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The only book on the market that provides a simple nonmathematical presentation of the statistics needed by Six Sigma Green Belts. Every concept is explained in plain English with a minimum of mathematical symbols. Includes real-world examples, step by step instructions and sample output for Minitab and JMP software as well as downloadble, ready to use data sets and templates. Includes applications to service industries to help managers understand the role of Six Sigma in nonmanufacturing industries. Acknowledgments xvii About the Author xix Preface xxi Chapter 1: Fundamentals of Six Sigma 1 Chapter 2: Introduction to Statistics 7 Chapter 3: Presenting Data in Charts and Tables 23 Chapter 4: Descriptive Statistics 39 Chapter 5: Probability and Probability Distributions 59 Chapter 6: Sampling Distributions and Confidence Intervals 95 Chapter 7: Hypothesis Testing 113 Chapter 8: Design of Experiments 157 Chapter 9: Simple Linear Regression 211 Chapter 10: Multiple Regression 241 Chapter 11: Control Charts for Six Sigma Management 279 Appendix A: Review of Arithmetic and Algebra 321 Appendix B: Summation Notation 329 Appendix C: Statistical Tables 333 Appendix D: Documentation of Data Files 347 Glossary 349 Index 359
Applied Statistics for the Six Sigma Green Belt is a desk reference for Six Sigma green belts or beginners who are not familiar with statistics. As Six Sigma team members, green belts will help select, collect data for, and assist with the interpretation of a variety of statistical or quantitative tools within the context of the Six Sigma methodology. This book will serve as an excellent instructional tool developing a strong understanding of basic statistics including how to describe data both graphically and numerically. Ites specific focus is on concepts, applications, and interpretations of the statistical tools used during, and as part of, the Design, Measure, Analyze, Improve, and Control (DMAIC) methodology. Preview a sample chapter from this book along with the full table of contents by clicking here. You will need Adobe Acrobat to view this pdf file.
To make Six Sigma work, executive and managerial "greenbelts" and "champions" need to understand core statistical concepts and techniques--but they don't need to become professional statisticians. Now, there's a concise, non-mathematical guide to all the statistics they need--and none of the statistics they don't need. The author shows them exactly how to capture the right information, make sense of it, and use it to improve quality throughout the entire Six Sigma DMAIC process. Levine illuminates topics ranging from statistical process control and experimental design to regression analysis and hypothesis testing. Drawing on the experience that has made him one of the world's most honored statistics educators, Levine presents statistical topics with the least possible mathematics. Throughout, he teaches through realistic examples--including many examples from the service industries, among the fastest-growing areas of Six Sigma implementation.
This book is a desk reference and instructional aid for those individuals currently involved with, or preparing for involvement with, Six Sigma project teams. As Six Sigma team members, Green Belts help select, collect data for, and assist with the interpretation of a variety of statistical or quantitative tools within the context of the Six Sigma methodology. The second in a four-book series geared specifically for these Green Belt activities, this book provides a thorough discussion of statistical quality control (SQC) tools. These tools are introduced and discussed from the perspective of application rather than theoretical development. From this perspective, readers are taught to consider the SQC tools as statistical “alarm bells” that send signals when there are one or more problems with a particular process. Guidance is also given on the use of Minitab and JMP in doing these various SQC applications. In addition, examples and sample problems from all industries appear throughout the book to aid a Green Belt's comprehension of the material.
How do you like to learn? Is it by reading textbooks? Or do you want to learn by doing and seeing the results for yourself? If so, this book is for you as it is written as a teaching guide. The book aims to teach using example-based learning so you can learn data analysis and problem-solving at the Green Belt level. The author recognised that Six Sigma Green and Black belts needed more support to understand the complex statistical techniques used within Six Sigma, but this had to be delivered effectively. In this book, the author uses his experience of industrial process improvement and Minitab training to provide Six Sigma Green Belts with the learning support they need to drive Minitab 19. Key Features of this book are: -Covers all main topics used by Six Sigma Green Belts in easy to understand language. -Improved and updated for Minitab 19.-The main Six Sigma tools are explained. It uses example-based learning with hundreds of screenshots in the book.-Focusses on using the Assistant and includes features such as Sequential DOE and Multiple Regression.-The data sets for the examples and exercises are available to download from www.rmksixsigma.com; along with model answers. -Support Videos are also available from the RMK Six Sigma Youtube channel.-Examples cover both continuous and attribute data where possible.
This reference manual is designed to help those interested in passing the ASQ's certification exam for Six Sigma Green Belts and others who want a handy reference to the appropriate materials needed to conduct successful Green Belt projects. It is a reference handbook on running projects for those who are already knowledgeable about process improvement and variation reduction. The primary layout of the handbook follows the ASQ Body of Knowledge (BoK) for the Certified Six Sigma Green Belt (CSSGB) updated in 2015. The authors were involved with the first edition handbook, and have utilized first edition user comments, numerous Six Sigma practitioners, and their own personal knowledge gained through helping others prepare for exams to bring together a handbook that they hope will be very beneficial to anyone seeking to pass the ASQ or other Green Belt exams. In addition to the primary text, the authors have added a number of new appendixes, an expanded acronym list, new practice exam questions, and other additional materials
Six Sigma has arisen in the last two decades as a breakthrough Quality Management Methodology. With Six Sigma, we are solving problems and improving processes using as a basis one of the most powerful tools of human development: the scientific method. For the analysis of data, Six Sigma requires the use of statistical software, being R an Open Source option that fulfills this requirement. R is a software system that includes a programming language widely used in academic and research departments. Nowadays, it is becoming a real alternative within corporate environments. The aim of this book is to show how R can be used as the software tool in the development of Six Sigma projects. The book includes a gentle introduction to Six Sigma and a variety of examples showing how to use R within real situations. It has been conceived as a self contained piece. Therefore, it is addressed not only to Six Sigma practitioners, but also to professionals trying to initiate themselves in this management methodology. The book may be used as a text book as well.