Clt Six Sigma Glossary

GLOSSÁRIO SIX SIGMACOMUNIDADELEANTHINKING
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Benchmarking
An improvement process whereby a company measures its performance against that of
best-in-class companies, determines how those companies achieved their performance
levels, and uses the information to improve its own performance.
Black Belt
Full-time Six Sigma project leader who is certified following a four-month training and
application program and successful completion of two Six Sigma Projects, the first under
the guidance of a Master Black Belt, the second more autonomously.
“Breakthrough Strategy”
The data driven, Six Sigma process improvement strategy involving four phases:
Measure, Analyze, Improve and Control.
Cause
That which produces an effect or brings about change.
Cause-And-Effect Diagram
A schematic sketch, usually resembling a fishbone, which illustrates the main causes and
subcauses leading to an effect (symptom). Also known as Fishbone Diagram.
Champion
Member of the senior Aircraft Engines staff who has undergone extensive Six Sigma
training. Champions provide direction, resources and support to the Six Sigma effort and
approve and review projects.
Characteristic
A definable or measurable feature of a process, product or variable.
Control Chart
A graphical rendition of a characteristic’s performance across time in relation to its
natural limits and central tendency.
Correlation
The determination of the effect of one variable upon another in a dependent situation.
Cp
A widely used capability index for process capability studies. It may range in value from
zero to infinity with a larger value indicating a more capable process. Six Sigma
represents Cp of 2.0.

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Cpk
An index combining Cp and K (Difference between the process mean and the specification
mean) to determine whether the process will produce units within tolerance. Cpk is
always less than or equal to Cp. When the process is centered at nominal, Cpk is equal to
Cp.
Critical To Quality (CTQ)
An element of a design or a characteristic of a part that is essential to quality in the eyes
of the customer, formerly known as a key quality characteristic (KQC).
Data
Factual information used as a basis for reasoning, discussion or calculation; often refers
to quantitative information.
Defect
A failure to meet an imposed requirement on a single quality characteristic or a single
instance of nonconformance to the specification.
Defects Per Million Opportunities (DPMO)
The number of defects counted, divided by the actual number of opportunities to make a
defect, then multiplied by one million. A direct measure of sigma level.
Defects Per Unit (DPU)
The number of defects counted, divided by the number of products or characteristics
produced. A process of counting and reducing defects as an initial step toward Six Sigma
quality.
Defective
A unit of product containing one or more defects.
Design For Manufacturability (DFM)
A concept in which products are designed within the current manufacturing process
capability to ensure that engineering requirements are met during production.
Design of Experiments (DOE)
Statistical experimental designs to economically improve product and process quality. A
major tool used during the “Improve Phase” of Six Sigma methodology.
Distributions
Tendency of large numbers of observations to group themselves around some central
value with a certain amount of variation or “scatter” on either side.

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Effect
That which was produced by a cause.
Experiment
A test under defined conditions to determine an unknown effect; to illustrate or verify a
known law; to test or establish a hypothesis.
Experimental Error
A test under defined conditions to determine an unknown effect; to illustrate or verify a
known law; to test or establish a hypothesis.
“Factory” Processes
For Six Sigma purposes, defined as design, manufacturing, assembly or test processes
which directly impact hardware (see also transaction processes).
Fishbone Diagram
A schematic sketch, usually resembling a fishbone, which illustrates the main causes and
subcauses leading to an effect (symptom). Also known as Cause-And-Effect Diagram.
Failure Mode Effects Analysis (FMEA)
A process in which each potential failure mode in every sub-item of an item is analyzed
to determine its effect on other sub-items and on the required function of the item.
“Five Ms”
Major sources of variation: manpower, machine, method, material and measurement.
Additionally, “environment” is considered to be a source of variation.
Frequency Distribution
The pattern or shape formed by the group of measurements in a distribution.
Gage Repeatability & Reproducibility (Gage R&R)
A measurement system evaluation to determine equipment variation and appraiser
variation. This study is critical to ensure that the collected data is accurate.
Histogram
Vertical display of a population distribution in terms of frequencies; a formal method of
plotting a frequency distribution.
Independent Variable
A controlled variable; a variable whose value is independent of the value of another
variable.

