Design for Six Sigma Statistics, Chapter 9 - Detecting Changes in Nonnormal Data
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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92 Testing for Goodness of Fit
93 Normalizing Data with Transformations
3-parameter lognormal Anderson-Darling test ANOVA apply assumption average rank Box-Cox transformation calculate Click OK confidence interval continuous random variables Control Charts count of observations dataset Days to close Dielectric Thickness distribution model distribution shapes Dotplot eccentricity Exact P-value example Figure Fisher test Formulas and Information Fritz Glen goodness-of-fit tests graph Histogram of Days hypothesis tests Individual Distribution Identification Johnson transformation Kruskal-Wallis Test kurtosis Lathe leakage current leakage inductance ln(X lognormal distribution measurements MINITAB functions MINITAB Report nonnormal nonparametric tools normal distribution normal probability plot normal-based procedures normal-based tools Normality Test old fixture one-sample sign test One-Sample Signed Rank options parameter pattern of points Paula Platykurtic process distribution process median Quality Tools random variables Runout Data sample sizes Select Stat Signed Rank Test Six Sigma standard deviation subgroups Table tail test median test statistic Thyristor transformed data tribution Tukey end-count test Weibull Wilcoxon test Wilcoxon’s One-Sample Signed ZYiZ