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If data are placed in order, then the lower quartile is central to the lower half of the data and the upper quartile is central to the upper half of the data. These quartiles are used to calculate the interquartile range, which helps to describe the spread of the data, and determine whether or not any data points are outliers.
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
sample maximum (nominal: lowest hundredth percentile) Note that the middle five of the seven numbers can all be obtained by successive partitioning of the ordered data into subsets of equal size. Extending the seven-number summary by continued partitioning produces the nine-number summary , the eleven-number summary , and so on.
Consider a set of elements in which two of the elements satisfy the general case. This occurs when both element's respective weights border the midpoint of the set of weights without encapsulating it; Rather, each element defines a partition equal to /. These elements are referred to as the lower weighted median and upper weighted median.
Maximum (Q 4 or 100th percentile): the highest data point in the data set excluding any outliers Median ( Q 2 or 50th percentile) : the middle value in the data set First quartile ( Q 1 or 25th percentile) : also known as the lower quartile q n (0.25), it is the median of the lower half of the dataset.
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The 25th percentile is also known as the first quartile (Q 1), the 50th percentile as the median or second quartile (Q 2), and the 75th percentile as the third quartile (Q 3). For example, the 50th percentile (median) is the score below (or at or below , depending on the definition) which 50% of the scores in the distribution are found.
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