Six Sigma eLearning Glossary of Six Sigma Terms (E - M)

Effect
The change in the average value of the output caused by a change in an input.

Entitlement
The best potential performance of a process, based on the current design.

Error
Any deviation from the intended process or from the value expected according to a model.

EVOP
Evolutionary Operation, a method developed by George Box to determine the direction for improving a process while production is underway using simple 2^1, 2^2 or 2^3 experiments.

Experimental Error
The variation in data left over after all significant sources of variability have been accounted for. In DOE (design of experiments), experimental error is often a synonym for residuals, the differences between observed values and values expected based on the regression equation obtained from the analysis of the experiment.

Factor
An input variable being studied in an experiment or ANOVA.

Failure Effect
The way a failure impacts the customer.

Failure Mode
The manner in which the process could potentially fail to meet the process or customer requirements.

Fits
The expected values from a model; the predicted values of the output at a specific set of input conditions.

F-Ratio
A statistic for evaluating whether two variances or standard deviations are significantly different, obtained by dividing one variance by another variance.

Full Model
The best-fit predictive equation using all of the factors and interactions in an experiment.

Full-Factorial Experiment
An experiment that examines the effect of all possible combinations of factors and levels.

Goalpost Mentality
Anything outside the specification limits represents quality losses.

Green Belt
Six Sigma trained key contributor and team leader, a part-time quality position.

Hidden Factory
The differences between the documented process and the actual process.

Information Board
Communication tool for tracking EVOP improvements.

Instrument Correlation
A measure of the linear association between two measurement systems.

Interaction
The combined effect of two factors observed over and above the singular effect of each factor against the level the other factor. A significant interaction indicates that the effect of each factor on the response changes depending on the value of the other factor.

Interaction Plots
A graphical display of the interaction in which the means of the responses at each level of a factor are shown for each level of a second factor.

Lean Manufacturing
A manufacturing improvement approach based on the premise that work is accompanied waste or non-value-added effort that should be minimized or eliminated.

Level
The value of an input in an experimental run.

Levene’s Test
Test for equal variances that can be used for data that is represented by a non-normal distribution.

Main Effect
The average change of the output observed during a change from one level of an input to another level.

Main Effects Plot
A plot of means at the various levels of each factor compared to the overall mean.

Master Black Belt
Highly experienced, recognized expert; consultant to the Six Sigma project team.

Mean
The arithmetic average of a set of values: the sum of a set of values divided by the number of values.

Median
The middle value found after a set of values has been rank ordered. If there are an even number of values, then it is the average of the middle two numbers.

Mistake-Proofing
Fool-proofing, error-proofing, Poka Yoke:  a control method that makes it unlikely or impossible for an error to occur.

Mode
The most frequently occurring value in a data set.

Muda
Waste.

Multiple Regression
A method for determining an optimal equation (least-squared difference between observed and predicted values for the response) for a response as a function of several inputs, y= b0 + b1 X1 + b2 X2 + b3 X3 + error.