Introduction to Statistics Through Resampling Methods and R, 2nd 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.

Table of Contents

1. Variation 1
2. Probability 25
3. Two Naturally Occurring Probability Distributions 43
4. Estimation and the Normal Distribution 59
5. Testing Hypotheses 71
6. Designing an Experiment or Survey 91
7. Guide to Entering, Editing, Saving, and Retrieving Large Quantities of Data Using R 119
8. Analyzing Complex Experiments 125
9. Developing Models 149
10. Reporting Your Findings 181
11. Problem Solving 197