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Question No : 1
Question No : 2
Question No : 3
A.Moving Average Chart
C.X bar Chart
Question No : 4
Question No : 5
A.Much is known
B.Little is known
C.We don¡¯t care
Question No : 6
Question No : 7
Question No : 8
A.The process is properly assumed to be a Normal process
B.The Mean of the process moving range is 1.78
C.The process is out of Control
D.This Capability Analysis used subgroups
E.Majority of the dimensional values are outside of the tolerance than within
Question No : 9
A.Expect the relationship to be Linear between the output and inputs
B.Know that there is a non-linear relationship between output and input(s)
C.Need to understand how to control a process output by controlling the input(s)
D.Experience several process defects and have no other way to fix hem
E.When it is very expensive or too late to measure the output
Question No : 10
A.When Reactant increases, the Energy Consumed increases.
B.The slope of the equation is a positive 130.5.
C.The predicted output Y is close to -18 when the Reactant level is set to 6.
D.Over 85 % of the variation of the Energy Consumed is explained by the Reactant via this Linear Regression.
Question No : 11
A.Non-linear relationships between the inputs X¡¯s and output Y
B.Uncertainty in the slope of the linear relationship between an X and a Y
C.Relationships between Y (output) and more than one X (Input)
D.Preventing the use of a Designed Experiment if unnecessary
E.We assume that the X¡¯s are independent of each other
Question No : 12
A.Train personnel often and thoroughly
B.Keep a Six Sigma project going on the process at all times
C.Design defect prevention into the product
D.Have each process consist of no more than five steps
Question No : 13
Question No : 14
D.nP and P Charts
Question No : 15
A.This Regression is an example of a Multiple Linear Regression.
B.This Regression is an example of Cubic Regression.
C.%Cu explains the majority of the process variance in heat flux.
D.Thickness explains over 80% of the process variance in heat flux.
E.The number of Residuals in this Regression Analysis is 26.