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Even though comparison-sorting n items requires Ω(n log n) operations, selection algorithms can compute the k th-smallest of n items with only Θ(n) operations. This includes the median, which is the n / 2 th order statistic (or for an even number of samples, the arithmetic mean of the two middle order statistics). [25]
Each quartile is a median [8] calculated as follows. Given an even 2n or odd 2n+1 number of values first quartile Q 1 = median of the n smallest values third quartile Q 3 = median of the n largest values [8] The second quartile Q 2 is the same as the ordinary median. [8]
This median is 2.5 since it is an even set. The lower weighted median is 2 with partition sums of 0.25 and 0.5, and the upper weighted median is 3 with partition sums of 0.5 and 0.25. These partitions each satisfy their respective special condition and the general condition.
The median of the first group is the lower or first quartile, and is equal to (0 + 1)/2 = 0.5. The median of the second group is the upper or third quartile, and is equal to (27 + 61)/2 = 44. The smallest and largest observations are 0 and 63. So the five-number summary would be 0, 0.5, 7.5, 44, 63.
For p = 0 the limiting values are 0 0 = 0 and a 0 = 1 for a ≠ 0, so the difference becomes simply equality, so the 0-norm counts the number of unequal points. For p = ∞ the largest number dominates, and thus the ∞-norm is the maximum difference.
The rank of the second quartile (same as the median) is 10×(2/4) = 5, which is an integer, while the number of values (10) is an even number, so the average of both the fifth and sixth values is taken—that is (8+10)/2 = 9, though any value from 8 through to 10 could be taken to be the median.
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If there are an even number of data points in the original ordered data set, split this data set exactly in half. The lower quartile value is the median of the lower half of the data. The upper quartile value is the median of the upper half of the data. This rule is employed by the TI-83 calculator boxplot and "1-Var Stats" functions.