|  | Gardener, Mark
Beginning R
The Statistical Programming Language
 1. Edition June 2012
30.90 Euro
2012. 504 Pages, Softcover
- Practical Approach Book -
ISBN 978-1-118-16430-3 - John Wiley & Sons
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| Detailed description
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. Both students and workers in fields that require extensive statistical analysis will find this book helpful as they learn to use R for simple summary statistics, hypothesis testing, creating graphs, regression, and much more. It covers formula notation, complex statistics, manipulating data and extracting components, and rudimentary programming.
* R, the open source statistical language increasingly used to handle statistics and produces publication-quality graphs, is notoriously complex
* This book makes R easier to understand through the use of simple statistical examples, teaching the necessary elements in the context in which R is actually used
* Covers getting started with R and using it for simple summary statistics, hypothesis testing, and graphs
* Shows how to use R for formula notation, complex statistics, manipulating data, extracting components, and regression
* Provides beginning programming instruction for those who want to write their own scripts
Beginning R offers anyone who needs to perform statistical analysis the information necessary to use R with confidence.
From the contents
Introduction xxi
Chapter 1: Introducing R: What It Is and How to Get It 1
Chapter 2: Starting Out: Becoming Familiar with R 25
Chapter 3: Starting Out: Working With Objects 65
Chapter 4: Data: Descriptive Statistics and Tabulation 107
Chapter 5: Data: Distrib ution 151
Chapter 6: Si mple Hypothesis Testing 181
Chapter 7: Introduction to Graphical Analysis 215
Chapter 8: Formula Notation and Complex Statistic s 263
Chapter 9: Manipulating Data and Extracting Components 295
Chapter 10: Regression (Li near Modeling) 327
Chapter 11: More About Graphs 363
Chapter 12: Writing Your Own Scripts: Beginning to Program 415
Appendix: Answers to Exerci ses 433
Index 461
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