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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.
When there are two independent causes of variability capable of producing in an otherwise uniform population distributions with standard deviations and , it is found that the distribution, when both causes act together, has a standard deviation +. It is therefore desirable in analysing the causes of variability to deal with the square of the ...
Genetic variability, a measure of the tendency of individual genotypes in a population to vary from one another; Heart rate variability, a physiological phenomenon where the time interval between heart beats varies; Human variability, the range of possible values for any measurable characteristic, physical or mental, of human beings
Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved (underlying) variables.
Climate variability is the term to describe variations in the mean state and other characteristics of climate (such as chances or possibility of extreme weather, etc.) "on all spatial and temporal scales beyond that of individual weather events." Some of the variability does not appear to be caused by known systems and occurs at seemingly ...
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences between groups. It uses F-test by comparing variance between groups and taking noise , or assumed normal distribution of group, into consideration by ...
The data set [90, 100, 110] has more variability. Its standard deviation is 10 and its average is 100, giving the coefficient of variation as 10 / 100 = 0.1; The data set [1, 5, 6, 8, 10, 40, 65, 88] has still more variability. Its standard deviation is 32.9 and its average is 27.9, giving a coefficient of variation of 32.9 / 27.9 = 1.18
In statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data.It can also refer to the population parameter that is estimated by the MAD calculated from a sample.