Beginning R: An Introduction to Statistical Programming

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Apress, Oct 17, 2012 - Computers - 336 pages

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

What you’ll learn Acquire and install R Import and export data and scripts Generate basic statistics and graphics Program in R to write custom functions Use R for interactive statistical explorations Implement simulations and other advanced techniques Who this book is for

Beginning R: An Introduction to Statistical Programming is an easy-to-read book that serves as an instruction manual and reference for working professionals, professors, and students who want to learn and use R for basic statistics. It is the perfect book for anyone needing a free, capable, and powerful tool for exploring statistics and automating their use.

Table of ContentsPart 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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Chapter 1 Getting R and Getting Started
Chapter 2 Programming in R
Chapter 3 Writing Reusable Functions
Chapter 4 Summary Statistics
Chapter 5 Creating Tables and Graphs
Chapter 6 Discrete Probability Distributions
Chapter 7 Computing Normal Probabilities
Chapter 8 Creating Confidence Intervals
Chapter 14 Logistic Regression
Chapter 15 ChiSquare Tests
Chapter 16 Nonparametric Tests
Chapter 17 Using R for Simulation
Chapter 18 The New Statistics Resampling and Bootstrapping
Chapter 19 Making an R Package
Chapter 20 The R Commander Package

Chapter 9 Performing t Tests
Chapter 10 OneWay Analysis of Variance
Chapter 11 Advanced Analysis of Variance
Chapter 12 Correlation and Regression
Chapter 13 Multiple Regression
About the Author
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About the author (2012)

Dr. Larry Pace is a statistics author and educator, as well as a consultant. He lives in the upstate area of South Carolina in the town of Anderson. He is a professor of statistics, mathematics, psychology, management, and leadership. He has programmed in a variety of languages and scripting languages including R, Visual Basic, JavaScript, C##, PHP, APL, and in a long-ago world, Fortran IV. He writes books and tutorials on statistics, computers, and technology. He has also published many academic papers, and made dozens of presentations and lectures. He has consulted with Compaq Computers, AT&T, Xerox Corporation, the U.S. Navy, and International Paper. He has taught at Keiser University, Argosy University, Capella University, Ashford University, Anderson University (where he was the chair of the behavioral sciences department), Clemson University, Louisiana Tech University, LSU in Shreveport, the University of Tennessee, Cornell University, Rochester Institute of Technology, Rensselaer Polytechnic Institute, and the University of Georgia.

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