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Interaction
When the effects of a factor A are not the same at all levels of another factor B.
Lower Control Limit
A horizontal dotted line plotted on a control chart which represents the lower process
limit capabilities of a process.
Master Black Belt
An expert in quality techniques specially trained to advise leaders, facilitate quality teams
and accelerate process improvement. Master Black Belts select, train and mentor Black
Belts; develop and implement the Six Sigma deployment plan; and select and ensure
completion of Six Sigma projects.
Nonconformity
A condition within a unit which does not conform to some specification, standard, and/or
requirement; often referred to as a defect; any given nonconforming unit can have the
potential for more than one nonconformity.
Normal Distribution
A continuous symmetrical density function characterized by a bell-shaped curve, e.g.,
distribution of sampling averages.
Pareto Diagram
A chart which ranks, or places in order, common occurrences.
Primary Control Variables
The major independent variables used in the experiment.
Probability
The chance of something happening; the percent or number of occurrences over a large
number of trails.
Process
A particular method of doing something, generally involving a number of steps or
operations.
Process Capability
The relative ability of any process to produce consistent results centered on a desired
target value when measured over time.

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Process Control Chart
Any of a number of various types of graphs upon which data are plotted against specific
control limits.
Process Map
Flow chart to analyze a process by breaking it down into its component steps, and then
gaining a better understanding of the process, step-by-step.
Process Spread
The range of values which a given process characteristic displays; this particular term
most often applies to the range but may also encompass the variance. The spread may be
based on a set of data collected at a specific point in time or may reflect the variability
across a given amount of time.
Quality Functional Deployment (QFD)
Structured methodology to identify and translate customer needs and wants into technical
requirements and measurable features and characteristic. This tool is used to identify
Critical to Quality Characteristics (CTQCs).
Random
Selecting a sample so each item in the population has an equal chance of being selected;
lack of predictability; without pattern.
Random Cause
A source of variation which is random; a change in the source (“trivial many” variables)
will not produce a highly predictable change in the response (dependent variable), e.g., a
correlation does not exist; any individual source of variation results in a small amount of
variation in the response; cannot be economically eliminated from a process; an inherent
natural source of variation.
Random Variation
Variations in data which result from causes which cannot be pinpointed or controlled.
Regression Analysis
A statistical technique for determining the relationship between one response and one or
more independent variables.
Robust
The condition or state in which a response parameter exhibits hermetically to external
cause of a nonrandom nature; e.g., impervious to perturbing influence.

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Rolled Yield
The combined resulting quality level, stated as a percent acceptable, that occurs when
several processes known to produce defects at some rate are combined to produce a
product. For example, a product that requires 100 steps, each of which produces a yield
of 98.78% will produce a rolled yield of 0%, that is, no acceptable products.
Scatter Diagram
A diagram that displays the relationships between two variables.
Sigma
Standard deviation; an empirical measure based on the analysis of random variation in a
standard distribution of values; a uniform distance from the mean or average value such
that 68.26% of all values are within 1 sigma on either side of the mean, 95.44% are
within 2 sigma, 99.73% are within 3 sigma, 99.9% are within 4 sigma and so forth.
Sigma Level
A statistical estimate of the number of defects that any process will produce equivalent to
defects per million opportunities for that process.
Six Sigma Quality
A combination of verified customer requirements reflected in robust designs and matched
to the capability of production processes that creates products with fewer then 3.4 defects
per million opportunities to make a defect. World-class quality. A collection of tools
and techniques for raising quality to worked-class levels.
Stable Process
A process which i free of assignable causes, e.g., in statistical control.
Standard Deviation
A statistical index of variability which describes the spread.
Statistical Control
A quantitative condition which describes a process that is free of assignable/special
causes of variation, e.g., variation in the central tendency and variance. Such a condition
is most often evidenced on a control chart.
Statistical Process Control
The application of statistical methods and procedures relative to a process and a given set
of standards.

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Transaction Processes
For Six Sigma purposes, defined as any business process that contributes to customer
satisfaction or impacts operating efficiency and which is designated by a vice president or
by GE Corporate as a focus for process improvement. Such efforts will be led by the
process owner, with teams being led by specially trained transaction project leaders
and/or by certified Black Belts.
Transaction Project Leader
An individual designated to lead a transaction process improvement project. Transaction
project leaders attend a four-day course in specific Six Sigma tools and tactics.
Upper Control Limit
A horizontal line on a control chart (usually dotted) which represents the upper limits of
process capability.
Variable
A characteristic that may take on different values.
Variables Data
A numerical measurement made at the interval or ratio level; quantitative data, e.g..,
ohms, voltage, diameter; subdivisions, of the measurement scale are conceptually
meaningful, e.g.., 1.6478 volts.
Variation
Any quantifiable difference between individual measurements; such differences can be
classified as being due to common causes (random) or special causes (assignable).
“Xs”
Designation in Six Sigma terminology for those variables which are independent, root
causes; as opposed to “Ys” which are dependent outputs of a process. Six Sigma focuses
on measuring and improving Xs, to see subsequent improvement in Ys.
X & R Charts
A control chart which is a representation of process capability over time; displays the
variability in the process average and range across time.
“Ys”
Designation in Six Sigma terminology for those variables which are dependent outputs of
a process, as opposed to “Xs” which are independent root causes.

