# Why does Covariance measure only Linear dependence?

1) What is meant by linear dependence?

2) How can I convince myself that covariance measures linear dependence?

3) How I can convince myself that non-linear dependence is not measured by covariance?

## Answer

A1) Say two variables X and Y are linearly dependent, then $$X=αY+cX = \alpha Y + c$$ for some $$α,c∈R\alpha,c \in \mathbb{R}$$.

A2) The formula for covariance is:

$$COV(X,Y)=E([X−E(X)][Y−E(Y)])=E(XY)−E(X)E(Y)COV(X,Y) = E([X-E(X)][Y-E(Y)]) = E(XY)-E(X)E(Y)$$

From A1, consider some linear relationship $$X=αY+cX = \alpha Y + c$$, but all we have is the data from individual points in each variable. How do we get the value of $$α\alpha$$? Well, it turns out we can instead ask the question, “how do we draw a line between these points so as to minimise the sum of squared differences between each point and the line?”. And when we do this analysis for two variables, we get a closed form equation that looks like this:

$$α=E(XY)−E(Y)E(X)E(X2)−E(X)2\alpha = \dfrac{E(XY) -E(Y)E(X)}{E(X^2) - E(X)^2}$$

Please note that the numerator is the covariance. I.e.

$$α=COV(X,Y)E(X2)−E(X)2 \alpha = \dfrac{COV(X,Y)}{E(X^2) - E(X)^2}$$

Correlation (e.g. Pearson) is often a measure of the covariance normalised against something to give it a comparable value. So you see the entire measure precedes from the analysis of how to fit a line to some data.

A3) Covariance doesn’t measure non-linear relationships for the exact same reason it measures linear ones. Namely, that you can basically think of it as the slope in a linear equation (e.g. $$X=αY+cX=\alpha Y + c$$), so when you try and fit a line to a curve, the sum of square differences between the points and the line may be large. Here is a good diagram illustrating the implications. The numbers indicate Pearson’s correlation coefficient, whilst the diagrams show the corresponding scatter plots.

Attribution
Source : Link , Question Author : ColorStatistics , Answer Author : JP Zhang