SPC Glossary

Accuracy of measurements refers to the closeness of agreement between observed values and a known reference standard. Any offset from the known standard is called bias.

A cause of process variation that isn’t random or inherent and that is attributable to some identifiable and controllable influence. A dulled cutting tool, for example, could be an assignable cause for a cutting process’s increased variation.

Qualitative data that can be counted for recording and analysis. Examples include: number of defects, number of errors in a document, number of rejected items in a sample, presence of paint flaws. Attribute data are analyzed using the p-, np-, c- and u-charts.

See mean.

Short for average run length, ARL is the interval between out-of-control events that can be expected. For example, is a common out-of-control event chosen to determine a process’ ARL. When an out-of-control event appears on a control chart, an analyst can examine the interval between that event and the previous out-of-control event. If the interval matches or exceeds the process’ ARL value: a) the process can probably be classified as still in-control, b) the violation can probably be attributed to typical process variation, and c) a search for an assignable cause can probably be considered unwarranted. The inverses of these statements are likely true if the interval between out-of-control events is smaller than the ARL value.

The offset of a measured value from the true population value.

A discrete probability distribution used for counting the number of successes and failures, or conforming and nonconforming units. This distribution underlies the p-chart and the np-chart.

A graphical display of data tat shows the median and upper and lower quartiles, along with extreme points and any outliers.

In the field of SPC as it is applied to manufacturing, a recognized strategy for identifying manufacturing problems and solutions.

A measure of the amount of variation inherent in a stable process. Capability can be determined using data from control charts and histograms and is often quantified using the C_p and C_pk indices. A process is said to be capable when all of its output is in-spec.

A quality-control tool used to analyze potential causes of problems in a product or process. It organizes potential problems into four groups: man, method, machine, and material. It is also called a fishbone diagram or an Ishikawa diagram, after its developer.

A control chart based on counting the number of defects per constant size subgroup. Also known as a Count of Nonconformities chart. The c-chart is based on the Poisson distribution.

The line on the control chart that represents the long-run expected, or average value, of the quality characteristic that corresponds to the in-control state which occurs when only chance causes are present.

An important statistical theorem that states that subgroup averages tend to be normally distributed even if the output as a whole is not. This allows control charts to be widely used for process control, even if the underlying process is not normally distributed.

In the field of SPC, a simple user-friendly form for collecting data over a period of time. Originally, it was a paper form but today it is often found integrated into SPC software.

See Correlation below.

Problems with the system itself that are always present, influencing all of the production until found and removed. These are “common” to all manufacturing or production output. Also called chance causes, system causes, or chronic problems. Compare common causes to special causes.

The ongoing improvement of products, services, or processes through incremental and breakthrough improvements.

A graphical mechanism for deciding whether the underlying process has changed based on sample data from the process. Control charts help determine which causes are “special” and thus should be investigated for possible correction. Control charts contain the plotted values of some statistical measure for a series of samples or subgroups, along with the upper and lower control limits for the process.

Numerical limits, often represented on control charts as horizontal lines, which indicate whether the process is statistically in control. There is typically an upper control limit (UCL) and a lower control limit (LCL). If the process is in control and only common causes are present, and nearly all of the sample points fall within the control limits.

A measure of the relationship between two variables. If both variables grow larger (or smaller) together, they have a positive correlation. If one variable becomes smaller as the other grows larger, they have a negative correlation. Correlation values range from -1 to 1, with -1 indicating a negative correlation and 1 indicating a positive correlation.

See attribute data.

A measure of the capability of a process to produce output within the specifications. The measurement is made without regard to the centering of the process.

A measure of the capability of the process to produce output within the specifications. The centering of the process is taken into consideration by looking at the minimum of the upper specification limit capability and the lower specification limit capability. C_pk = min (C_pu, C_pl).

A control chart designed to detect small process shifts by looking at the Cumulative SUMs of the deviations of successive samples from a target value.

A branch of applied statistics dealing with planning, conducting, analyzing, and interpreting controlled tests which are used to identify and evaluate the factors that control a value of a parameter of interest.

An occurrence of a defect type (see below) in a manufactured part. A part can have multiple defect types and each type can have multiple occurrences.

A part that is determined to be defective, without detailing what makes the part defective.

A type of defect that may be observed in a part; for example, scratched. Each defect type may have multiple occurrences.

A method of quality control that only inspects a process’s output. It is considered inferior to a prevention method, as it tends to result in more output needing to be scrapped or reworked. In contrast, a prevention method anticipates scrap and rework and makes process adjustments accordingly.

A mathematical model that relates the value of a variable with the probability of the occurrence of that value in the population.

