Mathematical statistics with resampling and R /

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.Throughout the book, new and updated case studies representing a diverse range of subjects such as flight delays, birth weights of babies, and U.S demographics and views on sociological issues illustrate the relevance of mathematical statistics to real-world applications.Changes and additions to the second edition include: New material on topics such as paired data, Fisher's Exact Test and the EM algorithm. A new chapter on ANOVA. A "Google Interview Question" case study and discussion that illustrate statistical thinking—starting with understanding the problem and framing it properly before proceeding to solutions. New exercises and examples, 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.Includes bibliographical references and index.Data and Case Studies -- Exploratory Data Analysis -- Introduction to Hypothesis Testing: Permutation Tests -- Sampling Distributions -- Introduction to Confidence Intervals: The Bootstrap -- Estimation -- More Confidence Intervals -- More Hypothesis Testing -- Regression -- Categorical Data -- Bayesian Methods -- One-way ANOVA -- Additional Topics -- Review of Probability -- Probability Distributions -- Distributions Quick Reference.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.Throughout the book, new and updated case studies representing a diverse range of subjects such as flight delays, birth weights of babies, and U.S demographics and views on sociological issues illustrate the relevance of mathematical statistics to real-world applications.Changes and additions to the second edition include: New material on topics such as paired data, Fisher's Exact Test and the EM algorithm. A new chapter on ANOVA. A "Google Interview Question" case study and discussion that illustrate statistical thinking—starting with understanding the problem and framing it properly before proceeding to solutions. New exercises and examples, 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