Design for Six Sigma Statistics, Chapter 4 - Estimating Population Properties
Here is a chapter from Design for Six Sigma Statistics, written by a Six Sigma practitioner with more than two decades of DFSS experience who provides a detailed, goal-focused roadmap. It shows you how to execute advanced mathematical procedures specifically aimed at implementing, fine-tuning, or maximizing DFSS projects to yield optimal results. For virtually every instance and situation, you are shown how to select and use appropriate mathematical methods to meet the challenges of today's engineering design for quality.
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45 Estimating the Probability of Defective Units by the Binomial Probability 960
46 Estimating the Rate of Defects by the Poisson Rate Parameter 955
ˆLT ˆST Assumption Zero bathtub curve binomial random variable Bore Diameter calculate changes per panel Click OK column confidence bound confidence interval confidence level containing control limits count of defective datasets defective units DFSS project Distribution ID plot exact confidence interval example exponential distribution failure rate formula function graph histogram IX,MR control chart lognormal long-term variation lower confidence limit Lower limit mean and standard measurements median methods Mike MINITAB motors MTTF normal distribution np chart plot points point estimate Poisson process Poisson rate parameter population mean predict probability curve probability of observing probability plot quantile random sample rational subgroups reliability represents sample mean sample standard deviation Select Stat shape parameter short-term and long-term short-term variation Six Sigma Solution stable statistical tolerance bound Statistical tolerance intervals touchup true value unbiased estimator unstable process upper confidence limit Upper limit Weibull distribution zero failures