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This example calculates the five-number summary for the following set of observations: 0, 0, 1, 2, 63, 61, 27, 13. These are the number of moons of each planet in the Solar System . It helps to put the observations in ascending order: 0, 0, 1, 2, 13, 27, 61, 63.
The 12-quantiles are called duo-deciles or dodeciles → DD; The 16-quantiles are called hexadeciles → H; The 20-quantiles are called ventiles, vigintiles, or demi-deciles → V; The 100-quantiles are called percentiles or centiles → P; The 1000-quantiles have been called permilles or milliles, but these are rare and largely obsolete [24]
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In descriptive statistics, a decile is any of the nine values that divide the sorted data into ten equal parts, so that each part represents 1/10 of the sample or population. [1] A decile is one possible form of a quantile ; others include the quartile and percentile . [ 2 ]
In statistics, a k-th percentile, also known as percentile score or centile, is a score (e.g., a data point) below which a given percentage k of arranged scores in its frequency distribution falls ("exclusive" definition) or a score at or below which a given percentage falls ("inclusive" definition); i.e. a score in the k-th percentile would be above approximately k% of all scores in its set.
The first quartile (Q 1) is defined as the 25th percentile where lowest 25% data is below this point. It is also known as the lower quartile. The second quartile (Q 2) is the median of a data set; thus 50% of the data lies below this point. The third quartile (Q 3) is the 75th percentile where
Percentile ranks are not on an equal-interval scale; that is, the difference between any two scores is not the same as between any other two scores whose difference in percentile ranks is the same. For example, 50 − 25 = 25 is not the same distance as 60 − 35 = 25 because of the bell-curve shape of the distribution. Some percentile ranks ...
For example, they require the median and 25% and 75% quartiles as in the example above or 5%, 95%, 2.5%, 97.5% levels for other applications such as assessing the statistical significance of an observation whose distribution is known; see the quantile entry.