Here is a chapter from Six Sigma Statistics with Excel and MINITAB. This is a comprehensive and easy-to-use guide for understanding and using Excel and MINITAB programs for Six Sigma statistical data analysis. Each chapter includes relevant theory and technique, step-by-step exercises, case studies, graphical illustrations and screen shots for performing the techniques in both Excel and MINITAB.
Master the Statistical Techniques for Six Sigma Operations, While Boosting Your Excel and Minitab Skills! Now with the help of this “one-stop” resource, operations and production managers can learn all the powerful statistical techniques for Six Sigma operations, while becoming proficient at Excel and Minitab at the same time. Six Sigma Statistics with Excel and Minitab offers a complete guide to Six Sigma statistical methods, plus expert coverage of Excel and Minitab, two of today's most popular programs for statistical analysis and data visualization. Written by a seasoned Six Sigma Master Black Belt, the book explains how to create and interpret dot plots, histograms, and box plots using Minitab...decide on sampling strategies, sample size, and confidence intervals...apply hypothesis tests to compare variance, means, and proportions...conduct a regression and residual analysis...design and analyze an experiment...and much more. Filled with clear, concise accounts of the theory for each statistical method presented, Six Sigma Statistics with Excel and Minitab features: Easy-to-follow explanations of powerful Six Sigma tools A wealth of exercises and case studies 200 graphical illustrations for Excel and Minitab Essential for achieving Six Sigma goals in any organization, Six Sigma Statistics with Excel and Minitab is a unique, skills-building toolkit for mastering a wide range of vital statistical techniques, and for capitalizing on the potential of Excel and Minitab. Six Sigma Statistical with Excel and Minitab offers operations and production managers a complete guide to Six Sigma statistical techniques, together with expert coverage of Excel and Minitab, two of today's most popular programs for statistical analysis and data visualization. Written by Issa Bass, a Six Sigma Master Black Belt with years of hands-on experience in industry, this on-target resource takes readers through the application of each Six Sigma statistical tool, while presenting a straightforward tutorial for effectively utilizing Excel and Minitab. With the help of this essential reference, managers can: Acquire the basic tools for data collection, organization, and description Learn the fundamental principles of probability Create and interpret dot plots, histograms, and box plots using Minitab Decide on sampling strategies, sample size, and confidence intervals Apply hypothesis tests to compare variance, means, and proportions Stay on top of production processes with statistical process control Use process capability analysis to ensure that processes meet customers' expectations Employ analysis of variance to make inferences about more than two population means Conduct a regression and residual analysis Design and analyze an experiment In addition, Six Sigma Statistics with Excel and Minitab enables you to develop a better understanding of the Taguchi Method...use measurement system analysis to find out if measurement processes are accurate...discover how to test ordinal or nominal data with nonparametric statistics...and apply the full range of basic quality tools. Filled with step-by-step exercises, graphical illustrations, and screen shots for performing Six Sigma techniques on Excel and Minitab, the book also provides clear, concise explanations of the theory for each of the statistical tools presented. Authoritative and comprehensive, Six Sigma Statistics with Excel and Minitab is a valuable skills-building resource for mastering all the statistical techniques for Six Sigma operations, while harnessing the power of Excel and Minitab.
The book focuses on the introduction of the basic concepts, processes, and tools used in Lean Six Sigma. A unique feature is the detailed discussion on Design for Six Sigma aided by computer modeling and simulation. The authors present several sample projects in which Lean Six Sigma and Design for Six Sigma were used to solve engineering problems or improve processes based on their own research and development experiences in engineering design and analysis. This book is intended to be a textbook for advanced undergraduate students, graduate students in engineering, and mid-career engineering professionals. It can also be a reference book, or be used to prepare for the Six Sigma Green Belt and Black Belt certifications by organizations such as American Society for Quality.
