Distribution in Statistics

Learn the Normal or Gaussian distribution in statistics  eBooks & eLearning

Posted by naag at Feb. 13, 2016
Learn the Normal or Gaussian distribution in statistics

Learn the Normal or Gaussian distribution in statistics
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 223 MB
Genre: eLearning | Language: English

Learn through clear lectures and hands-on solved problems how to fully understand the Normal Distribution.

Tensor Methods in Statistics  eBooks & eLearning

Posted by arundhati at April 10, 2018
Tensor Methods in Statistics

P. McCullagh, "Tensor Methods in Statistics: Monographs on Statistics and Applied Probability"
2017 | ISBN-10: 1315898012 | 301 pages | PDF | 17 MB

Breakthroughs in Statistics: Volume II: Methodology and Distribution [Repost]  eBooks & eLearning

Posted by ChrisRedfield at Feb. 28, 2018
Breakthroughs in Statistics: Volume II: Methodology and Distribution [Repost]

Samuel Kotz, Norman L. Johnson - Breakthroughs in Statistics: Volume II: Methodology and Distribution
Published: 1991-12-20 | ISBN: 0387975721, 3540975721, 0387940391, 3540940391 | PDF + DJVU | 628 pages | 35.85 MB

Multivariate Analysis with LISREL (Springer Series in Statistics) [Repost]  eBooks & eLearning

Posted by hill0 at March 27, 2018
Multivariate Analysis with LISREL (Springer Series in Statistics) [Repost]

Multivariate Analysis with LISREL (Springer Series in Statistics) by Karl G. Jöreskog
English | 27 Oct. 2016 | ISBN: 3319331523 | 557 Pages | PDF | 19.04 MB
New Advances in Statistics and Data Science (ICSA Book Series in Statistics) [Repost]

New Advances in Statistics and Data Science (ICSA Book Series in Statistics) by Ding-Geng Chen
English | 6 Feb. 2018 | ISBN: 3319694154 | 374 Pages | PDF | 4.93 MB

Advanced Statistical Methods in Data Science (ICSA Book Series in Statistics)  eBooks & eLearning

Posted by hill0 at Feb. 13, 2018
Advanced Statistical Methods in Data Science (ICSA Book Series in Statistics)

Advanced Statistical Methods in Data Science (ICSA Book Series in Statistics) by Ding-Geng Chen
English | 15 Dec. 2016 | ISBN: 9811025932 | 222 Pages | EPUB | 2.73 MB

Multivariate Analysis with LISREL (Springer Series in Statistics) [Repost]  eBooks & eLearning

Posted by hill0 at Jan. 23, 2018
Multivariate Analysis with LISREL (Springer Series in Statistics) [Repost]

Multivariate Analysis with LISREL (Springer Series in Statistics) by Karl G. Jöreskog
English | 27 Oct. 2016 | ISBN: 3319331523 | 557 Pages | PDF | 19.04 MB

This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL,

Bayesian Essentials with R (Springer Texts in Statistics)  eBooks & eLearning

Posted by hill0 at Jan. 19, 2018
Bayesian Essentials with R (Springer Texts in Statistics)

Bayesian Essentials with R (Springer Texts in Statistics) by Jean-Michel Marin
English | 29 Oct. 2013 | ISBN: 1461486866 | 312 Pages | EPUB | 5.34 MB

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package

A Second Course in Statistics: Pearson New International Edition: Regression Analysis  eBooks & eLearning

Posted by Underaglassmoon at Jan. 8, 2018
A Second Course in Statistics: Pearson New International Edition: Regression Analysis

A Second Course in Statistics: Pearson New International Edition: Regression Analysis
Pearson | English | 2014 | ISBN-10: 1292042907 | 749 pages | PDF | 15.31 mb

By William Mendenhall, Terry Sincich

Bayesian Learning for Neural Networks (Lecture Notes in Statistics)  eBooks & eLearning

Posted by lengen at Dec. 24, 2017
Bayesian Learning for Neural Networks (Lecture Notes in Statistics)

Bayesian Learning for Neural Networks (Lecture Notes in Statistics) by Radford M. Neal
English | Aug. 9, 1996 | ISBN: 0387947248 | 195 Pages | PDF | 2 MB

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods.