Q1 is the median of the first half and Q3 is the median of the second half. The median itself is excluded from both halves: one half contains all values below the median, and the other contains all the values above it. In an odd-numbered data set, the median is the number in the middle of the list. Step 1: Order your values from low to high. This time we’ll use a data set with 11 values. Step 4: Calculate the interquartile range. Since each of these halves have an odd number of values, there is only one value in the middle of each half. With an even-numbered data set, the median is the mean of the two values in the middle, so you simply divide your data set into two halves. Step 2: Locate the median, and then separate the values below it from the values above it. We’ll walk through four steps using a sample data set with 10 values. To see how the exclusive method works by hand, we’ll use two examples: one with an even number of data points, and one with an odd number. The exclusive interquartile range may be more appropriate for large samples, while for small samples, the inclusive interquartile range may be more representative because it’s a narrower range. While there is little consensus on the best method for finding the interquartile range, the exclusive interquartile range is always larger than the inclusive interquartile range. It’s more common to use the exclusive method in this case.
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