Error Propagation and Numerical Artifacts

An extension to two- resp. multidimensional filters is presented in Refs. 114, 167, 168. Matched filters are the most elegant solution, provided one knows what form the signal has. The choice between different filter types is discussed in Ref. 114. 

The Cusum technique (Section 1.8.5, program CUSUM) can also be regarded as a special type of filtering function, one that detects changes in the 

The flowsheet shown in the introduction and that used in connection with a simulation (Section 1.4) provide insights into the pervasiveness of errors at the source, random errors are experienced as an inherent feature of every measurement process. The standard deviation is commonly substituted for a more detailed description of the error distribution (see also Section 1.2), as this suffices in most cases. Systematic errors due to interference or faulty interpretation cannot be detected by statistical methods alone control experiments are necessary. One or more such primary results must usually be inserted into a more or less complex system of equations to obtain the final result (for examples, see Refs. 23, 91-94, 104, 105, 142. The question that imposes itself at this point is how reliable is the final result Two different mechanisms of action must be discussed  

The act of manipulating numbers on calculators of finite accuracy leads to numerical artifacts. (See Table l.l.) -  

The measurement uncertainty is transformed into a corresponding uncertainty of the final result due to algebraic distortions and weighting factors, even if the calculator s accuracy is irrelevant. 

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Of all the requirements that have to be fulfilled by a manufacturer, starting with responsibilities and reporting relationships, warehousing practices, service contract policies, airhandUng equipment, etc., only a few of those will be touched upon here that directly relate to the analytical laboratory. Key phrases are underlined or are in italics Acceptance Criteria, Accuracy, Baseline, Calibration, Concentration range. Control samples. Data Clean-Up, Deviation, Error propagation. Error recovery. Interference, Linearity, Noise, Numerical artifact. Precision, Recovery, Reliability, Repeatability, Reproducibility, Ruggedness, Selectivity, Specifications, System Suitability, Validation. 

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