# Is the cosine angle between two R.V. an (approximation) not equality to the correlation coefficient?

I have seen in websites that given two R.V. $$X,Y$$, if $$\cos(\theta)=\frac{X\cdot Y}{\|X\|_2\|Y\|_2}$$ and $$\rho=\frac{\text{Cov}(X,Y)}{\sqrt{\text{Var}(X)\text{Var}(Y)}}$$then

$$\cos(\theta)=\rho$$

This identity implies $$\text{Cov}(X,Y)=X\cdot Y$$. Isn't $$X\cdot Y$$ the Maximum Likelihood Estimate for the covariance missing some factors? If true then the equation above is not equality but rather $$≈$$ as the samples become bigger.

Next is the denominator which implies that $$\text{Var}(X)= \| X\|_2 ^2$$. Again, isn't the right side not an identity but rather an estimator (MLE) to the variance of $$X$$? Isn't $$\rho ≈ \cos(\theta)$$

I have also seen the dot product (without the denominator in the first equation I've given but being more general using inner products) being used to measure correlation in some papers like Least Angle Regression. I am confused about the relationship between dot products and correlation. This leads me to a general question:

Is $$\langle X,X\rangle = \text{Var}(X)$$ in Euclidean space.

• One defines an inner product by setting (for real random variables) $X\cdot Y = Cov(X,Y)$. Thus, for the corresponding norm, we get $\lvert \lvert X \rvert\rvert_2^2 = Cov(X,X) = \text{Var}(X)$. Commented Jun 26, 2022 at 14:31
• @CrackedBauxite May I ask about that definition? Isn't $Cov(X,Y)=E[(X-\mu_X)(Y-\mu_Y)]$ which can be either a summation or integral? Commented Jun 26, 2022 at 14:38
• That is not quite true (edit: with your edit the formula is true), we have $Cov(X,Y) = E((X-\mu_x)(Y-\mu_Y))$. If $X$ and $Y$ are discrete this is a sum, and if they are continuous it is indeed an integral. In fact one can write both of the cases above as a (Lebesgue) integral. Commented Jun 26, 2022 at 14:42
• @CrackedBauxite Assuming centered dataset, then $Cov(X,Y)=E(XY)$. I fail to see the final answer as an inner product. Commented Jun 26, 2022 at 14:42
• The point is that $X\cdot Y$ is defined as $Cov(X,Y)$. So the equality you are asking about is essentially true by definition. Commented Jun 26, 2022 at 14:44

Hint:

$$X \cdot Y$$ is a random variable.

$$\text{Cov}(X,Y)$$ is an expected value.

There is some confusion of terminolgy in your post.

If $$\bf X , \bf Y$$ are two random vectors (in $$m$$-space) then their dot product is a random variable $$\begin{array}{l} {\bf X},{\bf Y} \in R^m \\ q = {\bf X} \cdot {\bf Y} = \left\| {\bf X} \right\|\,\left\| {\bf Y} \right\|\;\cos \alpha \quad \left| {\;q \in R} \right. \\ \end{array}$$ which is actually the product of three random variables, among which $$\cos \alpha$$.
In this case the covariance is defined as a matrix of expected values, which is not what you are considering.

If instead $$\bf X , \bf Y$$ are two vectors corresponding to the joint sampling (of size $$m$$) of two random variables $$X,Y$$ and we are to estimate the correlation between them (which seems what you mean to do) then

• considering the variables to have zero mean, or the sampling mean having been subtracted from the sampling,
• considering the samples to have equal probability to be drawn,
• then the sampling covariance would in fact be $${\mathop{\rm cov}} (X,Y) = E\left[ {XY} \right] = \frac{1}{m}\sum\limits_{k = 1}^m {X_k Y_k } = \frac{1}{m}{\bf X} \cdot {\bf Y} = \frac{1}{m}\left\| {\bf X} \right\|\,\left\| {\bf Y} \right\|\;\cos \alpha$$ and $$\rho _{X,Y} = \frac{{{\mathop{\rm cov}} (X,Y)}}{{\sqrt {{\mathop{\rm cov}} (XX){\mathop{\rm cov}} (YY)} }} = \cos \alpha$$
• If you mean that $X\cdot Y$ denotes pointwise multiplication, then this is only true for zero-mean random variables $X$ and $Y$. Commented Jun 26, 2022 at 14:34
• yes, you are right: that's for zero mean. However, I wanted to underline the difference between r.v. and expected value. Commented Jun 26, 2022 at 14:44
• Then $X\cdot Y$ must be the dot product of sampled data from the distribution $X,Y$. So this concludes that $\rho$ and $\cos(\theta)$ are not equality but approximation? Commented Jun 26, 2022 at 14:51
• @12775: I added some clarifications Commented Jun 26, 2022 at 23:15