Mathematical Statistics with Resampling and R

by Laura M. Chihara, Tim C Hesterberg

Mathematical Statistics with Resampling and R This thoroughly updated third 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 third edition of Mathematical Statistics with Resampling and R combines modern resampling techniques and mathematical statistics. This book is classroom-tested to ensure an accessible presentation, and uses the powerful and flexible computer language R for data analysis. This book introduces permutation tests and bootstrap methods to motivate classical inference methods, as well as to be utilized as useful tools in their own right when classical methods are inaccurate or unavailable. The book strikes a balance between simulation, computing, theory, data, and applications. Throughout the book, new and updated case studies representing a diverse range of subjects, such as flight delays, birth weights of babies, U.S. demographics, views on sociological issues, and problems at Google and Instacart, illustrate the relevance of mathematical statistics to real-world applications. Changes and additions to the third edition Mathematical Statistics with Resampling and R is an ideal textbook for undergraduate and graduate students in mathematical statistics courses, as well as practitioners and researchers looking to expand their toolkit of resampling and classical techniques.

Genres: Mathematics

576 pages, Hardcover

First published September 6, 2011

Reviews

[Buciu Petre · Rating 5 out of 5]

Probably the best introductory statistics book you will read. It uses modern computationally-intensive resampling techniques to illustrate basic concepts of statistical inference and estimation and their application in real-world data analysis. It is very clear, with many graphs and well-written R code but the math could have been a little more rigorous in some parts.

[Rahul · Rating 4 out of 5]

Great explanations and proofs of statistical concepts. Example problems and plots from R are effective. However, the book could benefit from a larger number of problems at the end of each chapter.

The authors provide (readable) theory with some important tips regarding practical applications as well (from one of the authors' extensive applied stats work, I presume). Overall, a great intro to mathematical statistics.

P.S.: Be sure to keep track of the Errata page online (ex. One of the distribution pdfs is incorrect in the book's appendix, but the errata lists the correct pdf).

[Cael · Rating 4 out of 5]

A good introductory statistics book, with plenty of emphasis on modern techniques such as permutation tests, bootstrap distributions, etc. I didn't know any R before reading this and now I feel pretty comfortable in R. A lot of typos, mostly unimportant, but one typo in a technical book can really throw you for a while. Fortunately the authors are committed to eliminating typos in an upcoming edition, so if you can wait I'd suggest getting the next edition.