Mathematical Statistics with Resampling and R
by Chihara, Laura M.; Hesterberg, Tim C.Edition: 3rd
Format: Hardcover
Pub. Date: 2022-09-21
Publisher(s): Wiley
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Summary
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. Classroom-tested to ensure a comprehensive presentation, this book uses the powerful and flexible computer language R for data analysis and explore the benefits of modern resampling techniques. It strikes a balance between theory, computing, and applications. Throughout the book, new and updated case studies featuring areas such as COVID-19, climate action, and more illustrate the relevnce of mathematical statistics to real-world applications. This third edition includes several all-new sections, including a discussion of pivotal statistics and causal reference. Written for undergraduate students in mathematical statistics courses as well as practitioners and researchers, this third edition presented a revised and updated guide for applying the most current resampling techniues to mathematical statistics.
Author Biography
Laura M. Chihara, PhD, is Professor of Mathematics at Carleton College with extensive experience teaching mathematical statistics and applied regression analysis. Dr. Chihara has experience with S+ and R from her work at Insightful Corporation (formerly MathSoft) and in statistical consulting.
Tim C. Hesterberg, PhD, is a Staff Data Scientist at Instacart. He was previously a data scientist at Google and research scientist at Insightful Corporation, led the development of S+Resample, and wrote the R resample package.
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
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