A. Good (1-alpha) decisionB. Type II (Beta) errorC. Type I (alpha) errorD. Good (1-Beta) decision Type I (alpha) error The rejection of a test result, when the null hypothesis really is true is a type I (alpha) error.
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A. Accept the gage as validB. Check for ndc valueC. Check for Part VariationD. Check Equipment and Appraiser Variation Check Equipment and Appraiser Variation If %GRR value is >30%, the Six Sigma team should check the Equipment and Appraiser Variation to know which one of...
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A. Partial Least SquaresB. Maximum Likelihood EstimateC. Ordinary Least SquaresD. None of the above Ordinary Least Squares For a Simple Linear Regression in determining the best fit line, Ordinary Least Squares is the method used.
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A. 0.007B. 0.00655C. 0.0062D. 0.0055 0.0062 Z = (150-125)/10 = 25/10 = 2.5 Area under 2.5 corresponds to 0.0062. Thus, the probability of a Type II Error, which is beta, is 0.62%.
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A. A type I error improves with each individual analysisB. Interactions are clearly determinedC. A type I error increases with each individual analysisD. The optimum combination of factors is revealed A type I error increases with each individual analysis With each single comparison, more errors...
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A. Simplex designsB. Orthogonal designsC. Screening experimentsD. Mixture experiments Screening experiments In screening experiments, highly fractional factorial designs are used to look for only factor main effects. They are called screening because they try to eliminate seemingly unimportant factors.
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A. CollinearityB. ConfoundedC. CorrelationD. Covariates Collinearity Having two variables that are highly correlated in the experimental model will make it difficult or impossible to detect which factor really affects the response. This condition is called collinearity. The correct answer is Collinearity .
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