Introductory Statistics and Analytics: A Resampling Perspective

PREFACE

This book was developed by Statistics.com to meet the needs of its introductory students, based on experience in teaching introductory statistics online since 2003. The field of statistics education has been in ferment for several decades. With this book, which continues to evolve, we attempt to capture three important strands of recent thinking:

1. Connection with the field of data science—an amalgam of traditional statistics, newer machine learning techniques, database methodology, and computer programming to serve the needs of large organizations seeking to extract value from “big data.”
2. Guidelines for the introductory statistics course, developed in 2005 by a group of noted statistics educators with funding from the American Statistical Association. These Guidelines for Assessment and Instruction in Statistics Education (GAISE) call for the use of real data with active learning, stress statistical literacy and understanding over memorization of formulas, and require the use of software to develop concepts and analyze data.
3. The use of resampling/simulation methods to develop the underpinnings of statistical inference (the most difficult topic in an introductory course) in a transparent and understandable manner.

We start off with some examples of statistics in action (including two of statistics gone wrong) and then dive right in to look at the proper design of studies and account for the possible role of chance. All the standard topics of introductory statistics are ...