Exercise

# EDA of literacy/fertility data

In the next few exercises, we will look at the correlation between female literacy and fertility (defined as the average number of children born per woman) throughout the world. For ease of analysis and interpretation, we will work with the *il*literacy rate.

It is always a good idea to do some EDA ahead of our analysis. To this end, plot the fertility versus illiteracy and compute the Pearson correlation coefficient. The Numpy array `illiteracy`

has the illiteracy rate among females for most of the world's nations. The array `fertility`

has the corresponding fertility data.

Here, it may be useful to refer back to the function you wrote in the previous course to compute the Pearson correlation coefficient.

Instructions

**100 XP**

- Plot
`fertility`

(y-axis) versus`illiteracy`

(x-axis) as a scatter plot. - Set a 2% margin.
- Compute and print the Pearson correlation coefficient between
`illiteracy`

and`fertility`

.