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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
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.
A dispersion relation relates the wavelength or wavenumber of a wave to its frequency. Given the dispersion relation, one can calculate the frequency-dependent phase velocity and group velocity of each sinusoidal component of a wave in the medium, as a function of frequency
In statistics, overdispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on a given statistical model. A common task in applied statistics is choosing a parametric model to fit a given set of empirical observations. This necessitates an assessment of the fit of the chosen model.
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 .
To begin, we first need to introduce self-similar processes: For the sequence of numbers = (: =,,, …,) with mean ^ = (), deviations = ^, variance ^ = (), and autocorrelation function = (, +) with lag k, if the autocorrelation of this sequence has the long range behavior () as k →∞ and where L(k) is a slowly varying ...
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.