It’s important to note that there are some limitations to using correlation or covariance as a way of assessing whether there is an association between two variables. Because correlation and covariance both measure the strength of **linear** relationships with non-zero slopes, but not other kinds of relationships, correlation can be misleading.

For example, the four scatter plots below all show pairs of variables with near-zero correlations. The bottom left image shows an example of a perfect linear association where the slope is zero (the line is horizontal). Meanwhile, the other three plots show non-linear relationships — if we drew a line through any of these sets of points, that line would need to be curved, not straight!

### Instructions

**1.**

A simulated dataset named `sleep`

has been loaded for you in **script.py**. The hypothetical data contains two columns:

`hours_sleep`

: the number of hours that a person slept`performance`

: that person’s performance score on a physical task the next day

Create a scatter plot of `hours_sleep`

(on the x-axis) and `performance`

(on the y-axis). What is the relationship between these variables?

**2.**

Calculate the correlation for `hours_sleep`

and `performance`

and save the result as `corr_sleep_performance`

. Then, print out `corr_sleep_performance`

. Does the correlation accurately reflect the strength of the relationship between these variables?