Then, find the “median” of the upper half of the data. To find the quartiles, begin by finding the median. If something is in the 60 th percentile, it means that 60% of the data is below it and 40% is above it. The second quartile is equal to the mean.Īdditionally, recall that a percentile is a sort of ranking of data. The first quartile is the 25 th percentile, the second quartile is the 50 th percentile, and the third quartile is the 75 th percentile. Recall that quartiles are one-fourth sections of the data. To find the interquartile range, find the difference between the third and first quartile of a data set. In particular, a data point is an outlier if it is more than the third quartile value plus $1.5\times$IQR or if it is less than the first quartile value minus $1.5\times$ IQR. The IQR helps to identify outliers because data points that lie outside these values are considered untypically large or small. In terms of percentiles, the interquartile range is the 75 th percentile minus the 25 th percentile. Note that the interquartile range is also known as the IQR, the midspread, or H-spread. This makes it easy to focus on the more typical data points without exceptional distractions. The interquartile range displays the middle section of the data, so it excludes upper and lower outliers. This data has an upper bound of the third quartile and a lower bound of the first quartile. The interquartile range of a set of data is the middle fifty percent of the data. This range tends to eliminate upper and lower outliers and focuses on the section where the majority of the data lies.Īll subjects that use statistics, including math, economics, business, and all branches of science, use the interquartile range.īefore moving forward with this section, make sure to review quartiles and box and whisker plots. The interquartile range of a data set is the middle fifty percent of the data set found between the first and third quartiles. Interquartile Range – Explanation and Examples
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