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
This book provides an accessible one-volume introduction to Lean Six Sigma and statistics in engineering for students and industry practitioners. Lean production has long been regarded as critical to business success in many industries. Over the last ten years, instruction in Six Sigma has been linked more and more with learning about the elements of lean production. Building on the success of the first and second editions, this book expands substantially on major topics of increasing relevance to organizations interested in Lean Six Sigma. Each chapter includes summaries and review examples plus problems with their solutions. As well as providing detailed definitions and case studies of all Six Sigma methods, the book uniquely describes the relationship between operations research techniques and Lean Six Sigma. Further, this new edition features more introductory material on probability and inference and information about Deming's philosophy, human factors engineering, and the motivating potential score – the material is tied more directly to the Certified Quality Engineer (CQE) exam. New sections that explore motivation and change management, which are critical subjects for achieving valuable results have also been added. The book examines in detail Design For Six Sigma (DFSS), which is critical for many organizations seeking to deliver desirable products. It covers reliability, maintenance, and product safety, to fully span the CQE body of knowledge. It also incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on experiment design, and includes practical experiments that will help improve students’ intuition and retention. The emphasis on lean production, combined with recent methods relating to DFSS, makes this book a practical, up-to-date resource for advanced students, educators and practitioners.
"Once solely the domain of engineers, quality control has become a vital business operation used to increase productivity and secure competitive advantage. Introduction to Statistical Quality Control offers a detailed presentation of the modern statistical methods for quality control and improvement. Thorough coverage of statistical process control (SPC) demonstrates the efficacy of statistically-oriented experiments in the context of process characterization, optimization, and acceptance sampling, while examination of the implementation process provides context to real-world applications. Emphasis on Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) provides a strategic problem-solving framework that can be applied across a variety of disciplines.Adopting a balanced approach to traditional and modern methods, this text includes coverage of SQC techniques in both industrial and non-manufacturing settings, providing fundamental knowledge to students of engineering, statistics, business, and management sciences.A strong pedagogical toolset, including multiple practice problems, real-world data sets and examples, provides students with a solid base of conceptual and practical knowledge."--
Lean production, has long been regarded as critical to business success in many industries. Over the last ten years, instruction in six sigma has been increasingly linked with learning about the elements of lean production. Introduction to Engineering Statistics and Lean Sigma builds on the success of its first edition (Introduction to Engineering Statistics and Six Sigma) to reflect the growing importance of the "lean sigma" hybrid. As well as providing detailed definitions and case studies of all six sigma methods, Introduction to Engineering Statistics and Lean Sigma forms one of few sources on the relationship between operations research techniques and lean sigma. Readers will be given the information necessary to determine which sigma methods to apply in which situation, and to predict why and when a particular method may not be effective. Methods covered include: • control charts and advanced control charts, • failure mode and effects analysis, • Taguchi methods, • gauge R&R, and • genetic algorithms. The second edition also greatly expands the discussion of Design For Six Sigma (DFSS), which is critical for many organizations that seek to deliver desirable products that work first time. It incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on the design of experiments, and on two level and full factorial experiments, to help improve student intuition-building and retention. The emphasis on lean production, combined with recent methods relating to Design for Six Sigma (DFSS), makes Introduction to Engineering Statistics and Lean Sigma a practical, up-to-date resource for advanced students, educators, and practitioners.
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.
Essential Statistics for Economics, Business and Management assumes no prior knowledge of statistics. It will also be highly relevant for the statistics component of courses in quantitative methods. The style of the book is similar to that of the highly successful Essential Mathematics for Economics and Business by Teresa Bradley and Paul Patton, with many worked examples integrated throughout. Emphasis is placed on verbalising concepts, problems and results of statistical analysis. This will help students learn how to start a problem, complete the calculations, and report the results in a way that makes sense to a non-statistician. Each concept is introduced with a brief but plausible explanation followed by Worked Examples. The Worked Examples will provide students with the necessary practice that they need in order to succeed at the subject. Emphasis is also placed on ?learning through doing? problems. Excel is used to encourage students in doing problems and to enhance understanding (with links to datasets online). Minitab printouts are also included in the text. Skills Development Exercises with brief solutions are included within the chapters, and Progress Exercises on theory and applications are provided at the end of each chapter. Solutions to all the worked examples and progress exercises are available as an appendix. Web-based supplementary materials will be provided for lecturers adopting the text, including additonal exercises and solutions, excel datasets and exercises, powerpoint slides with key formula, figures and tables. Students can access an online glossary and weblinks.
