Statistical Analysis With R

Statistical Analysis with R For Dummies  eBooks & eLearning

Posted by readerXXI at Sept. 5, 2017
Statistical Analysis with R For Dummies

Statistical Analysis with R For Dummies
by Joseph Schmuller
English | 2017 | ISBN: 1119337062 | 456 Pages | PDF | 11 MB

Statistical Analysis with R For Dummies  eBooks & eLearning

Posted by Bayron at March 25, 2017
Statistical Analysis with R For Dummies

Statistical Analysis with R For Dummies by Joseph Schmuller
English | 2017 | ISBN: 1119337062 | 465 pages | EPUB | 4 MB

Statistical Analysis with R For Dummies (For Dummies (Computer/Tech)) [Kindle Edition]  eBooks & eLearning

Posted by AlenMiler at March 10, 2017
Statistical Analysis with R For Dummies (For Dummies (Computer/Tech)) [Kindle Edition]

Statistical Analysis with R For Dummies (For Dummies (Computer/Tech)) by Joseph Schmuller
English | 3 Mar. 2017 | ASIN: B06XGNPTL6 | 456 Pages | AZW3 | 4.8 MB

Statistical Analysis with R (Repost)  eBooks & eLearning

Posted by igor_lv at April 4, 2014
Statistical Analysis with R (Repost)

Statistical Analysis with R - John M. Quick
2011 | ISBN: 1849512086 | PDF | 300 pages | 7 Mb

This is a practical, step by step guide that will help you to quickly become proficient in the data analysis using R. The book is packed with clear examples, screenshots, and code to carry on your data analysis without any hurdle. If you are a data analyst, business or information technology professional, student, educator, researcher, or anyone else who wants to learn to analyze the data effectively then this book is for you. No prior experience with R is necessary. Knowledge of other programming languages, software packages, or statistics may be helpful, but is not required.

Statistical Analysis with R  eBooks & eLearning

Posted by lout at Dec. 13, 2010
Statistical Analysis with R

Statistical Analysis with R By John M. Quick
Publisher: Pac kt Publi shing 2010 | 300 Pages | ISBN: 1849512086 | PDF | 11 MB

Deep Dive into Statistical Modeling with R  eBooks & eLearning

Posted by naag at July 7, 2017
Deep Dive into Statistical Modeling with R

Deep Dive into Statistical Modeling with R
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 421 MB
Genre: eLearning | Language: English

R is a data analysis tool, graphical environment, and programming language. Without any prior experience in programming or statistical software, this video tutorial will help you quickly become a knowledgeable user of R. Now is the time to take control of your data and start producing superior statistical analysis with R.

Graphical Data Analysis with R  eBooks & eLearning

Posted by AlenMiler at Jan. 3, 2018
Graphical Data Analysis with R

Graphical Data Analysis with R (Chapman & Hall/CRC: The R Series) by Antony Unwin
English | 12 May 2015 | ISBN: 1498715230 | 310 Pages | PDF | 15.11 MB
Applied Statistical Genetics with R: For Population-based Association Studies (Repost)

Andrea S. Foulkes, "Applied Statistical Genetics with R: For Population-based Association Studies"
2009 | pages: 264 | ISBN: 0387895531 | PDF | 1,5 mb

Computational Methods for Numerical Analysis with R  eBooks & eLearning

Posted by ksveta6 at Aug. 15, 2017
Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R by James P Howard II
2017 | ISBN: 1498723632 | English | 277 pages | PDF | 5 MB
Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application

Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application By Grace Y. Yi
English | PDF | 2017 | 497 Pages | ISBN : 1493966383 | 4.67 MB

This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies.