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.

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