Uncertainty Quantification, Volume 5  eBooks & eLearning

Posted by ChrisRedfield at March 22, 2015
Topics in Model Validation and Uncertainty Quantification, Volume 5

Todd Simmermacher, Scott Cogan, Babak Moaveni, Costas Papadimitriou - Topics in Model Validation and Uncertainty Quantification, Volume 5
Published: 2013-05-31 | ISBN: 146146563X, 1489996044 | PDF | 264 pages | 12 MB
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.

Risk Model Validation  eBooks & eLearning

Posted by interes at June 15, 2014
Risk Model Validation

Risk Model Validation by Christian Meyer and Peter Quell
English | 2011 | ISBN: 1906348510 | ISBN-13: 9781906348519 | 124 pages | PDF | 2 MB

Senior management are expected to make crucial business decisions using complex risk models that, without specialized quantitative financial knowledge, can lead to ill judged choices.

Linear Regression  eBooks & eLearning

Posted by AvaxGenius at Oct. 19, 2017
Linear Regression

Linear Regression By David J. Olive
English | EPUB | 2017 | 499 Pages | ISBN : 3319552503 | 4.75 MB

This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables.