a Alpha, risk of rejecting the null hypothesis erroneously
AFR Average failure rate
ANOM Analysis of means
ANOVA Analysis of variance
AQL Acceptable quality level
ARL Average run length
b Beta, risk of not rejecting the null hypothesis erroneously
c chart Control chart for nonconformities
C&E Cause and effect (diagram)
CL Center line in an SPC chart
CDF Cumulative distribution function
Cp Capability index which shows the process capability potential but does not consider how centered the process is.
Cpk Capability index used to compare the natural tolerance of a process within the specification limits. Cpk has a value equal to Cp if the process is centered on the nominal; if Cpk is negative, the process mean is outside of the specification limits; if Cpk is between 0 and 1 then the natural tolerances of the process falls outside the spec limits.
COPQ Cost of poor quality
CTC Critical to cost
CTD Critical to delivery
CTP Critical to process
CTQ Critical to quality
CUSUM Control chart which plots the cumulative deviation of each subgroup’s average from the nominal value.
D chart Demerit chart
DF Degrees of freedom
DOE Design of experiments
DPM Defects per million
DPMO Defects per million opportunities
FMEA Failure mode and effects analysis
Gage R&R Gage repeatability and reproducibility
Ho Null hypothesis
Ha Alternative hypothesis
KCA Knowledge centered activity
KPIV Key process input variables
KPOV Key process output variables
LCL Lower control limit (in SPC)
m Mu, population true mean
Estimate of population mean
MCT WIP/EXIT
MCE Value Added Time/Manufacturing Cycle Time
MS Mean square
MSA Measurement Systems Analysis
MTBF Mean time between failures
n Sample size
np (chart) SPC chart of number of nonconforming items
NGT Nominal group technique
P probability
p (chart) Control chart of the proportion of defective units (or fraction defective) in a subgroup. Based on the binomial distribution
PCE Value Added Time/Process Cycle Time
PCT Process Cycle Time, the time for single or one batch unit through the process
P Value The probability of making a Type I error.
PDF Probability density function
ppm Parts per million (defect rate)
Pp Performance index (AIAG l995b) (calculated using "longterm" standard deviation)
Ppk Performance index (AIAG 1995b) (calculated using "longterm" standard deviation)
P/T Precision to tolerance ratio
QFD Quality function deployment
R Range (in SPC)
RSM Response surface methodology
RTY Rolled Throughput Yield
r Number of failures, correlation coefficient
R2 Coefficient of determination
r(t) System failure rate at time (t) for the NHPP model
s Sigma, population standard deviation
Estimate for population standard deviation
s Standard deviation of a sample
S Chart Sample standard deviation chart
SPC Statistical process control
SS Sum of squares
UCL Upper control limit (SPC)
u (chart) SPC chart of number of nonconformities per unit
Mean of a variable x
Median of variable x
chart SPC chart of means (i.e., xbar chart)
Xmr (chart) SPC chart of individual and moving range measurements
Z - Value (Xi - X-Bar)/ s

ACCEPTABLE QUALITY LEVEL: when a continuing series of lots is considered, a quality level that, for the purposes of sampling inspection, is the limit of a satisfactory process average.

ACCEPTANCE SAMPLING: inspection of a sample from a lot to decide whether to accept or not accept that lot. There are two types: attributes sampling and variables sampling. In attributes sampling, the presence or absence of a characteristic is noted in each of the units inspected. In variables sampling, the numerical magnitude of a characteristic is measured and recorded for each inspected unit; this involves reference to a continuous scale of some kind.

ACCEPTANCE SAMPLING PLAN: a specific plan that indicates the sampling sizes and the associated acceptance or non-acceptance criteria to be used. In attributes sampling, for example, there are single, double, multiple, sequential, chain, and skip-lot sampling plans. In variables sampling, there are single, double, and sequential sampling plans.

ALIASING: when two factors or interaction terms are set at identical levels throughout the entire experiment (i.e., the two columns are 100% correlated).

ALPHA RISK: See “Type I Error”.

ALTERNATIVE HYPOTHESIS: the hypothesis to be accepted if the null hypothesis is rejected. It is denoted by H1.

ANALYSIS OF MEANS (ANOM): a statistical procedure for troubleshooting industrial processes and analyzing the results of experimental designs with factors at fixed levels.

ANALYSIS OF VARIANCE (ANOVA): a basic statistical technique for analyzing experimental data. It subdivides the total variation of a data set into meaningful component parts associated with specific sources of variation in order to test a hypothesis on the parameters of the model or to estimate variance components. There are three models: fixed, random, and mixed.

ATTRIBUTE DATA: go/no-go information. The control charts based on attribute data include percent chart, number of affected units chart, count chart, count-per-unit chart, quality score chart, and demerit chart.

BALANCED DESIGN: a 2-level experimental design is balanced if each factor is run the same number of times at the high and low levels.

BENCHMARKING: an improvement process in which 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. The subjects that can be benchmarked include strategies, operations, processes, and procedures.


BETA RISK: see “Type II Error”.

BIAS: systematic error which leads to a difference between the average result of a population of measurements and the true accepted value of the quantity being measured.