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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]
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]
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]
In NMR spectroscopy, e.g. of the nuclei 1 H, 13 C and 29 Si, frequencies depend on the magnetic field, which is not the same across all experiments. Therefore, frequencies are reported as relative differences to tetramethylsilane (TMS), an internal standard that George Tiers proposed in 1958 and that the International Union of Pure and Applied Chemistry has since endorsed.
Chemical accuracy is the accuracy required to make realistic chemical predictions and is generally considered to be 1 kcal/mol or 4 kJ/mol. To reach that accuracy in an economic way, it is necessary to use a series of post-Hartree–Fock methods and combine the results. These methods are called quantum chemistry composite methods. [56]
In science, and most specifically chemistry, the accepted value denotes a value of a substance accepted by almost all scientists and the experimental value denotes the value of a substance's properties found in a localized lab. [1]
A critical element in modern automated thermometric titrimetry is the ability to locate the endpoint with a high degree of reproducibility. It is clearly impractical and insufficient for modern demands of accuracy and precision to estimate the inflection by intersection of tangents.
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 ...