Resampling Methods: A Practical Guide to Data Analysis

by Phillip Good

This is a great small book on using the bootstrap (more specifically the permutation bootstrap). Dr. Good presents several interesting datasets and applications where using the bootstrap can help clarify conclusions that can be drawn from the data. The permutation test (and this book in general) is especially helpful when the data scientist is in the case where they have a very few samples. In that case it is harder to use classical statistical tests and the permutation test can add insight to your analysis. Reading this book (and the associated solution manual with its R codes) is a great way to quickly learn these techniques and to be applying them to your own problems as fast as possible. Readers unfamiliar with this book can see what others have said here.

To learn this material as well as possible I worked through all of the book's end of chapter problems. The R code for the problems can be found below. In addition to the R code I wrote up solutions and comments on each problem (and some examples) in PDF form (using the mathematical typesetting language LaTeX). If you are interested in purchasing the PDF version of the solution manual for $15.00 (US dollars) please follow the PayPal link on this page.

• Chapter 1 (Descriptive Statistics)
  
• Chapter 2 (Cause and Effect)
  
• Chapter 3 (Testing Hypothesis)
  
• Chapter 5 (Estimation)
  
• Chapter 6 (Power of a Test)
  
• Chapter 8 (Experimental Design and Analysis)
  
• Chapter 9 (Multiple Variables and Multiple Hypothesis)
  
• Chapter 10 (Classification and Discrimination)
  
• Chapter 11 (Survival Analysis and Reliability)