Sentence examples for deviation identification from inspiring English sources

Exact(1)

As gam-5 requires less information than gam-6, it can be seen as a good compromise between an unbiased fit and deviation identification.

Similar(59)

NA, Not available; Std dev, Standard deviation; ID, Identification L'Atalante had the overall lowest nematode densities compared to the other DHABs.

Articular evaluation is part of this assessment and allows joint deviation quantification, identification of orthopedic deformities, observation of painful events, and follow-up of progress made by patients.

SBAR, Situation, Background, Assessment, Plan; SOAP, Subjective, Objective, Assessment, Plan; MAN, medical admission note; SD, standard deviation; ID, identification; CC, chief complaint; HPI, history of present illness; PMH, past medical history; MHA, medications, habits and allergies; DOC, directives of care; PE, physical examination; A+P, assessment and plan.

Are the internal controls designed to ensure the validity and reliability of the accounting information system and prevent deviations in identification of accruals?

There was no significant difference according to the psychophysiological odor performance between the groups (CM-group: odor threshold mean 7.1 (standard deviation [SD] 2.3) odor identification mean 25.0 (SD 7.0) controls: odor threshold mean 6.7 (SD 2.9) odor identification mean 22.5 (SD 7.0)).

Contrary to prior literature which uses qualitative cause-effect based techniques, the present work incorporates quantitative aspects such as the time taken by measurements for deviation, to make alarm identification more reliable.

However, it reduces the standard deviation and facilitates the identification of statistically significant results.

This may be particular helpful to discriminate between tumor-related white matter tract deviation versus infiltration and identification of compensatory rewiring of functional centers affected by adjacent brain tumors.

These comparisons allow for the identification of deviations from the behavior expected of conventional blends and composites, highlighting the effects of nanostructuration and the presence of the organic modifier on materials behavior.

Identification of deviations from predicted or programed consumption profiles modeled by a Multichannel Structure are shown to be easily designed, tuned and implemented with low computation demand of memory and processing speed.

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