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The mid-range is closely related to the range, a measure of statistical dispersion defined as the difference between maximum and minimum values. The two measures are complementary in sense that if one knows the mid-range and the range, one can find the sample maximum and minimum values.
In descriptive statistics, the range of a set of data is size of the narrowest interval which contains all the data. It is calculated as the difference between the largest and smallest values (also known as the sample maximum and minimum). [1] It is expressed in the same units as the data. The range provides an indication of statistical ...
For estimating the standard deviation of a normal distribution, the scaled interdecile range gives a reasonably efficient estimator, though instead taking the 7% trimmed range (the difference between the 7th and 93rd percentiles) and dividing by 3 (corresponding to 86% of the data of a normal distribution falling within 1.5 standard deviations ...
When used in this sense, range is defined as "a pair of begin/end iterators packed together". [1] It is argued [1] that "Ranges are a superior abstraction" (compared to iterators) for several reasons, including better safety. In particular, such ranges are supported in C++20, [2] Boost C++ Libraries [3] and the D standard library. [4]
L ∞ norm statistics: the mid-range minimizes the maximum absolute deviation trimmed L ∞ norm statistics: for example, the midhinge (average of first and third quartiles ) which minimizes the median absolute deviation of the whole distribution, also minimizes the maximum absolute deviation of the distribution after the top and bottom 25% ...
The sample range is the difference between the maximum and minimum. It is a function of the order statistics: {, …,} = (). A similar important statistic in exploratory data analysis that is simply related to the order statistics is the sample interquartile range.
Instead, the closest ancestor of the modern median is the mid-range, invented by Al-Biruni [62]: 31 [63] Transmission of his work to later scholars is unclear. He applied his technique to assaying currency metals, but, after he published his work, most assayers still adopted the most unfavorable value from their results, lest they appear to cheat .
One of the most common robust measures of scale is the interquartile range (IQR), the difference between the 75th percentile and the 25th percentile of a sample; this is the 25% trimmed range, an example of an L-estimator. Other trimmed ranges, such as the interdecile range (10% trimmed range) can also be used.