Beginning R: The Statistical Programming Language

Mark Gardener

ISBN 10: 111816430X / ISBN 13: 9781118164303

Edité par John Wiley & Sons Inc, 2012

A propos de cet article

2012. 1st Edition. Paperback. Conquer the complexities of this open source statistical language R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. This book examines this complex language using simple statistical examples, showing how R operates in a user-friendly context. Num Pages: 504 pages. BIC Classification: UFM. Category: (P) Professional & Vocational. Dimension: 234 x 188 x 24. Weight in Grams: 868. . . . . . Books ship from the US and Ireland. N° de réf. du vendeur V9781118164303

Détails bibliographiques

Titre : Beginning R: The Statistical Programming ...

Éditeur : John Wiley & Sons Inc

Date d'édition : 2012

Reliure : Couverture souple

Etat : New

A propos de ce titre

Synopsis :

Unusual book

Quatrième de couverture:

Gain better insight into your data using the power of R

While R is very flexible and powerful, it is unlike most of the computer programs you have used. In order to unlock its full potential, this book delves into the language, making it accessible so you can tackle even the most complex of data analysis tasks. Simple data examples are integrated throughout so you can explore the capabilities and versatility of R. Along the way, you′ll also learn how to carry out a range of commonly used statistical methods, including Analysis of Variance and Linear Regression. By the end, you′ll be able to effectively and efficiently analyze your data and present the results.

Beginning R:

• Discusses how to implement some basic statistical methods such as the t–test, correlation, and tests of association

• Explains how to turn your graphs from merely adequate to simply stunning

• Provides you with the ability to define complex analytical situations

• Demonstrates ways to make and rearrange your data for easier analysis

• Covers how to carry out basic regression as well as complex model building and curvilinear regression

• Shows how to produce customized functions and simple scripts that can automate your workflow

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