Design and Analysis of Gauge R&R Studies: Making Decisions with Confidence Intervals in Random and Mixed ANOVA Models
This book provides a protocol for conducting gauge repeatability and reproducibility (R&R) experiments. Such an experiment is required whenever a new test system is developed to monitor a manufacturing process. The protocol presented here is used to determine if the testing system is capable of monitoring the manufacturing process with the desired level of accuracy and precision. This protocol is not currently available in other books or technical reports. In addition to providing a protocol for testing a measurement system, the book presents an up-to-date summary of methods used to construct confidence intervals in normal-based random and mixed analysis of variance (ANOVA) models. Thus, this comprehensive book will be useful to scientists in all fields of application who wish to construct interval estimates for ANOVA model parameters. It includes approaches that can be applied to any ANOVA model, and because it contains detailed examples of all computations, practitioners will be able to easily apply the methods. The book describes methods for constructing two types of confidence intervals: modified large-sample (MLS) and generalized confidence intervals. Computer codes written in SAS and Excel are provided to perform the computations. Appendices are included for readers who are unfamiliar with confidence intervals or lack a basic understanding of random and mixed ANOVA models.
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analysis ANOVA applied approach approximate 100(1 assumptions balanced bound Burdick Chapter chi-squared random variables computed conﬁdence coefﬁcient conﬁdence interval consider constants crossed deﬁned in Equation deﬁned in Table Deﬁnition degrees of freedom demonstrate described determine distribution effects error exact example experiment experimental factors Figure functions gauge R&R experiment gauge R&R parameters gauge R&R study Graybill head independent chi-squared random interaction jointly independent Lower bound mean squares measurement system method misclassiﬁcation rates mixed model MLS intervals nested normal random variables Note observations obtained one-factor operators Order perform point estimate presented probability proposed random model recommended Replicates reports represent requires Residual Residual plots Residuals Versus respectively response sample shown in Table simulated statistical step tape treatment two-factor unbalanced Upper bound values variance zero