An Exponentially Weighted Moving Average control chart that uses current and historical data to detect small changes in the process. Typically, the most recent data is given the most weight, and progressively smaller weights are given to older data.

A bar graph representing the frequency of different measurements in a set of data. The graph is divided into ranges, such as 1-5, 6-10, 11-15, 16-20, and 21-25. Each range is represented by a bar, the height of which indicates the number of measurements in the set of data fall within that range.

A procedure that is used on a sample from a population to investigate the applicability of an assertion (inference) to the entire population. Hypothesis testing can also be used to test assertions about multiple populations using multiple samples.

A process in which the quality characteristic being evaluated is in a state of statistical control. This means that the variation among the observed samples can all be attributed to common causes, and that no special causes are influencing the process.

A single unit or a single measurement of a quality characteristic, usually denoted as X. This measurement is analyzed using an individuals chart, CUSUM or EWMA chart.

A control chart for processes in which individual measurements of the process are plotted for analysis. Also called an I-chart or X-chart.

The degree of peakedness, or flatness, of a histogram’s distribution curve.

The average of the individual values in a subgroup.

The “middle” value in a group of values. If the number of values is even, by convention, the median is determined by averaging the two middle values.

A pattern on a control chart that indicates data is coming from different systems or processes. The pattern consists of 8 consecutive points that occupy both sides of the center line in Zone B or beyond but not Zone C.

A generally improper sampling technique that arises in practice when the output from several processes is first thoroughly mixed and then random samples are drawn from the mixture. This may increase the sample variability and make the control chart less sensitive to process changes. This action violates the fundamental rule of rational sampling.

The observation that occurs most frequently in a sample. The data can have no mode, be unimodal, bimodal, etc.

A measure used to help calculate the variance of a population based on differences in consecutive data. Two consecutive individual data values are compared and the absolute value of their difference is recorded on the moving range chart. The moving range chart is typically used with an Individuals (X) chart for single measurements.

A unit with one or more nonconformities or defects. Also called a reject or defective unit.

A defect or an occurrence of something that violates a requirement, such as a scratch or dent.

A continuous, symmetrical, bell-shaped frequency distribution for variables data that is the basis for control charts for variables, such as x-bar and individuals charts. For normally distributed values, 99.73% of the population lies within ± 3 standard deviations of the mean. According to the Central Limit Theorem, subgroup averages tend to be normally distributed even if the output as a whole is not.

A control chart that plots the number of defective units in a lot. Only used when the lot size is fixed. The np-chart is based on the binomial distribution.

Unusually large or small observations relative to the rest of the data.

An element often introduced into a process by a well-meaning operator or controller who considers any appreciable deviation from the target value as a special cause. In this case, the operator is incorrectly viewing common-cause variation as a fault in the process. Over control of a process can actually increase the variability of the process and is viewed as a form of tampering.

A problem-solving tool that involves ranking all potential problem areas or sources of variation according to their contribution to cost or total variation. Typically, 80% of the effects come from 20% of the possible causes, so efforts are best spent on these “vital few” causes, temporarily ignoring the “trivial many” causes.

The principle that 80% of the problems are due to 20% of the causes. Also known as the 80/20 rule.

A control chart that plots the proportion of nonconforming units per lot.

Percentiles divide the ordered data into 100 equal groups. The k^th percentile p_k is a value such that at least k% of the observations are at or below this value and (100-k)% of the observations are at or above this value.

A probability distribution used to count the number of occurrences of relatively rare events. The Poisson distribution is used in constructing the c-chart and the u-chart.

Precision of measurements refers to their long-run variation (s²). It is a measure of the closeness between several individual readings.

A method of quality control that proactively adjusts a process using SPC so that scrap and rework are prevented. It is considered superior to a detection model, which only inspects a process’s output.

A measure of the ability of a process to produce output that meets the process specifications.

Quartiles divide the ordered data into 4 equal groups. The second quartile (Q2) is the median of the data.

A subset of the population chosen such that each member of the population has an equal probability of being included in the sample.

The difference between the highest and lowest values in a subgroup. For example, if a subgroup contains the values 1, 2, 6, 4, and 5, the range is the difference between 6 and 1, which is 5.

A principle of sampling which states that the variation between subgroups or samples should be solely attributable to the common causes in the system rather than the sampling method. Rational subgroups are usually chosen so that the variation represented within each subgroup is as small as feasible for the process, so that any changes in the process, or special causes, appear as differences between subgroups. Rational subgroups are typically made up of consecutive pieces, although random samples are sometimes used.

