Exercise

# Covariance vs Correlation

Covariance is a measure of whether two variables change ("vary") together. It is calculated by computing the products, point-by-point, of the deviations seen in the previous exercise, `dx[n]*dy[n]`

, and then finding the average of all those products.

Correlation is in essence the normalized covariance. In this exercise, you are provided with two arrays of data, which are highly correlated, and you will visualize and compute **both** the `covariance`

and the `correlation`

.

Instructions

**100 XP**

- Compute the deviations,
`dx`

and`dy`

by subtracting the mean, using`np.mean()`

, and compute`covariance`

as the mean of their product`dx*dy`

. - Compute the normalize deviations,
`zx`

and`zy`

, by dividing by the standard deviation, using`np.std()`

, and compute the`correlation`

as the mean of their product,`zx*zy`

. - Use
`plot_normalized_deviations(zx, zy)`

to plot the product of the normalized deviations and visually check it against the correlation value.