(PDF)Mathematical Statistics with Resampling and R 3rd Edition
by Laura M. Chihara, Tim C. Hesterberg
Key Highlights
- •Focuses on modern resampling techniques (bootstrap, permutation tests).
- •Integrates mathematical statistics theory with practical R implementation.
- •Updated 3rd Edition with current examples and refined explanations.
Description
Ready to tackle modern statistics with confidence? Dive into Mathematical Statistics with Resampling and R, 3rd Edition! This book, by Laura M. Chihara and Tim C. Hesterberg, is your guide to understanding core statistical concepts through the powerful lens of resampling methods, all implemented using the popular R language. It’s a fresh approach to learning stats thats both rigorous and accesible.
Who is this book for? This book is perfect for undergraduate or graduate students in statistics, data science, or related fields. It's also a fantastic resource for researchers, data analysts, and practitioners who want to update their statistical toolkit with modern, computationally intensive techniques. If you're learning stats and R, this is for you!
What problem does this book solve? Traditional statistics courses can sometimes feel abstract and disconnected from real-world data analysis. Maybe you find concepts like bootstrapping or permutation tests confusing? This book solve that by emphasizing intuition and practical application. It directly addresses the need for a statistics textbook that integrates modern resampling techniques and computational tools (specifically R) right from the start, making these powerful methods easier to grasp and use.
What will you gain from reading it? You'll gain a solid foundation in mathematical statistics, but with a unique focus on resampling – think bootstrap confidence intervals and permutation tests. You’ll learn not just the theory, but how to actually do these analyses using R code provided throughout the book. This practical skill set is incredibly valuable in today's data-driven world.
Why is it worth reading? This isn't just another dry stats textbook. The 3rd edition is updated with current examples and refined explanations, focusing on building statistical intuition alongside computational skill. It moves beyond rote memorization of formulas to genuine understanding. Its a modern, practical, and effective way to learn mathematical statistics. Grab your PDF copy today and start mastering stats with resampling and R!
FAQ
Do I need to be an expert in R before using this book?
No, not necessarily! The book includes R code and explanations, making it suitable even if you're relatively new to R. However, some basic familiarity with programming concepts would be helpful.
Is this book suitable for a first course in mathematical statistics?
Yes, it's designed to be accessible for an undergraduate course, often suitable for students who have completed introductory statistics and calculus. It offers a modern alternative to traditional theory-heavy texts.
What exactly are 'resampling methods' covered in the book?
Resampling methods are computational techniques like the bootstrap (for estimating standard errors and confidence intervals) and permutation tests (for hypothesis testing). They rely on repeatedly drawing samples from your data.
Does the book cover traditional topics like Maximum Likelihood Estimation (MLE)?
Yes, while it emphasizes resampling, it still covers core mathematical statistics concepts, including probability, distributions, estimation (like MLE), and hypothesis testing, often presenting them alongside or through the lens of resampling.
What's different about the 3rd Edition?
The 3rd Edition typically includes updated R code, new examples using contemporary datasets, refined explanations based on feedback, and potentially new sections or reorganized content to improve clarity and flow.
Is the PDF searchable and easy to navigate?
Yes, the PDF format generally allows for text searching, making it easy to find specific topics, definitions, or R functions discussed in the book.
Reader Reviews
About the Author
Laura M. Chihara is a Professor of Mathematics & Statistics known for her work in statistics education and applications. Tim C. Hesterberg is a Senior Data Scientist with extensive industry experience, renowned for developing and advocating resampling methods. Together, they bring a unique blend of academic rigor and practical insight to statistical learning.