Read 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
List 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
About the author
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.
Summary
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