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6M’s - Man, Machines, Materials, Methods, Measurement, Mother Nature
ANOVA - Analysis of Variance
BB - Black Belts
C&E Matrix - Cause & Effect Matrix
CAP - Change Acceleration Process
C&E - Cause & Effect
COPQ - Cost of Poor Quality
COQ - Cost of Quality
Cp - Capability Process Index (Ideal) - Pooled
Cpk - Capability Process Index (Real) - Pooled
CTQ - Critical to Quality
CUSUM - Cumulative Sum
DF - Degrees of Freedom
DFM - Design for Manufacturing
DFSS - Design for Six Sigma
DOE - Design of Experiments
DPM - Defects per Million
DPMO - Defects per Million Opportunities
DPO - Defects per Opportunities
DPU - Defects per Unit
EVOP - Evolutionary Operation
EWMA - Exponential Weight Moving Average
FMEA - Failure Mode & Effect Analysis
GAGEAOV - Gage Analysis of Variance
GRR - Gage Repeatability & Reproducibility
IDOV - Identify, Design, Optimize, Validate
IQR - Inter Quartile Range
ISO - International Organization for Standardization
KNP - Key Noise Parameters
KPI (Factors) - Key Process Inputs
KPIV (KCP) - Key Process Input Variable (Key Control Parameter)
KPOV or - Key Process Output Variable(Response)
LCL - Lower Controls Limits
LSL - Lower Specification Limits
MAIC - Measurement, Analysis, Improvement, Control
MBB - Master Black Belt

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MBNQA - Malcolm Baldrich National Quality Award
MGF - Minitab Graph File
MSA - Measurement System Analysis
MTB - Minitab
MTW - Minitab Worksheet
NPI - New Product Introduction
OJT - On the Job Training
P(ND) - Probability (Not Defective)
PEAR - Process, Engineering, Application, Regulatory CTQ’s
Pp - Capability Process Index (Ideal) - Overall
Ppk - Capability Process Index (Real) - Overall
PPM - Parts per Million
QA - Quality Assurance
QFD - Quality Functional Deployment
P/T Ratio - Precision / Tolerance Ratio
ROI - Return of Investment
RPN - Risk Priority Number
RSM - Response Surface Methodology
RTY - Rolled Throughput Yield
SOP - Standard Operating Procedure
SOV - Source of Variation
SPC - Statistical Process Control
SQC - Statistical Quality Control
T - Target
TCS - Total Customer Satisfaction
TOP - Total Opportunities
TQL - Total Quality Leadership
TQM - Total Quality Management
UCL - Upper Control Limits
USL - Upper Specification Limits
WIP - Work in Process
XLS - Excel Spreadsheet
Zlt - Z-long term
ZST - Z-short term

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S = Summation; i.e., 1 + 2 + 3 + 4 + 5 = 15
! = Factorial; i.e. 5! = 5 x 4 x 3 x 2 x 1 = 120
e = Natural constant = 2.7183
g = Total number of subgroups.
i = The ith element in a string of 1, 2, 3, 4, -- i
j = The jth element in a string of 1, 2, 3, 4, -- j
n = Subgroup size (for high volume production, the range for n would
normally be between 3 and 10.
R = Range = difference (subtraction) between the maximum and minimum
measurements observed/recorded for a subgroup
R = Average of subgroup ranges = R; g
S = Standard deviation = s
X = A variable measurement made on an individual characteristic and on an
individual unit (often a process output variable) recorded onto a data log
or control chart.
Note: X is also used in another sense to denote the variables that
cause process variation.
X = Average of the X observations associated with a subgroup of size n
X = Average of observations over all subgroups = X ng
s LT = Standard deviation of the total population over a long period of time.
/j = 1
g
X /n
i = 1
i = 1j = 1
i i /

 n X i
 

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sLT = Estimate of long-term standard deviation =
s = Standard deviation of an individual subgroup =
sST = Standard deviation of a population over a short period of time
sST = Estimate for short-term standard deviation sST ; sST =
sW = Pooled standard deviation =
s2 = Variance
u = Process average or mean = X
u = Subgroup average or mean = X
Y = A process output variable - may likely be a CTQ
YRT = Rolled thruput yield
Cp = Short term process capability assuming no shift. Cp = 3 X ZST
Cpk = Short term process capability including mean shift occurring in the
process.
Z ST = Number of short-term standard deviations (sST) that fit between the
specification center and the specification limit (in either direction)
j
<
<
<
i
n
i = 1
(X - X)2
i
N - 1
<
<
(X - X)2
i j
g
j = 1 i = 1
n g - 1
<
<
(X - X )2
i j
g
j = 1 i = 1
g (n - 1)
n
<
s1 +s2 –+s1


