Free LSSMBB – Lean Six Sigma Master Black Belt Practice Test Question

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  The air-fuel ratio explains most of the TurbineOutput variation

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  The Regression explains over 98% of the process variation

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  This Multiple Linear Regression has three statistically significant independent variables

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  If the air-fuel ratio increases by 1, the TurbineOutput more than triples

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  The SteamExitTemp explains the most variation of the TurbineOutput
  
  

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  When the cutting speed increased from low to high level, the tool age increases

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  The coefficient of the metal hardness is positively related to the output of tool age

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  The coded coefficient is lower for cutting speed than the cutting angle related to the output of tool age

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  These plots prove a statistically significance factor with 95% confidence

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  These plots are an example of interaction plots
  
  

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  Shaping

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  Sorting

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  Shifting

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  Straightening
  
  

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  The Main Effects for the 5 factors are not aliased or confounded but the 2-way interactions are confounded with the 3-way interactions

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  The Main Effects are confounded with only 4-way interactions

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  The Experimental Design is half-fractional

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  The experiment has 8 experimental runs with the first factor at the high level

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  The experiment has only 4 experimental runs with the 5th factor at the high level
  
  

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  With 95% confidence, we cannot conclude if the samples are from three Normal Distributions.

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  With greater than 95% confidence, we conclude the samples are from Non-normal Distributions.

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  If we wanted to compare the Central Tendencies of these three samples we would use the one way ANOVA test.

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  If we wanted to compare the Central Tendencies of these three samples we could use Mood’s Median test.
  
  

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  The Correlation Coefficient is always greater than the Regression Coefficient in a SLR

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  General Regression Analysis deals only with Continuous Data

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  Non-linear Regressions can explain curvature when with more statistical confidence than Linear Regressions

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  SLR can help quantify the significance of variation in X that influences the variation in Y via a mathematical equation

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  A Correlation does not explain causation but a Regression Analysis with a statistically valid mathematical equation does explain causation
  
  

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  Created for every project

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  Based on team's collective opinions

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  Updated whenever a parameter is changed

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  Used to show each step in a process

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  A living document throughout project lifecycle
  
  

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  Destructive variable gage R&R and Crossed Study

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  Destructive variable gage R&R and Nested Study

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  Nondestructive variable gage R&R and Crossed Study

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  Nondestructive variable gage R&R and Nested Study
  
  

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  -3

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  3

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  -6

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  6
  
  

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  Data shift analysis

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  Outlier analysis methods

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  Center Line and Control Limits

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  None of the above
  
  

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  F test

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  1-Sample t-test

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  2-Sample t-test

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  ANOVA test
  
  

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  Management

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  Merger

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  Materials

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  Medical
  
  

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  Estimates from Industry Experts

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  Simulated failure rates from reliability models

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  Best guesses by those preparing FMEA

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  Historical failure data
  
  

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  Linearity

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  Bias

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  Stability

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  Magnitude
  
  

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  True

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  False