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Data quality control is the process of controlling the usage of data for an application or a process. This process is performed both before and after a Data Quality Assurance (QA) process, which consists of discovery of data inconsistency and correction.
In some cases, the software's default settings may produce incorrect results; in others, user modifications to the settings could result in incorrect results. Sample data and results are presented by Wheeler for the explicit purpose of testing SPC software. [7] Performing such software validation is generally a good idea with any SPC software.
An example of a data-integrity mechanism is the parent-and-child relationship of related records. If a parent record owns one or more related child records all of the referential integrity processes are handled by the database itself, which automatically ensures the accuracy and integrity of the data so that no child record can exist without a parent (also called being orphaned) and that no ...
Data is compared to the expected outcomes to see any similarities and differences. The testing process is also evaluated to see if there were any changes from the original test created during the planning phase. If the data is placed in a chart it can make it easier to see any trends if the plan–do–check–act cycle is conducted multiple times.
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
Quality management ensures that an organization, product, or service consistently functions as intended. It has four main components: quality planning, quality assurance, quality control, and quality improvement. [1] Customers recognize that quality is an important attribute when choosing and purchasing products and services.
In statistical quality control, the CUSUM (or cumulative sum control chart) is a sequential analysis technique developed by E. S. Page of the University of Cambridge. It is typically used for monitoring change detection. [1] CUSUM was announced in Biometrika, in 1954, a few years after the publication of Wald's sequential probability ratio test ...
Statistical process control (SPC) or statistical quality control (SQC) is the application of statistical methods to monitor and control the quality of a production process. This helps to ensure that the process operates efficiently, producing more specification-conforming products with less waste scrap.