=
sST
2 2 2

 

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Z LT = Number of long-term deviations (sLT) that fit between the observed
process average (X) and the closest specification limit.
Z LT =
2 = CHI square distribution - Used for hypothesis testing as follows:
• Test for independence (used to test for independent relationship
between two discrete variables)
• Goodness of fit (used to determine if the data fits an assured
distribution)
• Establishing the confidence interval for standard deviation
F = F distribution - associated with hypothesis testing of standard
deviation between two or more process distributions.
T = T distribution - associated with hypothesis testing of the means
(averages) between two distributions (when sample sizes are less than
100).
X
(1SL -X)
s LT

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ABSCISSA The horizontal axis of a graph.
ACCEPTANCE REGION The region of values for which the null hypothesis is
accepted.
ALPHA RISK The probability of accepting the alternate hypothesis
when, in reality, the null hypothesis is true.
ALTERNATE HYPOTHESIS A tentative explanation which indicates that an event
does not follow a chance distribution; a contrast to the
null hypothesis.
ANALYSIS OF VARIANCE A statistical method for evaluating the effect that factors
(ANOVA) have on process mean and for evaluating the differences
between the means of two or more normal distributions.
ASSIGNABLE CAUSE A process input variable that can be identified and that
contributes in an observable manner to non-random shifts
in process mean and /or standard deviation.
ASSIGNABLE VARIATIONS Variations in data which can be attributed to specific
causes.
ATTRIBUTE DATA Quality data that typically reflects the number of
conforming or non-conforming units or the number of non-
conformities per unit on a go/no go or accept/ reject
basis.
AVERAGE Sum of all measurements divided by the total number of
measurements. Statistic which is used to estimate the
population mean. Same as MEAN.

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BACKGROUND VARIABLES Variables which are of no experimental interest and are
not held constant. Their effects are often assumed
insignificant or negligible, or they are randomized to
ensure that contamination of the primary response does
not occur. Also referred to as environmental variables
and uncontrolled variables.
BENCHMARKING A process for identification of external best-in-class
practices and standards for comparison against internal
practices.
BETA RISK The probability of accepting the null hypothesis when, in
reality, the alternate hypothesis is true.
BINOMIAL DISTRIBUTION A statistical distribution associated with data that is one of
two possible states such as Go-No Go or Pass-Fail. It is
also the distribution generated by rolling dice.
BLACK BELT A process improvement project team leader who is
trained and certified in Six Sigma methodology and tools
and who is responsible for successful project execution.
BLOCKING VARIABLES A relatively homogenous set of conditions within which
different conditions of the primary variables are
compared. Used to ensure that background variables do
not contaminate the evaluation of primary variables.
BRAINSTORMING A team-oriented meeting used in problem solving to
develop a list of possible causes that may be linked to an
observed effect.
CAPABILITY INDICES A mathematical calculation used to compare the process
variation to a specification. Examples are Cp, Cpk, Pp,
PpK, Zst, and Zlt. GE uses Zst & Zlt as the common
communication language on process capability.
CAUSALITY The principle that every change implies the operation of a
cause.
CAUSATIVE Effective as a cause.
CAUSE That which produces an effect or brings about a change.

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CAUSE AND EFFECT (C&E) One of the seven basic tools for problem solving and is
DIAGRAM sometimes referred to as a “fishbone”
diagram because of its structure. Spine represents the “effect” and the
major legs of the structure are the “cause categories.”
The substructure represents the list of potential causes
which can induce the “effect.” The 6M’s (man, machine,
material, method, measurements and mother nature, are
sometimes used as cause categories.
C CHARTS Charts which display the number of defects per sample.
Used where sample size is constant.
CENTER LINE The line on a statistical process control chart which
represents the characteristic’s central tendency.
CENTRAL TENDENCY Numerical average, e.g., mean, median, and mode;
center line on a statistical process control chart.
CHAMPION An executive level business leader who facilitates the
leadership, implementation, and deployment of Six Sigma
philosophies.
CHANGE ACCELERATION A process which helps accelerate stakeholder buy-in and
PROGRAM PROGRAM (CAP) implementation of new philosophies and processes within
a business.
CHARACTERISTIC A definable or measurable feature of a process, product,
or service.
CHI-SQUARE See x (symbol glossary).
CLASSIFICATION Differentiation of variables.
COMMON CAUSE See RANDOM CAUSE.
CONFIDENCE LEVEL The probability that a randomly distributed variable “x” lies
within a defined interval of a normal curve.
CONFIDENCE LIMITS The two values that define the confidence interval.
CONFOUNDING Allowing two or more variables to vary together so that it
is impossible to separate their unique effects.

