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 include:
* New and updated case studies that incorporate contemporary subjects like COVID-19
* Several new sections, including introductory material on causal models and regression methods for causal modeling in practice
* Modern terminology distinguishing statistical discernibility and practical importance
* New exercises and examples, data sets, and R code, using dplyr and ggplot2
* A complete instructor’s solutions manual
* A new github site that contains code, data sets, additional topics, and instructor resources
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.
Inhoudsopgave
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
Over de auteur
Laura M. Chihara, Ph D, 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 Math Soft) and in statistical consulting.
Tim C. Hesterberg, Ph D, 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.
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