Six Sigma statistical methodology using Minitab Problem Solving and Data Analysis using Minitab presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving. Each example and exercise is broken down into the exact steps that must be followed in order to take the reader through key learning points and work through complex analyses. Exercises are featured at the end of each example so that the reader can be assured that they have understood the key learning points. Key features: Provides readers with a step by step guide to problem solving and statistical analysis using Minitab 16 which is also compatible with version 15. Includes fully worked examples with graphics showing menu selections and Minitab outputs. Uses example based learning that the reader can work through at their pace. Contains hundreds of screenshots to aid the reader, along with explanations of the statistics being performed and interpretation of results. Presents the core statistical techniques used by Six Sigma Black Belts. Contains examples, exercises and solutions throughout, and is supported by an accompanying website featuring the numerous example data sets. Making Six Sigma statistical methodology accessible to beginners, this book is aimed at numerical professionals, students or academics who wish to learn and apply statistical techniques for problem solving, process improvement or data analysis whilst keeping mathematical theory to a minimum.

Industrial Statistics with MINITAB demonstrates the use of MINITAB as a tool for performing statistical analysis in an industrial context. This book covers introductory industrial statistics, exploring the most commonly used techniques alongside those that serve to give an overview of more complex issues. A plethora of examples in MINITAB are featured along with case studies for each of the statistical techniques presented. Industrial Statistics with MINITAB: Provides comprehensive coverage of user-friendly practical guidance to the essential statistical methods applied in industry. Explores statistical techniques and how they can be used effectively with the help of MINITAB 16. Contains extensive illustrative examples and case studies throughout and assumes no previous statistical knowledge. Emphasises data graphics and visualization, and the most used industrial statistical tools, such as Statistical Process Control and Design of Experiments. Is supported by an accompanying website featuring case studies and the corresponding datasets. Six Sigma Green Belts and Black Belts will find explanations and examples of the most relevant techniques in DMAIC projects. The book can also be used as quick reference enabling the reader to be confident enough to explore other MINITAB capabilities.

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.

The author develops and illustrates statistical ideas in the context of the kinds of practical data analysis that managers actually undertake, starting with the simple applications to quality control. MINITAB is used throughout allowing students to perform meaningful statistical computations and to concentrate on learning how statistics can be used effectively in business. Written for introductory courses in business statistics (especially at MBA level), the new edition offers a simpler yet more systematic and thorough approach to data analysis and incorporates new features of MINITAB Release 7, especially control chart commands.

This guide has been written to support all those who gather, analyse, and interpret data in the course of their work. The guide aims to remove some of the mystique surrounding statistics as well as showing how the techniques can be used to arrive at decisions in a logical data driven manner. This guide should be of interest and use to Six Sigma Green and Black Belts, Operations Scientists and Managers, Social Science Students and Practitioners as well as those involved in practical statistical analysis for many other reasons. For each technique this guide explains what the technique is and how it is used, it shows how to enter the data into MINITAB 18®, provides logical click-by-click instructions on how to execute each technique and explains how to interpret the results. The data files and example worksheets may be downloaded to aid the user.

This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy – when to use which technique – are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics.

This book introduces the reader to Six Sigma, a problem-solving technique for reducing defects and variation in processes. The author uses DMAIC phases (Define, Measure, Analyze, Improve and Control) and a data-centric approach, leveraging applied statistics with Minitab®. Readers are enabled to solve novel problems where there isn’t an apparent root cause or solution identified. The author walks readers through an (imaginary) case study, explaining both the DMAIC approach and how to use Minitab in a practical way. The presentation includes data sets and instructions on how to analyze data in the context of Six Sigma using Minitab.