# What is the formula for computation of covariance between two variables that are imperfectly correlated? [closed]

Generally, Covariance is computed assuming that two random variables are independent. But in a situation that warrants some association between these two variables, how can we compute covariance.

• The relate only insofar as they are a function of two variables. If two variables $X, Y$ are independent $X \perp Y$, then $\mbox{Cov}(X, Y) = 0$ and your covariate (or regression model parameter) will be set to some null value (e.g. 0 for parameters in linear models). Jan 8 '14 at 19:02
• I downvoted this question because anyone can easily and quickly resolve the distinction between covariate and covariance by consulting a dictionary. We request that you do some research into the answer before you post any question, because "sharing your research helps everyone. ... This demonstrates that you’ve taken the time to try to help yourself [and] it saves us from reiterating obvious answers."
– whuber
Jan 8 '14 at 20:17
• This question appears to be off-topic because it makes no contribution to the goals of the forum. If anybody could cite any source appearing to conflate or confuse the two ideas, there would be some sense in it, but not otherwise. Jan 7 '15 at 16:45
• @subhashc.davar: Sorry, but I haven't the slightest idea what you want to ask now, & doubt anyone else will have. Makes no sense to talk about correlated but independent variables, or about computing a covariate. Jan 11 '15 at 19:57
• Here is a metacomment on this thread, which has been revised many times since first posting. The question has been edited a long way from the original question. There is one good upvoted answer here explaining the difference between covariance and covariate, should they ever be confused. But that is no longer the question. Commentary on everything else indicates a lot of puzzlement shared by a lot of people on what is being asked and why. Oct 27 '15 at 14:24