Exercise

# Computing the covariance

The covariance may be computed using the Numpy function `np.cov()`

. For example, we have two sets of data `x`

and `y`

, `np.cov(x, y)`

returns a 2D array where entries `[0,1]`

and `[1,0]`

are the covariances. Entry `[0,0]`

is the variance of the data in `x`

, and entry `[1,1]`

is the variance of the data in `y`

. This 2D output array is called the **covariance matrix**, since it organizes the self- and covariance.

To remind you how the *I. versicolor* petal length and width are related, we include the scatter plot you generated in a previous exercise.

Instructions

**100 XP**

- Use
`np.cov()`

to compute the covariance matrix for the petal length (`versicolor_petal_length`

) and width (`versicolor_petal_width`

) of*I. versicolor*. - Print the covariance matrix.
- Extract the covariance from entry
`[0,1]`

of the covariance matrix. Note that by symmetry, entry`[1,0]`

is the same as entry`[0,1]`

. - Print the covariance.