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Interquartile range (IQR) is defined as the difference between the 75th and 25th percentiles or Q 3 - Q 1. While the maximum and minimum also show the spread of the data, the upper and lower quartiles can provide more detailed information on the location of specific data points, the presence of outliers in the data, and the difference in spread ...
The 3-quantiles are called tertiles or terciles → T; The 4-quantiles are called quartiles → Q; the difference between upper and lower quartiles is also called the interquartile range, midspread or middle fifty → IQR = Q 3 − Q 1. The 5-quantiles are called quintiles or pentiles → QU; The 6-quantiles are called sextiles → S
It is defined as the difference between the 75th and 25th percentiles of the data. [2] [3] [4] To calculate the IQR, the data set is divided into quartiles, or four rank-ordered even parts via linear interpolation. [1] These quartiles are denoted by Q 1 (also called the lower quartile), Q 2 (the median), and Q 3 (also called the
Third quartile (Q 3 or 75th percentile): also known as the upper quartile q n (0.75), it is the median of the upper half of the dataset. [ 7 ] In addition to the minimum and maximum values used to construct a box-plot, another important element that can also be employed to obtain a box-plot is the interquartile range (IQR), as denoted below:
Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered. On the other hand, when the variance is small, the data in the set is clustered.
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 ...
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.
Quantile functions are used in both statistical applications and Monte Carlo methods. The quantile function is one way of prescribing a probability distribution, and it is an alternative to the probability density function (pdf) or probability mass function, the cumulative distribution function (cdf) and the characteristic function.