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The Interquartile Range (IQR) in data analysis is a measure of statistical dispersion and is significant for a few reasons. Firstly, it represents the range of the central 50% of the data, providing a measure of where the bulk of the values lie. Secondly, it is less affected by outliers and extreme values as it only considers the middle 50% of the data. This makes it a more robust measure of spread than the range. Lastly, it can be used to identify outliers. Any data point that falls below Q1 - 1.5*IQR or above Q3 + 1.5*IQR is considered an outlier.
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The span between these hinges (Q3 - Q1) is known as the Interquartile range, which, as explained, represents the range of the central 50% of the data. There are two lines extending out from the box, the 'whiskers'. These whiskers stretch to the smallest and largest data values within a calculated range. Any data point beyond these whiskers is typically considered as outlier, meaning they are unusual. If you're familiar with a histogram, imagine if each box with its whiskers represents a single distribution. Each bar is a histogram with only four buckets representing 25%, 50%, 75%, and 100%. Our box chart template also has a filter to segment data by any category in the dataset.
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Use ten dynamic and customizable charts. They are grouped into four main categories: Progress, Timeline, Distribution, and Statistical charts. Each on...
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