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Types of Dispersion

Average Absolute Deviation
Average Absolute Deviation

The average absolute deviation (or mean absolute deviation) of a data set is the average of the absolute deviations from a central point. It is a summary statistic of statistical dispersion or variability. In this general form, the central point can be the mean, median, mode, or the result of another measure of central tendency.

Distance Standard Deviation
Distance Standard Deviation

In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range.

Interquartile Range (IQR)
Interquartile Range (IQR)

An interquartile range is a measurement of dispersion about the mean. The lower the IQR, the more the data is bunched up around the mean. It's calculated by subtracting Q1 from Q3.

Mean Absolute Difference
Mean Absolute Difference

The mean absolute difference (univariate) is a measure of statistical dispersion equal to the average absolute difference of two independent values drawn from a probability distribution. A related statistic is the relative mean absolute difference, which is the mean absolute difference divided by the arithmetic mean, and equal to twice the Gini coefficient.

Median Absolute Deviation (MAD)
Median Absolute Deviation (MAD)

So the median absolute deviation for this data is 1. Uses. 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.

Range
Range

In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range.

Standard Deviation
Standard Deviation

Now the standard deviation of the second data set is just going to be the square root of its variance, which is just 2. So the second data set has 1/10 the standard deviation as this first data set. This is 10 roots of 2, this is just the root of 2.