Using r With Multivariate Statistics

John Verzani - Using R for Introductory Statistics [Repost]  eBooks & eLearning

Posted by rotten comics at Nov. 25, 2016
John Verzani - Using R for Introductory Statistics [Repost]

John Verzani - Using R for Introductory Statistics
2004 | ISBN: 1584884509 | English | 432 pages | PDF | 3 MB

Using R for Introductory Statistics (2nd Edition) (Repost)  eBooks & eLearning

Posted by happy4all at March 12, 2016
Using R for Introductory Statistics (2nd Edition) (Repost)

Using R for Introductory Statistics (2nd Edition) By John Verzani
2014 | 518 Pages | ISBN: 1466590734 | PDF | 9 MB

Using R for Introductory Statistics, Second Edition (Repost)  eBooks & eLearning

Posted by roxul at March 5, 2016
Using R for Introductory Statistics, Second Edition (Repost)

John Verzani, "Using R for Introductory Statistics, Second Edition"
English | ISBN: 1466590734 | 2014 | 518 pages | PDF | 9 MB
Using R for Introductory Statistics, Second Edition (repost)

John Verzani, "Using R for Introductory Statistics, Second Edition"
English | ISBN: 1466590734 | 2014 | 518 pages | PDF | 9 MB
Using R for Introductory Statistics, Second Edition

John Verzani, "Using R for Introductory Statistics, Second Edition"
English | ISBN: 1466590734 | 2014 | 518 pages | PDF | 9 MB

Using R for Introductory Statistics  

Posted by ChrisRedfield at Aug. 29, 2014
Using R for Introductory Statistics

John Verzani - Using R for Introductory Statistics
Published: 2004-11-29 | ISBN: 1584884509 | PDF | 432 pages | 3 MB
Hidden Markov Models for Time Series: An Introduction Using R (Monographs on Statistics and Applied Probability)

Hidden Markov Models for Time Series
Walter Zucchini, Iain L. MacDonald | Chapman & Hall/CRC | 2009-04-28 | ISBN:1584885734 | Pages: 269 | PDF | 5.3MB

This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It presents an accessible overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts, and categorical observations. It explores a variety of applications in animal behavior, finance, epidemiology, climatology, and sociology. The authors discuss how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and checking, decoding, and forecasting. They provide all of the data sets analyzed in the text online.

Statistics and Data Analysis for Microarrays Using R and Bioconductor (2nd Edition) (Repost)

Statistics and Data Analysis for Microarrays Using R and Bioconductor (2nd Edition) By Sorin Draghici
2012 | 1036 Pages | ISBN: 1439809755 | PDF | 131 MB

Udemy - R - Business Analytics Using R Programming [repost]  eBooks & eLearning

Posted by house23 at Nov. 5, 2016
Udemy - R - Business Analytics Using R Programming [repost]

Udemy - R - Business Analytics Using R Programming
MP4 | AVC 470kbps | English | 1280x720 | 30fps | 17 hours | AAC stereo 59kbps | 2.68 GB
Genre: Video Training

This course ensure quick learning in a simplified way. It explains the most important aspects of working on data and conduct analysis through example. You will start by learning how to install and navigate R studio. Learn Data/Object Types and Operations, Importing into R, and Loops and Conditions. you will be introduced to the use of R in Analytics, where you will learn a little about each object type in R and use that in Data Mining/Analytical Operations. learn the use of R in Statistics, using R to evaluate Descriptive Statistics, Probability Distributions, Hypothesis Testing, Linear Modeling, Generalized Linear Models, Non-Linear Regression, and Trees. Learn to create 2-dimensional Univariate and Multi-variate plots.

A Handbook of Statistical Analyses Using R (Repost)  eBooks & eLearning

Posted by leonardo78 at Oct. 30, 2016
A Handbook of Statistical Analyses Using R (Repost)

Brian S. Everitt, Torsten Hothorn, "A Handbook of Statistical Analyses Using R"
English | 2006-02-17 | ISBN: 1584885394 | 269 pages | PDF | 1,4 mb

R is dynamic, to say the least. More precisely, it is organic, with new functionality and add-on packages appearing constantly. And because of its open-source nature and free availability, R is quickly becoming the software of choice for statistical analysis in a variety of fields.