This book is about the use of modern statistical methods for quality control and improvement. It provides comprehensive coverage of the subject from basic principles to state-of-the art concepts and applications. The objective is to give the reader a sound understanding of the principles and the basis for applying them in a variety of situations. Although statistical techniques are emphasized throughout, the book has a strong engineering and management orientation. Extensive knowledge of statistics is not a prerequisite for using this book. Readers whose background includes a basic course in statistical methods will find much of the material in this book easily accessible. Audience The book is an outgrowth of more than 40 years of teaching, research, and consulting in the appli-cation of statistical methods for industrial problems. It is designed as a textbook for students enrolled in colleges and universities who are studying engineering, statistics, management, and related fields and are taking a first course in statistical quality control. The basic quality-control course is often taught at the junior or senior level. All of the standard topics for this course are covered in detail. Some more advanced material is also available in the book, and this could be used with advanced undergraduates who have had some previous exposure to the basics or in a course aimed at gradu- ate students. I have also used the text materials extensively in programs for professional practition-ers, including quality and reliability engineers, manufacturing and development engineers, product designers, managers, procurement specialists, marketing personnel, technicians and laboratory ana-lysts, inspectors, and operators. Many professionals have also used the material for self-study. Chapter Organization and Topical Coverage The book contains five parts. Part 1 is introductory. The first chapter is an introduction to the philosophy and basic concepts of quality improvement. It notes that quality has become a major business strategy and that organizations that successfully improve quality can increase their pro-ductivity, enhance their market penetration, and achieve greater profitability and a strong compet- itive advantage. Some of the managerial and implementation aspects of quality improvement are included. Chapter 2 describes DMAIC, an acronym for Define, Measure, Analyze, Improve, and Control. The DMAIC process is an excellent framework to use in conducting quality-improvement projects. DMAIC often is associated with Six Sigma, but regardless of the approach taken by an organization strategically, DMAIC is an excellent tactical tool for quality professionals to employ. Part 2 is a description of statistical methods useful in quality improvement. Topics include sampling and descriptive statistics, the basic notions of probability and probability distributions, point and interval estimation of parameters, and statistical hypothesis testing. These topics are usually covered in a basic course in statistical methods; however, their presentation in this text is from the quality-engineering viewpoint. My experience has been that even readers with a strong statistical background will find the approach to this material useful and somewhat different from a standard statistics textbook.