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CONSUMERS RISK Probability of accepting a lot when, in fact, the lot should
have been rejected (see BETA RISK).
CONTINUOUS DATA Data obtained from a measurement system which has an
infinite number of possible outcomes.
CONTINUOUS RANDOM A random variable which can assume any value
VARIABLE continuously within some specified
interval.
CONTROL CHART A graphical rendition of a characteristic’s performance
across time in relation to its natural limits and central
tendency.
CONTOL LIMITS Apply to both range or standard deviation and subgroup
average (X) portions of process control charts and are
used to determine the state of statistical control. Control
limits are derived statistically and are not related to
engineering specification limits in any way.
CONTROL PLAN A formal quality document that describes all of the
elements required to control variations in a particular
process or could apply to a complete product or family of
products.
CONTROL SPECIFICATIONS Specification requirements for the product being
manufactured.
CORRELATION The relationship between two sets of data such that when
one changes, the other is likely to make a corresponding
change. Also, a statistical tool for determining the
relationship between two sets of data.
COST OF POOR QUALITY Cost associated with providing poor quality products or
(COPQ) services. Can be divided into four cost
categories: Appraisal, Scrap, Rework, and Field Complaint
(warranty costs).
CRITICAL TO QUALITY (CTQ) A drawing characteristic determined to be important for
CHARACTERISTIC variability reduction based
on a requirement from production, engineering, customer application, or
regulatory agency. Can also apply to transactional or
service delivery processes.

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CUTOFF POINT The point which partitions the acceptance region from the
reject region.
DATA Factual information used as a basis for reasoning,
discussion, or calculation; often refers to quantitative
information.
DATA TRANSFORMATION A mathematical technique used to create a near normally
distributed data set out of a non-normal (skewed) data
set.
DEFECT Any product characteristic that deviates outside of
specification limits.
DEFECT PER MILLION Quality metric used in the Six Sigma process and is
OPPORTUNITIES (DPMO) calculated by the number of defects observed divided by
the number of opportunities for defects normalized to 1
million units.
DEGREES OF FREEDOM The number of independent measurements available for
estimating a population parameter.
DENSITY FUNCTION The function which yields the probability that a particular
random variable takes on any one of its possible values.
DEPENDENT VARIABLE A Response Variable; e.g., y is the dependent or
“Response” variable where Y = f(X1. . .XN) process input
variables.
DESIGN OF EXPERIMENT A formal, proactive method for documenting the selected
(DOE) controllable factors and their levels, as
well as establishing blocks, replications and response
variables associated with a planned experiment. It is the plan
for conducting the experiment and evaluating the results.
DISCRETE DATA Data obtained from a measurement system which has a
finite number of possible outcomes.
DISCRETE RANDOM VARIABLE A random variable which can assume values only from a
definite number of discrete values.

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DISTRIBUTIONS Tendency of large numbers of observations to group
themselves around some central value with a certain
amount of variation or “scatter” on either side.
EFFECT That which was produced by a cause.
EVOLUTIONARY OPERATIONS A DOE process used to optimize the key process input
(EVOPS) variables in a production environment, is usually
limited to 2-3 variables, is performed over a long period of
time, and is non-disruptive to the process.
EXCEL Spreadsheet package within Microsoft Office used for
data manipulation & analysis.
EXPERIMENT A test under defined conditions to determine an unknown
effect, to illustrate or verify a known law, or to establish
a hypothesis. See DESIGN OF EXPERIMENT (DOE).
EXPERIMENTAL ERROR Variation in observations made under identical test
conditions. Also called residual error. The amount of
variation which cannot be attributed to the variables
included in the experiment.
EXPONENTIALLY WEIGHTED A control charting method where the most current data
MOVING AVERAGE (EWMA) point is
weighted on an exponential basis such that older data
points carry less value in calculating average. This
charting technique is used to detect small shifts in process
average.
FACTORS Independent variables.
FAILURE MODE & EFFECTS Analytical technique focused at problem prevention thru
ANALYSIS (FMEA) identification of potential problems.
The FMEA is a proactive tool that is used pragmatically to
identify potential failure modes and their effects, to
numerically rate the combined risk associated with
severity, probability of occurrence and delectability and to
document appropriate plans for prevention. FMEA’s
can be applied to system, (application) and
product design and to manufacturing and non-
manufacturing processes (i.e., services &
transactional processes).

