Errors specific examples

A specific example of a causal model is the root cause tree described in Section 6.8.4 and Figure 6.8. This is a very elaborate model which includes several levels of detail for both equipment and human causes of incidents. The root causes tree is a generic causal model, and may require tailoring for application to specific plants and processes (e.g., in the offshore sector) where other error causes may need to be considered. 

Having chosen the test mixture and mobile diase composition, the chromatogram is run, usually at a fairly fast chart speed to reduce errors associated with the measurement of peak widths, etc.. Figure 4.10. The parameters calculated from the chromatogram are the retention volume and capacity factor of each component, the plate count for the unretained peak and at least one of the retained peaks, the peak asymmetry factor for each component, and the separation factor for at least one pair of solutes. The pressure drop for the column at the optimum test flow rate should also be noted. This data is then used to determine two types of performance criteria. These are kinetic parameters, which indicate how well the column is physically packed, and thermodynamic parameters, which indicate whether the column packing material meets the manufacturer s specifications. Examples of such thermodynamic parameters are whether the percentage oi bonded... 

These above mentioned errors would be discussed briefly here with specific examples. It is pertinent to mention here that errors outside the range of permissible errors in the analyses of pharmaceutical substances may cause serious problems because most of these substances are usually highly toxic, potent and used extensively in life-saving processes across the globe. 

Elaborate with specific examples the various sampling procedures and errors commonly encountered in a quality control laboratory. 

One approach for reducing the errors associated with the prediction of pA a values is to employ an isodesmic reaction. To illustrate with a specific example, it may be very hard to correctly predict the free energy change for the aqueous reaction... 

An error in an experimental measurement is defined as a deviation of an observed value from the true value. There are two types of errors, determinate and indeterminate. Determinate errors are those that can be controlled by the experimenter and are associated with malfunctioning equipment, improperly designed experiments, and variations in experimental conditions. These are sometimes called human errors because they can be corrected or at least partially alleviated by careful design and performance of the experiment. Indeterminate errors are those that are random and cannot be controlled by the experimenter. Specific examples of indeterminate errors are variations in radioactive counting and small differences in the successive measurements of glucose in a serum sample. 

As a rule, batches that are rejected or reworked are not suitable for inclusion in a retrospective validation study [7]. Indeed, a processing failure that is not fully explainable should be cause to rethink the application of retrospective validation. Nonconformance to specification that is attributable to a unique event-operator error, for example, may be justifiably disregarded. In such cases, the batch is not considered when the historical data are assembled. 

The validation results shown in this specific example might lead one to make a generalized rule that the optimal complexity of a model corresponds to the level at which the RMSEP is at a minimum. However, it is not always the case that RMSEP-versus-complexity graph shows such a distinct minimum, and therefore such a generalized rule can result in overfit models. Alternatively, it might be more appropriate to choose the model complexity at which an increase in complexity does not significantly decrease the prediction error (RMSEP). This choice can be based on rough visual inspection of the prediction error-versus-complexity plot, or from statistical tools such as the/-test.50,51... 

For a number of reasons, including the difficulties inherent in proving a direct causal relationship between fatigue and adverse medical events and the lack of systems for reporting various types of adverse events and errors, few studies to date have examined the specific contribution of sleep deprivation to actual medical errors. Several recent studies, in attempting to unravel this relationship, have employed a number of different methodologies to assess prevalence, type, and risk factors for medical errors. For example, in one recent study of anesthetic... 

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