This book bridges the latest software applications with the benefits of modern resampling techniques

Resampling helps students understand the meaning of sampling distributions, sampling variability, P-vlaues, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques.

The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as:

• Exploratory data analysis
• Calculation of sampling distributions
• The Central Limit Theorem
• Monte Carlo sampling
• Maximum likelihood estimation and properties of estimators
• Confidence intervals and hypothesis
• Regression
• Bayesian methods

Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints.

Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. it also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.

• Data and Case Studies
• Exploratory Data Analysis
• Hypothesis Testing
• Sampling Distributions
• The Bootstrap
• Estimation
• Classical Inference: Confidence Intervals
• Classical Inference: Hypothesis Testing
• Regression
• Bayesian Methods
• Additional Topics

• Appendix A Review of Probability
• Appendix B Probability Distributions
• Appendix C Distributions Quick Reference
• Solutions to Odd-Numbered Exercises