Design and Analysis of Experiments
This bestselling professional reference has helped over 100,000 engineers and scientists with the success of their experiments. The new edition includes more software examples taken from the three most dominant programs in the field: Minitab, JMP, and SAS. Additional material has also been added in several chapters, including new developments in robust design and factorial designs. New examples and exercises are also presented to illustrate the use of designed experiments in service and transactional organizations. Engineers will be able to apply this information to improve the quality and efficiency of working systems.
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2k design ABCD aliased analysis of variance Analyze the residuals ANOVA average block design column confidence interval consider defining relation degrees of freedom designed experiments effect estimates effects model error etch rate expected mean squares experimental design factor effects factor levels factorial experiment fixed effects fractional factorial design Latin square least squares linear main effects material type matrix method Minitab mixed model normal equations normal probability plot observations obtained operators orthogonal output P-value parameters percent confidence interval Problem procedure random effects model regression coefficients regression model replicates residuals versus resolution IV design response surface response variable ro ro ro sample shown in Table significant SSAB sum of squares Suppose temperature test statistic three-factor treatment combinations treatment means two-factor interactions type I error variance component