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Measurements from gait analysis are affected by many sources of variability.
Such mathematical models often originate as intrinsically complex, high-dimensional systems with many degrees of freedom, and many sources of variability.
The transportation decision-making process about a road facility or a transport system, as a consequence of planning and design activities or operational analysis, often exposes planners and designers to many sources of variability and uncertainty [1].
The goal in designing an investigation is to reduce, insofar as possible, the many sources of variability to just two, random chance and the effects (if any) of the active agent.
There are many sources of variability.
These discrepant findings do not necessarily conflict with each other, as there are many sources of variability inherent in MRI investigations that may contribute to the reported differences.
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However, the technology possesses many sources of technical variability that are an obstacle to obtaining high quality data sets.
This value was to verify that changes in measured tumor SUV values were valid across a patient's scans as there are many sources of SUV variability.
These longitudinal within-subject studies are more sensitive than cross-sectional studies because they are less prone to error variance from the many sources of inter-subject variability.
Even prone to many sources of errors and variability, the semi-quantitative method (standardized uptake value SUV) is currently preferred to the absolute quantification of glucose metabolic rate, which requires dynamic imaging and measurement of the arterial input function, and thus considered to be too complex for a use in routine practice.
However, in real world applications there are many sources of uncertainty that introduce variability into the decision-making problem.
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