Design for Six Sigma statistics: 59 tools for diagnosing and solving problems in DFSS initiatives
In today's competitive environment, companies can no longer produce goods and services that are merely good with low defect levels, they have to be near-perfect. Design for Six Sigma Statistics is a rigorous mathematical roadmap to help companies reach this goal. As the sixth book in the Six Sigma operations series, this comprehensive book goes beyond an introduction to the statistical tools and methods found in most books but contains expert case studies, equations and step by step MINTAB instruction for performing: DFSS Design of Experiments, Measuring Process Capability, Statistical Tolerancing in DFSS and DFSS Techniques within the Supply Chain for Improved Results. The aim is to help you better diagnosis and root out potential problems before your product or service is even launched.
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Engineering in a Six Sigma Company
Describing Random Behavior
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analyze ANOVA assumption average Bernoulli trials Box-Cox transformation boxplot calculate capability metrics cell Chapter characteristics Click OK CM CM CM column components confidence interval control chart control limits Crystal Ball CTQs data set defect rate diameter engineers enter estimate example experiment experimenter factors failure Figure forecast formula Gage R&R Gage R&R study graph histogram hypothesis test illustrates interaction levels long-term lower main effects mean measurement system median method MINITAB normal distribution observations one-sample optimization options OptQuest P-value parameter Pareto chart perform population predict probability plot problem procedure process capability provides quantile random variable represents requires runs sample size scorecard Select Stat short-term shows simulation Six Sigma specific subgroup supplier Table target value tolerance design tolerance intervals tolerance limits transfer function transformation treatment structure trials units upper variation visual worksheet zero