C = cov(X)C = cov(x,y)
C = cov(X), where X is a vector, returns a scalar containing the variance. C = cov(X), where X is a matrix with each row an observation and each column a variable, returns the covariance matrix. diag(cov(X)) is a vector of variances for each column, and sqrt(diag(cov(X))) is a vector of standard deviations.
cov(X) is the 0-th lag of the covariance function, that is, the 0-th lag of xcov(X)/(n-1) packed into a square array.
cov(x,y), where x and y are column vectors of equal length, is equivalent to cov([x y]).
cov removes the mean from each column before calculating the results.
cov is
[n,p] = size(X);X = X-ones(n,1)*mean(X);Y = X'*X/(n-1);
corrcoef, mean, std
xcorr, xcov in the Signal Processing Toolbox
(c) Copyright 1994 by The MathWorks, Inc.