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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 many control applications, trying to write a mathematical model of the plant is considered a hard task, requiring efforts and time to the process and control engineers. This problem is overcome by data-driven methods, which fit a system model to the experimental data collected, choosing it in a specific models class. The control engineer can ...
Statistical process control is appropriate to support any repetitive process, and has been implemented in many settings where for example ISO 9000 quality management systems are used, including financial auditing and accounting, IT operations, health care processes, and clerical processes such as loan arrangement and administration, customer ...
The first edition of Juran's Quality Control Handbook was published in 1951. He also developed the "Juran's trilogy", an approach to cross-functional management that is composed of three managerial processes: quality planning, quality control, and quality improvement. These functions all play a vital role when evaluating quality.
Database testing mainly takes place at this layer and involves testing strategies such as quality control and quality assurance of the product databases. [2] Testing at these different layers is frequently used to maintain the consistency of database systems, most commonly seen in the following examples:
An introduction to MSA can be found in chapter 8 of Doug Montgomery's Quality Control book. [2] These tools and techniques are also described in the books by Donald Wheeler [3] and Kim Niles. [4] Advanced procedures for designing MSA studies can be found in Burdick et al. [5] Equipment: measuring instrument, calibration, fixturing.