File Info
| Exam | IASSC Certified Lean Six Sigma Green Belt |
| Number | ICGB |
| File Name | Six Sigma.ICGB.SelftestTraining.2020-12-14.95q.vcex |
| Size | 516 KB |
| Posted | Dec 14, 2020 |
| Download | Six Sigma.ICGB.SelftestTraining.2020-12-14.95q.vcex |
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Demo Questions
Question 1
A Belt concludes a Lean Six Sigma project with the creation of a Control Plan. At what point can the Control Plan be closed?
- Never, a Control Plan is a living document
- As soon as the Champion signs off
- Within 30 days of the LSS project review team meeting
- After the project has been presented at the recognition event
Correct answer: A
Question 2
When analyzing a data set we frequently graph one metric as a function of another. If the slope of the Correlation line is -2.5 we would say the two metrics are
___________ correlated?
- Positively
- Not
- Negatively
- None
Correct answer: C
Question 3
Multiple Linear Regressions (MLR) is best used when which of these are applicable? (Note: There are 3 correct answers).
- Non-linear relationships between the inputs X’s and output Y
- Uncertainty in the slope of the linear relationship between an X and a Y
- Relationships between Y (output) and more than one X (Input)
- Preventing the use of a Designed Experiment if unnecessary
- We assume that the X’s are independent of each other
Correct answer: CDE
Question 4
Fractional Factorial designs for an experimental approach are used when ____________ about the multiple metric interaction in a process.
- Much is known
- Little is known
- We don’t care
- Data exists
Correct answer: B
Question 5
A Belt will occasionally do a quick experiment referred to as an OFAT which stands for ____________________.
- Only a Few Are Tested
- Opposite Factors Affect Technique
- One Factor At a Time
- Ordinary Fractional Approach Technique
Correct answer: C
Question 6
Which statement(s) are correct for the Regression Analysis shown here? (Note: There are 2 correct answers).
- This Regression is an example of a Multiple Linear Regression.
- This Regression is an example of Cubic Regression.
- %Cu explains the majority of the process variance in heat flux.
- Thickness explains over 80% of the process variance in heat flux.
- The number of Residuals in this Regression Analysis is 26.
Correct answer: AD
Question 7
The Regression Model for an observed value of Y contains the term which represents the Y axis intercept when X = 0.
- True
- False
Correct answer: A
Question 8
Which statement(s) are true about the Fitted Line Plot shown here? (Note: There are 2 correct answers).
- When Reactant increases, the Energy Consumed increases.
- The slope of the equation is a positive 130.5.
- The predicted output Y is close to -18 when the Reactant level is set to 6.
- Over 85 % of the variation of the Energy Consumed is explained by the Reactant via this Linear Regression.
Correct answer: CD
Question 9
After reviewing the Capability Analysis shown here select the statement(s) that are untrue.
- The process is properly assumed to be a Normal process
- The Mean of the process moving range is 1.78
- The process is out of Control
- This Capability Analysis used subgroups
- Majority of the dimensional values are outside of the tolerance than within
Correct answer: A
Question 10
The actual experimental response data varied somewhat from what a Belt had predicted them to be. This is the result of which of these?
- Inefficiency of estimates
- Residuals
- Confounded data
- Gap Analysis
Correct answer: B
