Cross covariance of x and y, in statistics, is a measure of the similarity between x and shifted versions of y, as a function of the shift (lag). The cross covariance is given by the equation

where *E* is the expectation operator, and the processes have mean functions *v _{t}=E[Y_{t}] *and

*μ*

_{t}=E[X_{t}]In signal processing, cross-covariance has a slightly different definition: it measures the similarity between two signals, and is a function of the time between signals. It is sometimes called the sliding dot product or the cross-correlation.

Note that, in statistics, the cross-correlation and cross covariance are related but are not the same thing.

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*Statistical concepts explained visually* - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.

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