ISBN-10:
1430245549
ISBN-13:
9781430245544
Pub. Date:
10/17/2012
Publisher:
Apress
Beginning R: An Introduction to Statistical Programming / Edition 1

Beginning R: An Introduction to Statistical Programming / Edition 1

by Larry Pace

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Product Details

ISBN-13: 9781430245544
Publisher: Apress
Publication date: 10/17/2012
Edition description: 1st ed.
Pages: 336
Sales rank: 844,767
Product dimensions: 7.50(w) x 9.30(h) x 0.90(d)

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.

Table of Contents

Part I. Learning the R Language

1. Getting Started

2. Dealing with Dates, Strings, and Data Frames

3. Input and Output

4. Control Structures

Part II. Using R for Descriptive Statistics

5. Functional Programming

6. Probability Distributions

7. Working with Tables

Part III. Using R for Inferential Statistics

8. Descriptive Statistics and Exploratory Data Analysis

9. Working with Graphics

10. Traditional Statistical Methods

11. Modern Statistical Methods

12. Analysis of Variance

13. Correlation and Regression

14. Multiple Regression

15. Logistic Regression

16. Modern Statistical Methods II

Part IV. Taking R to the Next Level

17. Data Visualization Cookbook

18. High-performance Computing

19. Text Mining

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