Mathematical Statistics with Resampling and R, 2nd Edition

This thoroughly updated second edition combines the latest software applications with the benefits of modern resampling techniques

Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. The second edition of Mathematical Statistics with Resampling and R combines modern resampling techniques and mathematical statistics. This book has been classroom-tested to ensure an accessible presentation, uses the powerful and flexible computer language R for data analysis and explores the benefits of modern resampling techniques.

This book offers an introduction to permutation tests and bootstrap methods that can serve to motivate classical inference methods. The book strikes a balance between theory, computing, and applications, and the new edition explores additional topics including consulting, paired t test, ANOVA and Google Interview Questions. Throughout the book, new and updated case studies are included representing a diverse range of subjects such as flight delays, birth weights of babies, and telephone company repair times. These illustrate the relevance of the real-world applications of the material. This new edition:

• Puts the focus on statistical consulting that emphasizes giving a client an understanding of data and goes beyond typical expectations
• Presents new material on topics such as the paired t test, Fisher’s Exact Test and the EM algorithm
• Offers a new section on “Google Interview Questions” that illustrates statistical thinking
• Provides a new chapter on ANOVA
• Contains more exercises and updated case studies, data sets, and R code

Written for undergraduate students in a mathematical statistics course as well as practitioners and researchers, the second edition of Mathematical Statistics with Resampling and R presents a revised and updated guide for applying the most current resampling techniques to mathematical statistics.

Table of Contents

Chapter 1 Data And Case Studies
Chapter 2 Exploratory Data Analysis
Chapter 3 Introduction To Hypothesis Testing: Permutation Tests
Chapter 4 Sampling Distributions
Chapter 5 Introduction To Confidence Intervals: The Bootstrap
Chapter 6 Estimation
Chapter 7 More Confidence Intervals
Chapter 8 More Hypothesis Testing
Chapter 9 Regression
Chapter 10 Categorical Data
Chapter 11 Bayesian Methods
Chapter 12 One-Way Anova
Chapter 13 Additional Topics
Appendix A Review of Probability
Appendix B Probability Distributions
Appendix C Distributions Quick Reference