Correlation is one of the most widely used and a well-known measure of the assocation (*linear* association, that is) of two variables.

Perhaps less well-known is that the correlation is in principle *identical to the covariation*.

To see this, consider ~~the~~ a formula of the covariance of two empirical datasets, and :

In other words, the covariance of $X$ and $Y$ $COV(X,Y)$ is the average of difference of some value to its mean.

This idea is conveyed by this picture:

# The covariance is identical to the correlation (?)

What does it mean to say the (coefficient of) correlation is “identical” to the covariation?

*If we “feed” z-scaled values to the covariation, we will get back the correlation*.

In other words, the correlation equals the covariation if the data are z-scaled.

So, let’s see. We replace by and by and see what happens.

However, , and by analogy, . So the eqaution simplifies to

Now, can be expressed as

The same rule applies for , by analogy.

Now, let’s insert the previous equation in the equation of :

can be pulled out of the sum, right at the front of the equation, leaving us with

.

And that’s the definition of the correlation of and , more frequently put this way:

.

Hence,

.

# Example time

It is helpful to consider an example.

This is a scatterplot of two variables, ie., “raw data” as is “fed in” for the calculation of the (empirical) covariation:

```
library(tidyverse)
mtcars %>%
ggplot +
aes(x = hp, y = mpg) +
geom_point()
```

And now, let’s z-scale the two variables and draw the same diagram again:

```
mtcars %>%
select(hp, mpg) %>%
mutate_all(funs(scale)) %>%
ggplot +
aes(x = hp, y = mpg) +
geom_point()
```

Now, what’s the difference? Nada, no difference. That’s reassuring, because we just derived that the assocation of the variables is the same - no matter if use the raw data or z-scaled data as input. The diagrams confirms this in an more intuitive way.

# Summary

The correlation is a “special case” of the covariance; it is the case when we feed z-scaled data to the covariance.

Happy data analyzing!