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One of the most common statistics to describe a dataset is the range. The range of a dataset is the difference between the maximum and minimum values. While this descriptive statistic is a good start, it is important to consider the impact outliers have on the results: In this image, most of the data is between `0` and `15`. However, there is one large negative outlier (`-20`) and one large positive outlier (`40`). This makes the range of the dataset `60` (The difference between `40` and `-20`). That’s not very representative of the spread of the majority of the data!

The interquartile range (IQR) is a descriptive statistic that tries to solve this problem. The IQR ignores the tails of the dataset, so you know the range around-which your data is centered.

In this lesson, we’ll teach you how to calculate the interquartile range and interpret it.

### Instructions

1.

We’ve imported a dataset of song lengths (measured in seconds) and plotted a histogram.

It looks like there are some outliers — this might be a good opportunity to use the IQR.

Before we do that, let’s calculate the range. We’ve found the maximum and minimum values of the dataset and stored them in variables named `maximum` and `minimum`.

Create a variable named `song_range` and set it equal to the difference between the maximum and the minimum.