Relative standard deviation in the context of Frequency distribution

⭐ Core Definition: Relative standard deviation

In probability theory and statistics, the coefficient of variation (CV), also known as normalized root-mean-square deviation (NRMSD), percent RMS, and relative standard deviation (RSD), is a standardized measure of dispersion of a probability distribution or frequency distribution. It is defined as the ratio of the standard deviation $\sigma$ to the mean $\mu$ (or its absolute value, $|\mu |$ ), and often expressed as a percentage ("%RSD"). The CV or RSD is widely used in analytical chemistry to express the precision and repeatability of an assay. It is also commonly used in fields such as engineering or physics when doing quality assurance studies and ANOVA gauge R&R, by economists and investors in economic models, in epidemiology, and in psychology/neuroscience.

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Relative standard deviation in the context of Precision (statistics)

In statistics, the precision matrix or concentration matrix is the matrix inverse of the covariance matrix or dispersion matrix, $P=\Sigma ^{-1}$ .For univariate distributions, the precision matrix degenerates into a scalar precision, defined as the reciprocal of the variance, $p={\frac {1}{\sigma ^{2}}}$ .

Other summary statistics of statistical dispersion also called precision (or imprecision)include the reciprocal of the standard deviation, $p={\frac {1}{\sigma }}$ ; the standard deviation itself and the relative standard deviation;as well as the standard error and the confidence interval (or its half-width, the margin of error).

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