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Mathematics - Computational Science & Engineering | Beginning R - An Introduction to Statistical Programming

Beginning R

An Introduction to Statistical Programming

Pace, Larry

2012, XXIV, 336 p.

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    • R is becomming the de facto standard language of statistics
    • Commercial adoption is increasing as exemplified by Oracle's recent adoption of R for a Big Data Appliance based upon their fast-growing Exadata hardware
    • R is widely supported in mainstream statistical applications such as SPSS, Matlab, Statistica, SAS, and more
Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, and has become the de facto standard for doing, teaching, and learning computational statistics.

R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets. R is also becoming adopted into commercial tools such as Oracle Database. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for statistical exploration and research.

  • Covers the freely-available R language for statistics
  • Shows the use of R in specific uses case such as simulations, discrete probability solutions, one-way ANOVA analysis, and more
  • Takes a hands-on and example-based approach incorporating best practices with clear explanations of the statistics being done

Content Level » Popular/general

Related subjects » Computational Science & Engineering

Table of contents 

Part I. Learning the R Language

1. Getting R and Getting Started

2. Programming in R

3. Writing Reusable Functions

4. Summary Statistics

Part II. Using R for Descriptive Statistics

5. Creating Tables and Graphs

6. Discrete Probability Distributions

7. Computing Standard Normal Probabilities

Part III. Using R for Inferential Statistics

8. Creating Confidence Intervals

9. Performing t Tests 

10.  Implementing One-Way ANOVA

11.  Implementing Advanced ANOVA

12. Simple Correlation and Regression in R

13. Multiple Correlation and Regression in R

14. Logistic Regression

15. Performing Chi-Square Tests

16. Working in Nonparametric Statistics

Part IV. Taking R to the Next Level

17. Using R for Simulation

18. Resampling and Bootstrapping

19. Creating R Packages

20. Executing R Packages

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