SIX SIGMA ICYB IASSC Certified Lean Six Sigma Yellow Belt Set 1

Only a Few Are Tested ✘ Opposite Factors Affect Technique ✘ One Factor At a Time ✘ Ordinary Fractional Approach Technique ✔

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.

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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.

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 ✔

Inefficiency of estimates ✘ Residuals ✘ Confounded data ✘ Gap Analysis ✔

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

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

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.

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