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FIRST TIME YIELD Yield that occurs in any process step prior to any rework
that may be required (see Yft Symbology) to overcome
process shortcomings.
FIXED EFFECTS MODEL An experimental model where treatments are specifically
selected by the researcher. Conclusions only apply to the
factor levels considered in the analysis. Inferences are
restricted to the experimental levels.
FLUCTUATIONS Variances in data which are caused by a large number of
minute variations or differences.
FREQUENCY DISTRIBUTION The pattern or shape formed by the group of
measurements in a distribution based on frequency of
occurrence.
GAGE ACCURACY The average difference observed between a gage under
evaluation and a master gage when measuring the same
parts over multiple readings.
GAGE LINEARITY A measure of gage accuracy variation when evaluated
over the expected operating range.
GAGE REPEATABILITY A measure of the variation observed when a single
operator uses a gage to measure a group of randomly
ordered (but identifiable) parts on a repetitive basis.
GAGE REPRODUCIBILITY A measure of average variation observed between
operations when multiple operators use the same gage to
measure a group of randomly ordered (but identifiable)
parts on a repetitive basis.
GAGE STABILITY A measure of variation observed when a gage is used to
measure the same master over an extended period of
time.
GREEN BELT Six Sigma role similar in function to Black Belt but length
of training and project scope are reduced.
HISTOGRAM Vertical display of a population distribution in terms of
frequencies; a formal method of plotting a frequency
distribution.

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HOMOGENEITY OF VARIANCE The variances of the data groups being contrasted are
equal (as defined by a statistical test of significant
difference).
HYPOTHESIS When used as a statistical term, it is a theory proposed
or postulated for comparing means and standard
deviations of two or more data sets. A “null” hypothesis
states that the data sets are from the same statistical
population, while the “alternate” hypothesis states that the
data sets are not from the same statistical population.
INDEPENDENT VARIABLE A controlled variable; a variable whose value is
independent of the value of another variable.
INSTABILITY Unnaturally large fluctuations in a process input or output
characteristic.
INTERACTION The tendency of two or more variables to produce an
effect in combination which neither variable would
produce if acting alone.
INTERVAL Numeric categories with equal units of measure but no
absolute zero point, i.e., quality scale or index.
KEY NOISE PARAMETERS Variables which are Hard or Expensive to control.
KEY PROCESS INPUT The vital few input variables, called “x’s”, (normally 2-6)
VARIABLES (KPIV’S) that drive 80% of the observed variations in the process
output characteristic (“y”). a.k.a Key Control Parameters
LINE CHARTS Charts used to track the performance without relationship
to process capability or control limits.
LOWER CONTROL LIMIT A horizontal dotted line plotted on a control chart which
represents the lowest process deviation that should occur
if the process is in control (free from assignable cause
variation).
MASTER BLACK BELT A person who is “expert” on Six Sigma techniques and on
project implementation. Master Black Belts play a major
role in training, coaching and in removing barriers to
successful project execution in addition to overall
promotion of the Six Sigma philosophy.

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MEAN See AVERAGE.
MEAN TIME BETWEEN Average time to failure for a statistically significant
FAILURES (MTBF) population of product operating in its normal environment.
MEASUREMENT SYSTEMS Means of evaluating a continuous or discrete
ANALYSIS (MSA) measurement system to quantify the amount of variation
contributed by the measurement system. Refer to
Automotive Std. (AIAG STD) for details.
MEDIAN The mid value in a group of measurements when ordered
from low to high.
MINITAB Statistical software package that operates on Microsoft
Windows with a spreadsheet format and has powerful
statistical analysis ability.
MISTAKE PROOFING Mistake proofing is a proactive technique used to
positively prevent errors from occurring.
MIXED EFFECTS MODEL Contains elements of both the fixed and random effects
models.
MULTI-VARI Method used in the measure/analyze phase of Six Sigma
to display in graphical terms the variation within parts,
machines, or processes between machines or process
parts, and over time.
NONCONFORMING UNIT A unit which does not conform to one or more
specifications, standards, and/or requirements.
NONCONFORMITY A condition within a unit which does not conform to some
specific specification, standard, and/or requirement; often
referred to as a defect; any given nonconforming unit can
have the potential for more than one nonconformity.
NORMAL DISTRIBUTION A continuous, symmetrical density function characterized
by a bell-shaped curve, e.g., distribution of sampling
averages.

