Introduction to Statistics Through Resampling Methods and R, Second Edition

A highly accessible alternative approach to basic statistics Praise for the First Edition:  "Certainly one of the most impressive little paperback 200-page introductory statistics books that I will ever see . . . it would make a good nightstand book for every statistician."-Technometrics 
Written in a highly accessible style, Introduction to Statistics through Resampling Methods and R, Second Edition guides students in the understanding of descriptive statistics, estimation, hypothesis testing, and model building. The book emphasizes the discovery method, enabling readers to ascertain solutions on their own rather than simply copy answers or apply a formula by rote.  The Second Edition utilizes the R programming language to simplify tedious computations, illustrate new concepts, and assist readers in completing exercises. The text facilitates quick learning through the use of: 
More than 250 exercises-with selected "hints"-scattered throughout to stimulate readers' thinking and to actively engage them in applying their newfound skills 
An increased focus on why a method is introduced 
Multiple explanations of basic concepts 
Real-life applications in a variety of disciplines 
Dozens of thought-provoking, problem-solving questions in the final chapter to assist readers in applying statistics to real-life applications 
Introduction to Statistics through Resampling Methods and R, Second Edition is an excellent resource for students and practitioners in the fields of agriculture, astrophysics, bacteriology, biology, botany, business, climatology, clinical trials, economics, education, epidemiology, genetics, geology, growth processes, hospital administration, law, manufacturing, marketing, medicine, mycology, physics, political science, psychology, social welfare, sports, and toxicology who want to master and learn to apply statistical methods.

Content:
Chapter 1 Variation (pages 1–24):
Chapter 2 Probability (pages 25–41):
Chapter 3 Two Naturally Occurring Probability Distributions (pages 43–57):
Chapter 4 Estimation and the Normal Distribution (pages 59–70):
Chapter 5 Testing Hypotheses (pages 71–90):
Chapter 6 Designing an Experiment or Survey (pages 91–117):
Chapter 7 Guide to Entering, Editing, Saving, and Retrieving Large Quantities of Data Using R (pages 119–124):
Chapter 8 Analyzing Complex Experiments (pages 125–148):
Chapter 9 Developing Models (pages 149–179):
Chapter 10 Reporting Your Findings (pages 181–195):
Chapter 11 Problem Solving (pages 197–204):