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  3. IASSC Lean Six Sigma - Green Belt

PEOPLECERT IASSC Lean Six Sigma - Green Belt IASSC Lean Six Sigma - Green Belt Exam Questions in PDF

Free PEOPLECERT IASSC Lean Six Sigma - Green Belt Dumps Questions (page: 6)

IASSC Lean Six Sigma - Green Belt PDF — 200 Questions

Multiple Linear Regressions (MLR) is best used when which of these are applicable? (Note: There are 3 correct answers).

  1. Non-linear relationships between the inputs X's and output Y
  2. Uncertainty in the slope of the linear relationship between an X and a Y
  3. Relationships between Y (output) and more than one X (Input)
  4. Preventing the use of a Designed Experiment if unnecessary
  5. We assume that the X's are independent of each other

Answer(s): C,D,E



The generation of a Regression Equation is justified when we _____________. (Note: There are 4 correct answers).

  1. Expect the relationship to be Linear between the output and inputs
  2. Know that there is a non-linear relationship between output and input(s)
  3. Need to understand how to control a process output by controlling the input(s)
  4. Experience several process defects and have no other way to fix hem
  5. When it is very expensive or too late to measure the output

Answer(s): A,C,D,E



Which statement(s) are correct for the Regression Analysis shown here? (Note: There are 2 correct answers).

  1. This Regression is an example of a Multiple Linear Regression.
  2. This Regression is an example of Cubic Regression.
  3. %Cu explains the majority of the process variance in heat flux.
  4. Thickness explains over 80% of the process variance in heat flux.
  5. The number of Residuals in this Regression Analysis is 26.

Answer(s): A,D



The Regression Model for an observed value of Y contains the term ?o which represents the Y axis intercept when X = 0.

  1. True
  2. False

Answer(s): A



Which statement(s) are true about the Fitted Line Plot shown here? (Note: There are 2 correct answers).

  1. When Reactant increases, the Energy Consumed increases.
  2. The slope of the equation is a positive 130.5.
  3. The predicted output Y is close to -18 when the Reactant level is set to 6.
  4. Over 85 % of the variation of the Energy Consumed is explained by the Reactant via this Linear Regression.

Answer(s): C,D



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