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NORMALIZED ROLLED The estimate of the average process yield used to
THROUGHPUT YIELD (RYTN) determine RTY. It is determined by taking the nth root of
the RTY (where “n” is the # process step) included in the
RTY calculation.
NULL HYPOTHESIS An assertion to be proven by statistical analysis where
two or more data sets are stated to be from the same
population.
ONE-SIDED ALTERNATIVE The value of a parameter which has an upper bound or a
lower bound, but not both.
ORDINAL Ordered categories (ranking) with no information about
distance between each category, i.e., rank ordering of
several measurements of an output parameter.
ORDINATE The vertical axis of a graph.
OUT OF CONTROL Condition which applies to statistical process control chart
where plot points fall outside of the control limits or fail an
established run or trend criteria, all of which indicate that
an assignable cause is present in the process.
PARAMETER A constant defining a particular property of the density
function of a variable.
PARETO DIAGRAM A chart which places common occurrences in rank order.
P CHARTS Charts used to plot percent defectives in a sample where
sample size is variable.
PERTURBATION A nonrandom disturbance.
POISSON DISTRIBUTION A statistical distribution associated with attribute data (the
number of non-continuities found in a unit) and can be
used to predict first pass yield.
POPULATION A group of similar items from which a sample is drawn.
Often referred to as the universe.
POPULATION The entire set of items from which a sample is drawn.

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POWER OF AN EXPERIMENT The probability of rejecting the null hypothesis when it is
false and accepting the alternate hypothesis when it is
true.
PRECISION TO TOLERANCE A ratio used to express the portion of engineering
RATIO (P/T) specification consumed by the 99% confidence interval of
measurement system repeatability and reproducibility
error. (5.15 standard deviations of R&R error)
PREVENTION The practice of eliminating unwanted variation before the
fact, e.g., predicting a future condition from a control
chart and then applying corrective action before the
predicted event transpires.
PRIMARY CONTROL The major independent variables used in the experiment.
VARIABLES
PROBABILITY The chance of an event happening or condition occurring
by pure chance and is stated in numerical form.
PROBABILITY OF AN EVENT The number of successful events divided by the total
number of trials.
PROBLEM A deviation from a specified standard.
PROBLEM SOLVING The process of solving problems; the isolation and control
of those conditions which generate or facilitate the
creation of undesirable symptoms.
PROCESS A particular method of doing something, generally
involving a number of steps or operations.
PROCESS AVERAGE The central tendency of a given process characteristic
across a given amount of time or at a specific point in
time.
PROCESS CONTROL See STATISTICAL PROCESS CONTROL.
PROCESS CONTROL CHART Any of a number of various types of graphs upon which
data are plotted against specific control limits.

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PROCESS MAP A detailed step-by-step pictorial sequence of a process
showing process inputs, potential or actual controllable
and uncontrollable sources of variation, process outputs,
cycle time, rework operations, and inspection points.
PROCESS SPREAD The range of values which a given process characteristic
displays; this particular term most often applies to the
range but may also encompass the variance. The spread
may be based on a set of data collected at a specific
point in time or may reflect the variability across a given
period of time.
PRODUCERS RISK Probability of rejecting a lot when, in fact, the lot should
have been accepted (see ALPHA RISK).
PROJECT A problem, usually calling for planned action.
QUALITY FUNCTION QFD is a disciplined matrix methodology used for
DEPLOYMENT (QFD) documenting customer wants and needs – “the voice of
the customer” – into operational “requirement” terms. It is
an effective tool for determining critical-to-quality
characteristics for transactional processes, services and
products.
R CHART Plot of the difference between the highest and lowest in a
sample. Normally associated with the range control
portion of an X, R chart.
RANDOM CAUSE A source of variation which is random, usually associated
with the “trivial many” process input variables, and which
will not produce a highly predictable change in the
process output response (dependent variable), e.g., a
correlation does not exist; any individual source of
variation results in a small amount of variation in the
response; cannot be economically eliminated from a
process; an inherent natural source of variation.
RANDOMNESS A condition in which any individual event in a set of
events has the same mathematical probability of
occurrence as all other events within the specified set,
i.e., individual events are not predictable even though
they may collectively belong to a definable distribution.

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RANDOM SAMPLE One or more samples randomly selected from the
universe (population).
RANDOM SAMPLE Selecting a sample such that each item in the population
has an equal chance of being selected; lack of
predictability; without pattern.
RANDOM VARIABLE A variable which can assume any value from a
distribution which represents a set of possible values.
RANDOM VARIATIONS Variations in data which result from causes which cannot
be pinpointed or controlled.
RANGE The difference between the highest and lowest values in
a “subgroup” sample.
RANK Values assigned to items in a sample to determine their
relative occurrence in a population.
RATIONAL SUBGROUP A subgroup is usually made up of consecutive pieces
chosen from the process stream so that the variation
represented within each subgroup is as small as feasible.
Any changes, shifts and drifts in the process will appear
as differences between the subgroups, selected over
time.
REGRESSION A statistical technique for determining the best
mathematical expression that describes the functional
relationship between one response and one or more
independent variables.
REJECT REGION The region of values for which the alternate hypothesis is
accepted.
REPLICATION Repeat observations made under identical test
conditions.
REPRESENTATIVE SAMPLE A sample which accurately reflects a specific condition or
set of conditions within the universe.

