Statistics for Six Sigma Made Easy! Second Edition

  • 4h 40m
  • Warren Brussee
  • McGraw-Hill
  • 2012

Six Sigma is one of the most effective strategies for improving processes, creating better products, and boosting customer satisfaction, but business leaders often balk at its reputation for being too complex. Don't fall into that trap. Six Sigma is simple to understand and implement--if you have Statistics for Six Sigma Made Easy!

Warren Brussee has helped businesses save millions of dollars with Six Sigma, and he explains how you can achieve similar results in this step-by-step guide. He presents a thorough overview of the Six Sigma methodology and techniques for successful implementation, as well as a clear explanation of DMAIC--the problem-solving method used by Six Sigma Greenbelts. Statistics for Six Sigma Made Easy! provides:

  • A simplified form of the most common Six Sigma tools
  • All the basic Six Sigma formulas and tables
  • Dozens of Six Sigma statistical problem-solving case studies
  • A matrix for finding the right statistical tool to meet your needs
  • Basic Greenbelt training in one concise reference

Best of all, no background in statistics is required--you can start improving quality and initiating costsaving improvements right away. Statistics for Six Sigma Made Easy! is the only reference you need to facilitate real-world application of Six Sigma tools.

About the Author

Warren Brussee is a Six Sigma Green Belt with decades of experience implementing and training Six Sigma. Brussee spent 33 years at GE, ground zero for Six Sigma, and currently consults and teaches on the topic. He is the author of Statistics for Six Sigma Made Easy.

In this Book

  • Six Sigma Methodology and Management's Role in Implementation
  • DMAIC: The Basic Six Sigma Road Map
  • Simplified QFD
  • Simplified FMEA
  • Cause-and-Effect Fishbone Diagram
  • Simplified Process Flow Diagram
  • Correlation Tests
  • Getting Good Samples and Data
  • Simplified Gauge Verification
  • Probability
  • Data Plots and Distributions
  • Testing for Statistically Significant Change Using Variables Data
  • Testing for Statistically Significant Change Using Proportional Data
  • Testing for Statistically Significant Change Using Nonnormal Distributions
  • Simplified Design of Experiments
  • Simplified Control Charts
  • What Tolerance is Really Required?
  • Simplified Linear Transfer Functions
  • Comparing Six Sigma Data with Quality Department Data
  • The Next Step for Six Sigma: Manufacturing Innovation
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