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Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]
Precision tests of QED have been performed in low-energy atomic physics experiments, high-energy collider experiments, and condensed matter systems. The value of α is obtained in each of these experiments by fitting an experimental measurement to a theoretical expression (including higher-order radiative corrections) that includes α as a parameter.
A calibration curve plot showing limit of detection (LOD), limit of quantification (LOQ), dynamic range, and limit of linearity (LOL).. In analytical chemistry, a calibration curve, also known as a standard curve, is a general method for determining the concentration of a substance in an unknown sample by comparing the unknown to a set of standard samples of known concentration. [1]
If the instrument has a needle which points to a scale graduated in steps of 0.1 units, then depending on the design of the instrument it is usually possible to estimate tenths between the successive marks on the scale, so it should be possible to read off the result to an accuracy of about 0.01 units.
Standard addition involves adding known amounts of analyte to an unknown sample, a process known as spiking.By increasing the number of spikes, the analyst can extrapolate for the analyte concentration in the unknown that has not been spiked. [2]
If this cannot be eliminated, potentially by resetting the instrument immediately before the experiment then it needs to be allowed by subtracting its (possibly time-varying) value from the readings, and by taking it into account while assessing the accuracy of the measurement.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
Another reason the precision matrix may be useful is that if two dimensions and of a multivariate normal are conditionally independent, then the and elements of the precision matrix are . This means that precision matrices tend to be sparse when many of the dimensions are conditionally independent, which can lead to computational efficiencies ...
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