In probability theory and statistics, two real-valued random variables are said to be uncorrelated if their covariance is zero. Uncorrelatedness is by definition pairwise; i.e. to say that more than two random variables are uncorrelated simply means that any two of them are uncorrelated.

Uncorrelated random variables have a correlation coefficient of zero, except in the trivial case when both variables have variance zero (are constants). In this case the correlation is undefined.

In general, uncorrelatedness is not the same as orthogonality, except in the special case where either X or Y has an expected value of 0. In this case, the covariance is the expectation of the product, and X and Y are uncorrelated if and only if E(XY) = E(X)E(Y).

If X and Y are independent, then they are uncorrelated. However, not all uncorrelated variables are independent. For example, if X is a continuous random variable uniformly distributed on [−1, 1] and Y = X², then X and Y are uncorrelated even though X determines Y and a particular value of Y can be produced by only one or two values of X.

A set of two or more random variables is called uncorrelated if each pair of them are uncorrelated.

[edit] References

• Galen R. Shorack, Probability for Statisticians, Birkhäuser, 2000, p127.

[edit] See also

• Correlation
• Covariance
• Bivariate normal distribution#Correlations_and_independence