A control chart based on the range (R) of a subgroup, typically used in conjunction with an x-bar chart.

A consecutive number of points consistently increasing or decreasing, or above or below the centerline. A run can be evidence of the existence of special causes of variation that should be investigated.

A simple graphic representation of a characteristic of a process which shows plotted values of some statistic gathered from the process. The graphic can be analyzed for trends or other unusual patterns.

A control chart based on the standard deviation, s, of a subgroup. The s-chart is typically used in conjunction with an x-bar chart.

A subset of data from a population that can be analyzed to make inferences about the entire population.

The probability distribution of a statistic. Common sampling distributions include t, chi-square (c²), and F.

A graphical technique used to visually analyze the relationship between two variables. Two sets of data are plotted on a graph, with the y-axis being used for one variable and the x-axis being used for the other.

Control chart interpretation rules that are designed to increase the responsiveness of a control chart to out-of-control conditions by looking for patterns of points that would rarely happen if the process has not changed.

Adaptations made to control charts to help determine meaningful control limits in situations when only a limited number of parts are produced or when a limited number of services are performed. Short-run techniques usually look at the deviation of a quality characteristic from a target value.

The Greek letter used to designate a standard deviation.

A high-performance, data-driven approach to analyzing the root causes of business problems and solving them. Six-sigma techniques were championed by Motorola.

The tendency of the data distribution to be non-symmetrical. Skewness can be positive or negative and may affect the validity of control charts and other statistical tests based on the normal distribution.

Statistical Process Control, a proven and comprehensive methodology for achieving and maintaining manufacturing quality.

Causes of variation which arise periodically in a somewhat unpredictable fashion. Also called assignable causes, local faults, or sporadic problems. Contrast to common causes. The presence of special causes indicates an out-of-control process.

The upper and lower limits within which a process’s output must fall in order for that output to be acceptable.

The amount of variability in a sample or population.

A process is considered stable if it is free from the influences of special causes. A stable process is said to be in control.

Deviation is the distance far process measurements deviate from the process mean. Standard deviation is a standardized distance that is calculated from process data.

A value calculated from, or based on, sample data which is used to make inferences about the population from which the sample came. Sample mean, median, range, variance, and standard deviation are commonly calculated statistics.

The condition describing a process from which all special causes of variation have been removed and only common causes remain.

A collection of problem solving tools useful in achieving process stability and improving capability through the reduction of variability. SPC includes using control charts to analyze a process to identify appropriate actions that can be taken to achieve and maintain a state of statistical control and to improve the capability of the process.

Another name commonly used to describe statistical process control techniques.

Stratification arises in practice when samples are collected by drawing from each of several processes, for example machines, filling heads or spindles. Stratified sampling can increase the variability of the sample data and make the resulting control chart less sensitive to changes in the process.

Another name for a sample from the population. Subgroups consist of individual measurements or readings and the number of measurements or readings is referred to as the subgroup size. A common subgroup size is 5, which means each subgroup consists of 5 measurements or readings.

An action taken to compensate for variation within the control limits of a stable system. Tampering increases rather than decreases variation.

The ideal or aimed-for measurement of a process. It is typically mid-way between the upper and lower specification limits but does not have to be.

Occurs when a true hypothesis about the population is incorrectly rejected. Also called false alarm. The probability of a Type I error occurring is designated by a.

Occurs when a false hypothesis about the population is incorrectly accepted. Also called lack of alarm. The probability of a Type II error occurring is designated by b.

A control chart that plots the number of non-conformities or defects per inspection unit. It is used when the lot size is not fixed.

Measurements, such as diameter or weight, that are taken from a measuring instrument, such as calipers or a scale. It contrasts with attribute data, which consists of defects that are observed by a human. An example of attribute data is scratches or dents.

The differences among individual results or output of a machine or process. Variation is classified in two ways: variation due to common causes and variation due to special causes.

A control chart used for process in which individual measurements of the process are plotted for analysis, as opposed to being grouped into subgroups and averaged. Also called an individuals chart or I-chart.

A control chart for variable data that plots the average of each subgroup.

Two control charts that are viewed together. Usually, the X-bar chart is positioned above the R chart. Both charts are for variable data. The X-bar chart plots the average of each subgroup. The R chart plots the range of each subgroup and is typically used when the subgroup size is 7 or less.

Same as Xbar-R but typically used when the subgroup size is 8 or more.

The outermost one-third of the area between the center line and the upper and lower limits on a control chart.

The center one-third of the area between the center line and the upper and lower limits on a control chart.

The innermost one-third of the area between the center line and the upper and lower limits on a control chart.