How to use regression analysis in quality control
This book models the regression protocol to suit applications within quality control while avoiding misapplication and misinterpretation. It gives an introduction to the general principles of regression analysis and an in-depth discussion of its use.Contents:What RA Is and What It Does The Modeling Process Enlisting Client Support The Method of Least Squares A Numeric Example: The Simple (One-Predictor) Model A Calibration Example: Inverse Regression The Necessity of Computers: Multiple Regression Finding the Causes of Quality Variation A Few More Fallacies and Dangers
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_ A2 analytic process ANOVA approximate associated basic booklet calculations Calibration Example calibration line choice coefﬁcient complex conﬁdence intervals control limits curvature curve data points data set diagnostics difﬁcult error variance established examine experimental design factors ﬁeld instrument Figure 6b Figure 9 ﬁnding ﬁrst ﬁt ﬁxed future given gives identiﬁed illustrated Imperfections Missed inclusive fraction individual inﬂuence intercept intercorrelation inverse estimation least squares mean measurement method non-linear regression Normal distribution observed obtained one-sided tolerance parameter prediction predictor variable procedure quadratic quality control quality variation reﬂect regression analysis regression line relationship represent residual error response variable sample sample size satisﬁed sequence number setup shown in Figure signiﬁcance Simple linear regression simple regression slope speciﬁcation speed SSReg SSRes standard deviation standard error statistical tolerance limits Sy.X t-ratios Table A2 techniques three inspectors tool wear