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In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. [1] Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered.
Dispersion (geology), a process whereby sodic soil disperses when exposed to water; Dispersion (materials science), the fraction of atoms of a material exposed to the surface; Dispersion polymerization, a polymerization process; Velocity dispersion, the statistical variation of velocities about the mean velocity for a group of astronomical objects
An advantage of variance as a measure of dispersion is that it is more amenable to algebraic manipulation than other measures of dispersion such as the expected absolute deviation; for example, the variance of a sum of uncorrelated random variables is equal to the sum of their variances. A disadvantage of the variance for practical applications ...
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.
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.
Dispersion is a process by which (in the case of solid dispersing in a liquid) agglomerated particles are separated from each other, and a new interface between the inner surface of the liquid dispersion medium and the surface of the dispersed particles is generated. This process is facilitated by molecular diffusion and convection. [4]
In mathematics and statistics, deviation serves as a measure to quantify the disparity between an observed value of a variable and another designated value, frequently the mean of that variable. Deviations with respect to the sample mean and the population mean (or " true value ") are called errors and residuals , respectively.
Mathematical definition [ edit ] A diffusion process is a Markov process with continuous sample paths for which the Kolmogorov forward equation is the Fokker–Planck equation .