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RESEARCH Critical and exhaustive investigation or experimentation
having for its aim the revision of accepted conclusions in
the light of newly discovered facts.
RESIDUAL ERROR See EXPERIMENTAL ERROR.
RESPONSE SURFACE A graphical (pictorial) analysis technique used in
METHODOLOGY (RSM) conjunction with DOE for
determining optimum process parameter settings.
ROBUST The condition or state in which a response parameter
exhibits a high degree of resistance to external causes of
a nonrandom nature; i.e., impervious to perturbing
influence.
ROLLED THROUGHPUT YIELD The product (series multiplication) of all of the individual
(RTY) first pass yields of each step of the total process.
ROOT SUM SQUARED (RSS) Square root of the sum of the squares. Means of
combining standard deviations from independent causes.
SAMPLE A portion of a population of data chosen to estimate some
characteristic about the whole population. One or more
observations drawn from a larger collection of
observations or universe (population).
SCATTER DIAGRAMS Charts which allow the study of correlation, e.g., the
relationship between two variables or data sets.
SHORT RUN STATISTICAL A statistical control charting technique which applies to
PROCESS CONTROL any process situation where there
is insufficient frequency of subgroup data to use traditional
control charts (typically associated with low-volume
manufacturing or where setups occur frequently). Multiple
part numbers and multiple process streams can be plotted
on a single chart.
SIX M’S The major categories that contribute to effects on the
fishbone diagram (man, machine, material, method,
measurement, and mother nature.

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SIX SIGMA A term coined by Motorola to express process capability
in parts per million. A Six Sigma process generates a
maximum defect probability of 3.4 parts per million
(PPM) when the amount of process shifts and drifts are
controlled over the long term to less than +1.5
standard deviations.
SKEWED DISTRIBUTION A non-symmetrical distribution having a tail in either a
positive or negative direction.
SPECIAL CAUSE See ASSIGNABLE CAUSE.
STABLE PROCESS A process which is free of assignable causes, e.g., in
statistical control.
STANDARD DEVIATION A statistical index of variability which describes the
process spread or width of distribution.
STATISTICAL CONTROL A quantitative condition which describes a process that is
free of assignable/special causes of variation (both
mean and standard deviation). Such a condition is most
often evidenced on a control chart, i.e., a control chart
which displays an absence of nonrandom variation.
STATISTICAL PROCESS The application of standardized statistical methods and
CONTROL (SPC) procedures to a process for control
purposes.
SUBGROUP A logical grouping of objects or events which displays
only random event-to-event variations, e.g., the
objects or events are grouped to create homogenous
groups free of assignable or special causes. By virtue of
minimizing within subgroup variability, any change in the
central tendency or variance of the universe will be
reflected in the “subgroup-to-subgroup” variability.
A predetermined sample of consecutive parts or other
data bearing objects removed from the process
for the purpose of data collection.
SYMPTOM That which serves as evidence of something not fully
understood in factual terms.

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SYSTEM That which is connected according to a scheme.
SYSTEMATIC VARIABLES A pattern which displays predictable tendencies.
TEST OF SIGNIFICANCE A statistical procedure used to determine whether or not a
process observation (data set) differs from a postulated
value by an amount greater than that due to random
variation alone.
THEORY A plausible or scientifically acceptable general principle
offered to explain phenomena.
TWO-SIDED ALTERNATIVE The values of a parameter which designate both an upper
and lower bound.
TYPE I ERROR See ALPHA RISK.
TYPE II ERROR See BETA RISK.
UNNATURAL PATTERN Any pattern in which a significant number of the
measurements do not group themselves around a central
tendency. When the pattern is unnatural, it means that
non-random disturbances are present and are affecting
the process.
UPPER CONTROL LIMIT A horizontal line on a control chart (usually dotted) which
represents the upper limits of capability for a process
operating with only random variation.
VARIABLE A characteristic that may take on different values.
VARIABLES DATA Data collected from a process input or output where the
measurement scale has a significant level of subdivisions
or resolution, e.g., ohms, voltage, diameter, etc.
VARIATION Any quantifiable difference between individual
measurements; such differences can be classified as
being due to common causes (random) or special causes
(assignable).
VARIATION RESEARCH Procedures, techniques, and methods used to isolate one
type of variation from another (for example, separating
product variation from test variation).
X & R CHARTS A control chart which is a representation of process
capability over time; displays the variability in the process
average and range across time.

Clt Six Sigma Glossary

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