This set of data, available in comparable format from regional and global data centers, quantifies measurements that increase the confidence in predicting ecosystem responses across landscapes, and provides a basis for generalizations.
Thus, validation sub-studies using objective measures of physical activity within large epidemiological studies are desirable to quantify measurement error and later correct estimates of physical activity and health outcomes for measurement error.
To assess the contribution of these measurement biases to thickness and therefore volume estimation, an experiment was conducted to quantify measurement uncertainties.
Measured data is fit with a reference function that serves as the basis for the envelope used in uncertainty space to quantify measurement uncertainty.
wCV quantifies measurement error relative to the size of the (positive) kinetic parameters.
Additional components of variation, such as replicate measurements to quantify measurement error, can easily be added [ 29, 31, 52].
Absolute reliability indices, including the standard error of measurement, minimal detectable change, and Bland-Altman limits of agreement, were used to quantify measurement errors and determine systematic biases of the two test occasions.
To quantify measurement precision with relative errors, experimentalists often use the coefficient of variation (CV), which is calculated by dividing the standard deviation of peak area/height by the mean peak area/height [ 4– 6].
Such vibration of complex machines is mostly determined and quantified by measurements.
First, the PM associated with primary combustion sources (e.g. traffic) was quantified using measurements of nitrogen oxides (NO X ) as a tracer.
