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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.
It shows the extent of variability in relation to the mean of the population. The coefficient of variation should be computed only for data measured on scales that have a meaningful zero ( ratio scale ) and hence allow relative comparison of two measurements (i.e., division of one measurement by the other).
Variance is a measure ... is therefore desirable in analysing the causes of variability to deal with the square of the standard deviation as the measure of ...
In statistics, the standard deviation is a measure of the amount of variation of the values of a variable about its mean. [1] A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range.
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it ...
The sum of squared deviations is a key component in the calculation of variance, another measure of the spread or dispersion of a data set. Variance is calculated by averaging the squared deviations. Deviation is a fundamental concept in understanding the distribution and variability of data points in statistical analysis. [1]
Leik's measure of dispersion (D) is one such index. [76] Let there be K categories and let p i be f i /N where f i is the number in the i th category and let the categories be arranged in ascending order. Let = = where a ≤ K. Let d a = c a if c a ≤ 0.5 and 1 − c a ≤ 0.5 otherwise. Then
In statistics, explained variation measures the proportion to which a mathematical model accounts for the variation of a given data set.Often, variation is quantified as variance; then, the more specific term explained variance can be used.