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2 consecutive measurements exceed 2 standard deviations of the reference range, and on the same side of the mean. Inaccuracy and/or imprecision R 4s: Two measurements in the same run have a 4 standard deviation difference (such as one exceeding 2 standard deviations above the mean, and another exceeding 2 standard deviations below the mean).
In statistics and in particular statistical theory, unbiased estimation of a standard deviation is the calculation from a statistical sample of an estimated value of the standard deviation (a measure of statistical dispersion) of a population of values, in such a way that the expected value of the calculation equals the true value.
The mean and the standard deviation of a set of data are descriptive statistics usually reported together. In a certain sense, the standard deviation is a "natural" measure of statistical dispersion if the center of the data is measured about the mean. This is because the standard deviation from the mean is smaller than from any other point.
The "68–95–99.7 rule" is often used to quickly get a rough probability estimate of something, given its standard deviation, if the population is assumed to be normal. It is also used as a simple test for outliers if the population is assumed normal, and as a normality test if the population is potentially not normal.
In thermodynamics, an activity coefficient is a factor used to account for deviation of a mixture of chemical substances from ideal behaviour. [1] In an ideal mixture, the microscopic interactions between each pair of chemical species are the same (or macroscopically equivalent, the enthalpy change of solution and volume variation in mixing is zero) and, as a result, properties of the mixtures ...
σ g: Geometric standard deviation. This value is determined mathematically by the equation: σ g = D 84.13 /D 50 = D 50 /D 15.87. The value of σ g determines the slope of the least-squares regression curve. α: Relative standard deviation or degree of polydispersity. This value is also determined mathematically.
The normal distribution is NOT assumed nor required in the calculation of control limits. Thus making the IndX/mR chart a very robust tool. Thus making the IndX/mR chart a very robust tool. This is demonstrated by Wheeler using real-world data [ 4 ] , [ 5 ] and for a number of highly non-normal probability distributions.
Since the square root introduces bias, the terminology "uncorrected" and "corrected" is preferred for the standard deviation estimators: s n is the uncorrected sample standard deviation (i.e., without Bessel's correction) s is the corrected sample standard deviation (i.e., with Bessel's correction), which is less biased, but still biased