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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.
A simple flowchart representing a process for dealing with a non-functioning lamp.. A flowchart is a type of diagram that represents a workflow or process.A flowchart can also be defined as a diagrammatic representation of an algorithm, a step-by-step approach to solving a task.
In an automatic process, once the data is extracted or captured from the invoice the data is sent into the system for automatic matching against the purchase order. This matching process can compare just the invoice data with that shown on the purchase order or be expanded to include a deeper level that looks at the receiving documents.
Business Process Model and Notation (BPMN) is a standard for business process modeling that provides a graphical notation for specifying business processes in a Business Process Diagram (BPD), [3] based on a flowcharting technique very similar to activity diagrams from Unified Modeling Language (UML). [4]
The term false discovery rate (FDR) was used by Colquhoun (2014) [4] to mean the probability that a "significant" result was a false positive. Later Colquhoun (2017) [ 2 ] used the term false positive risk (FPR) for the same quantity, to avoid confusion with the term FDR as used by people who work on multiple comparisons .
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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]