To draw inferences about a sample population being studied by modeling patterns of data in a way that accounts for randomness and uncertainty in the observations is known as ____________________.
A. Influential Analysis
B. Inferential Statistics
C. Physical Modeling
D. Sequential Inference
Bias in Sampling is an error due to lack of independence among random samples or due to systematic sampling procedures.
A. True
B. False
A primary benefit of using a Multi-Vari Chart is it provides a visual presentation of two-way interactions.
A. True
B. False
__________ Distributions occur when data comes from several sources that are supposed to be the same yet are not.
A. Skewed
B. Bimodal
C. Gaussian
D. Tri-peaked
A Six Sigma tool that helps to screen factors by using graphical techniques to logically subgroup multiple discrete X's plotted against a continuous Y is known as a _____________Chart.
A. SIPOC
B. Multi-Vari
C. Box Plot
D. Whisker
Select all the statements that are true after reviewing the Capability Analysis shown here. (Note: There are 4 correct answers).
A. The process is out of Control.
B. The process is properly assumed to be a Normal process.
C. The Mean of the process moving range is 1.78.
D. This Capability Analysis used subgroups.
E. Majority of the dimensional values are outside of the tolerance than within.
The Regression Model for an observed value of Y contains the term ?o which represents the Y axis intercept when X = 0.
A. True
B. False
Which statement(s) are true about the Fitted Line Plot shown here? (Note: There are 2 correct answers).
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.
Which statement(s) are correct for the Regression Analysis shown here? (Note: There are 2 correct answers).
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.
The generation of a Regression Equation is justified when we _____________. (Note: There are 4 correct answers).
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
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