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Here (partial_{text{i}}) represents the training error of sample (m_{i}).
The bigger the fitting error of sample data is, the smaller the weight value of the sample data.
Generally speaking, the standard error of sample means (and of maximum likelihood estimators in general) is inversely proportional to the square root of the sample size: this implies that to achieve the same between-animal COV when using MRGluc would require an increased sample size of 40% (i.e., 100 × 1/ 1-0.155 2) compared when using MRGlucMAX.
The error of sample reconstruction is minimized.
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The truncation error of sampling is also analyzed.
Absolute error of samples: varepsilon^{left( 0 right)} left( k right) = x^{left( 0 right)} left( k right) - hat{x}^{left( 0 right)} (k).
Generally, the measurement error would be smaller than the statistical error of sampling at a large sample size.
And two recent newsroom-wide initiatives calling for publication of the margin of error on sample-based surveys haven't yet produced any change.
Statistical analysis by VAMPIRE requires two distinct steps: (1) modeling of the error structure of sample groups and (2) significance testing with a priori-defined significance thresholds.
Among the various types of error that must be taken into account are errors of observation (which include instrumental errors, personal errors, systematic errors, and random errors), errors of sampling, and direct and indirect errors (in which one erroneous measurement is used in computing other measurements).
Recall that the standard error of a sample mean is inversely proportional to the square root of the sample size.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com