Beginning R
An Introduction to Statistical Programming
Samenvatting
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
Specificaties
Inhoudsopgave
1. Getting R and Getting Started<p>
2. Programming in R<p>
3. Writing Reusable Functions<p>
4. Summary Statistics<p>
<p>
<strong>Part II. Using R for Descriptive Statistics</strong> <p>
5. Creating Tables and Graphs<p>
6. Discrete Probability Distributions<p>
7. Computing Standard Normal Probabilities <p>
<p>
<strong>Part III. Using R for Inferential Statistics <p>
</strong>8. Creating Confidence Intervals <p>
9. Performing t Tests <p>
10. Implementing One-Way ANOVA <p>
11. Implementing Advanced ANOVA<p>
12. Simple Correlation and Regression in R <p>
13. Multiple Correlation and Regression in R <p>
14. Logistic Regression<p>
15. Performing Chi-Square Tests <p>
16. Working in Nonparametric Statistics <p>
<p>
<strong>Part IV. Taking R to the Next Level</strong> <p>
17. Using R for Simulation<p>
18. Resampling and Bootstrapping <p>
19. Creating R Packages<p>
20. Executing R Packages