*Practical and Visually Appealing with Clear Examples and Fully Detailed Proofs*

Probability and Statistics with R, Second Editionshows how to solve various statistical problems using both parametric and nonparametric techniques via the open source software R. It provides numerous real-world examples, carefully explained proofs, end-of-chapter problems, and illuminating graphs to facilitate the hands-on comprehension.

*Delves into Many Probability and Statistical Topics*

`Integrating theory with practice, the text briefly introduces the syntax, structures, and functions of the R language, before covering important graphical and numerical methods of exploring data. The package ``ggplot2`

is used throughout the book to create graphs. The next several chapters elucidate probability and random variables topics, including univariate and multivariate distributions. After exploring sampling distributions, the authors discuss point estimation, confidence intervals, hypothesis testing, and a wide range of nonparametric methods, spotlighting the bootstrap and permutation tests. The book also presents fixed- and random-effects models as well as randomized block and two-factor factorial designs in a chapter on experimental design. The final chapter describes simple and multiple regression analyses with an emphasis on model validation.

*Cohesively Incorporates Statistical Theory with R Implementation*

This comprehensive book presents extensive treatments of data analysis using parametric and nonparametric techniques. It effectively links statistical concepts with R procedures, empowering the reader to solve a vast array of statistical problems with R.

**Features**

Provides real-world examples of how R can be used to solve problems in probability and statistics, along with an overview on how to use R.

Explains the mathematics behind computational implementations.

Covers both traditional methods and nonparametric techniques, including goodness-of-fit tests, categorical data analysis, nonparametric bootstrapping, and permutation tests.

Uses regression analysis procedures to solve three interesting case studies based on real data.

Presents thoroughly worked-out derivations, detailed graphs, and abundant problems.

Provides the PASWR2 package, which contains data sets and functions from the text.

- 1. What is R?
- 2. Exploring Data
- 3. General Probability and Random Variables
- 4. Univariate Probability Distributions
- 5. Multivariate Probability Distributions
- 6. Sampling and Sampling Distributions

- 7. Point Estimation
- 8. Confidence Intervals
- 9. Hypothesis Testing
- 10. Nonparametric Methods
- 11. Experimental Design
- 12. Regression