Which statement(s) are true about the Fitted Line Plot shown here?
A. When Reactant increases, the Energy Consumed increases.
B. The predicted output Y is close to -18 when the Reactant level is set to 6.
C. Over 85 % of the variation of the Energy Consumed is explained by the Reactant via this Linear Regression.
D. Both b and c
A 1-Sample t-test is used to compare an expected population Mean to a target.
A. True
B. False
Multiple Linear Regressions (MLR) is best used when which of these are applicable?
A. We assume that the X's are independent of each other
B. Preventing the use of a Designed Experiment if unnecessary
C. Relationships between Y (output) and more than one X (Input)
D. All of the above
The generation of a Regression Equation is justified when we _____.
A. Expect the relationship to be Linear between the output and inputs
B. Experience several process defects and have no other way to fix hem
C. Need to understand how to control a process output by controlling the input(s)
D. All of the above
The actual experimental response data varied somewhat from what a Belt had predicted them to be. This is the result of which of these?
A. Inefficiency of estimates
B. Residuals
C. Confounded data
D. Gap Analysis
Unequal Variances can be the result of differing types of distributions.
A. True
B. False
After reviewing the Capability Analysis shown here select the statement that is untrue.
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
The Regression Model for an observed value of Y contains the term to which represents the Y axis intercept when X = 0.
A. True
B. False
A Belt will occasionally do a quick experiment referred to as an OFAT which stands for _____.
A. Only a Few Are Tested
B. Opposite Factors Affect Technique
C. One Factor At a Time
D. Ordinary Fractional Approach Technique
Which statement(s) are correct for the Regression Analysis shown here?
A. This Regression is an example of a Multiple Linear Regression.
B. This Regression is an example of Cubic Regression.
C. Thickness explains over 80% of the process variance in heat flux.
D. Both a and c
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