statistical control

statistical control

[stə′tis·tə·kəl kən′trōl]
(analytical chemistry)
In an analytical procedure, a state that exists when the means of a large number of individual values in the output of a measurement process tend to approach a limiting value known as the limiting mean.

statistical control

see CONTROL, STATISTICAL.
References in periodicals archive ?
To obtain a reliable (reliable probability equal to one) numerical estimates of the relative parameter dispersion, to develop, based on the use of interval statistical models, a statistical control card applicable to the entire set of parameters that are controlled during the manufacture of the enameled wire and to verify its applicability in the production conditions.
Within these limits, the process is within statistical control or stable.
In his training before and after combat, especially at the Harvard Business School's Statistical Control Program for military personnel, Bloch further learned how to apply the newest scientific breakthroughs in decision making.
Mody covers everything from good design and mistakes that can happen to software design, manufacturing leadership, effective quality management, sampling plans and statistical control charts.
Green criticized the original three analytical studies for their statistical methods, citing small sample size and "insufficient statistical control." Green's review was followed more than a decade later by three reviews of CML II.
He applied his knowledge of Shewhart statistical control charts to analyze global temperatures.
The text is primarily focused on an examination of the usage and impacts of combining the two widely known statistical methods of statistical design and statistical control, to solve any research problem with speed in a sustainable manner.
Analyzing the charts it may be concluded that the process is stable, therefore, it is in statistical control. There are no out of control signals (Nelson's patterns) noticed in the charts [11, 17].
In the meanwhile, the specific pattern recognition techniques, including neural network, support vector machines, statistical control chart analysis, and genetic algorithms have been used for structural damage assessment [3-8].
Clinical laboratories were early users, for example, of statistical control charts, such as X-bar (averages) control charts and p (proportion) charts for investigating preanalytic problems.

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