Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements

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John Wiley & Sons, Sep 20, 2011 - Technology & Engineering - 832 pages
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This hands-on book presents a complete understanding of Six Sigma and Lean Six Sigma through data analysis and statistical concepts

In today's business world, Six Sigma, or Lean Six Sigma, is a crucial tool utilized by companies to improve customer satisfaction, increase profitability, and enhance productivity. Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements provides a balanced approach to quantitative and qualitative statistics using Six Sigma and Lean Six Sigma methodologies.

Emphasizing applications and the implementation of data analyses as they relate to this strategy for business management, this book introduces readers to the concepts and techniques for solving problems and improving managerial processes using Six Sigma and Lean Six Sigma. Written by knowledgeable professionals working in the field today, the book offers thorough coverage of the statistical topics related to effective Six Sigma and Lean Six Sigma practices, including:

  • Discrete random variables and continuous random variables

  • Sampling distributions

  • Estimation and hypothesis tests

  • Chi-square tests

  • Analysis of variance

  • Linear and multiple regression

  • Measurement analysis

  • Survey methods and sampling techniques

The authors provide numerous opportunities for readers to test their understanding of the presented material, as the real data sets, which are incorporated into the treatment of each topic, can be easily worked with using Microsoft Office Excel, Minitab, MindPro, or Oracle's Crystal Ball software packages. Examples of successful, complete Six Sigma and Lean Six Sigma projects are supplied in many chapters along with extensive exercises that range in level of complexity. The book is accompanied by an extensive FTP site that features manuals for working with the discussed software packages along with additional exercises and data sets. In addition, numerous screenshots and figures guide readers through the functional and visual methods of learning Six Sigma and Lean Six Sigma.

Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements is an excellent book for courses on Six Sigma and statistical quality control at the upper-undergraduate and graduate levels. It is also a valuable reference for professionals in the fields of engineering, business, physics, management, and finance.

 

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Contents

Preface
Principles of Six Sigma
ESSENTIAL TOSIXSIGMA 1 13 SUCCESS FACTORS
Making Sense of Data in Six Sigma and Lean
Probability
UNION OFEVENTS
Discrete Random Variables and their Probability
Fundamentals of Capability and Rolled Throughput
Measurement Analysis 21 1 OVERVIEW 21 2 INTRODUCTION
AFFECT SPREAD
Optimization
SIGMA LEADERSHIPTHE FUEL
THE BEST PORTFOLIO WITH THE LEAST RISK
Survey Methods and Sampling Techniques
Appendix A Statistical Tables
Appendix B Answersto Selected OddNumbered

Linear andMultiple Regression

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About the author (2011)

Mikel J. Harry, PhD, is President and Chairman of the Board of the Six Sigma Management Institute. He is considered the principal architect of Six Sigma and one of the world's leading authorities in the field. Dr. Harry also focuses his research on applications of experimental design, inferential statistics, and statistical process control.

Prem S. Mann, PhD, is Professor and Chair of the Department of Economics at Eastern Connecticut State University. Dr. Mann has published numerous articles in the areas of labor economics, microeconomics, and statistics. He is the author of Introductory Statistics, Seventh Edition (Wiley).

Ofelia C. De Hodgins, MS, is a Six Sigma Global Master Black Belt. She has over twenty-five years of consulting experience in manufacturing and finance and has published more than thirty journal articles in the areas of physics, industrial engineering, statistics, and Statistical Process Control (SPC).

Richard L. Hulbert, MBA, is Vice President of Systems and Technology for the Bank of New York Mellon. He has more than thirty-five years of industry experience in the areas of network engineering, installation, implementation, network operations of technology infrastructure, distributed systems, market data, and government telecommunications.

Christopher J. Lacke, PhD, is Associate Professor of Mathematics at Rowan University. He has published numerous journal articles in his areas of research interest, which include decision analysis, Bayesian analysis, and operations research.

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