Editing Interquartile mean (section)

===Dataset size not divisible by four===
The above example consisted of 12 observations in the dataset, which made the determination of the quartiles very easy. Of course, not all datasets have a number of observations that is divisible by 4. We can adjust the method of calculating the IQM to accommodate this. So ideally we want to have the IQM equal to the [[mean]] for symmetric distributions, e.g.:

:1, 2, 3, 4, 5

has a mean value ''x''<sub>mean</sub> = 3, and since it is a symmetric distribution, ''x''<sub>IQM</sub> = 3 would be desired.

We can solve this by using a [[weighted average]] of the quartiles and the interquartile dataset:

Consider the following dataset of 9 observations:

:1, 3, 5, 7, 9, 11, 13, 15, 17

There are 9/4 = 2.25 observations in each quartile, and 4.5 observations in the interquartile range. Truncate the fractional quartile size, and remove this number from the 1st and 4th quartiles (2.25 observations in each quartile, thus the lowest 2 and the highest 2 are removed).

:<s>1, 3</s>, (5), 7, 9, 11, (13), <s>15, 17</s>

Thus, there are 3 ''full'' observations in the interquartile range with a weight of 1 for each full observation, and 2 fractional observations with each observation having a weight of 0.75 (1-0.25 = 0.75). Thus we have a total of 4.5 observations in the interquartile range, (3×1 + 2×0.75 = 4.5 observations).

The IQM is now calculated as follows:

:''x''<sub>IQM</sub> = {(7 + 9 + 11) + 0.75 &times; (5 + 13)} / 4.5 = 9

In the above example, the mean has a value x<sub>mean</sub> = 9. The same as the IQM, as was expected. The method of calculating the IQM for any number of observations is analogous; the fractional contributions to the IQM can be either 0, 0.25, 0.50, or 0.75.