covariance

As nouns the difference between autocorrelation and covariance

is that autocorrelation is (statistics|signal processing) the cross-correlation of a signal with itself: the correlation between values of a signal in successive time periods while covariance is (statistics) a statistical measure defined as $\backslash scriptstyle\backslash operatorname\{cov\}(x,\; y)\; =\; \backslash operatorname\{e\}((x\; -\; \backslash mu)\; (y\; -\; \backslash nu))$ given two real-valued random variables x'' and ''y , with expected values $\backslash scriptstyle\; e(x)\backslash ,=\backslash ,\backslash mu$ and $\backslash scriptstyle\; e(y)\backslash ,=\backslash ,\backslash nu$.

As nouns the difference between cooccurrence and covariance

is that cooccurrence is the fact of a thing occurring simultaneously with something else; correlation while covariance is a statistical measure defined as $\backslash scriptstyle\backslash operatorname\{Cov\}(X,\; Y)\; =\; \backslash operatorname\{E\}((X\; -\; \backslash mu)\; (Y\; -\; \backslash nu))$ given two real-valued random variables X and Y, with expected values $\backslash scriptstyle\; E(X)\backslash ,=\backslash ,\backslash mu$ and $\backslash scriptstyle\; E(Y)\backslash ,=\backslash ,\backslash nu$.

As nouns the difference between covariance and invariance

is that covariance is (statistics) a statistical measure defined as $\backslash scriptstyle\backslash operatorname\{cov\}(x,\; y)\; =\; \backslash operatorname\{e\}((x\; -\; \backslash mu)\; (y\; -\; \backslash nu))$ given two real-valued random variables x'' and ''y , with expected values $\backslash scriptstyle\; e(x)\backslash ,=\backslash ,\backslash mu$ and $\backslash scriptstyle\; e(y)\backslash ,=\backslash ,\backslash nu$ while invariance is the property of being invariant.

As nouns the difference between taxonomy and covariance

is that taxonomy is the science or the technique used to make a classification while covariance is a statistical measure defined as $\backslash scriptstyle\backslash operatorname\{Cov\}(X,\; Y)\; =\; \backslash operatorname\{E\}((X\; -\; \backslash mu)\; (Y\; -\; \backslash nu))$ given two real-valued random variables X and Y, with expected values $\backslash scriptstyle\; E(X)\backslash ,=\backslash ,\backslash mu$ and $\backslash scriptstyle\; E(Y)\backslash ,=\backslash ,\backslash nu$.

In statistics terms the difference between covariance and autocovariance

is that covariance is a statistical measure defined as $\backslash scriptstyle\backslash operatorname\{Cov\}(X,\; Y)\; =\; \backslash operatorname\{E\}((X\; -\; \backslash mu)\; (Y\; -\; \backslash nu))$ given two real-valued random variables X and Y, with expected values $\backslash scriptstyle\; E(X)\backslash ,=\backslash ,\backslash mu$ and $\backslash scriptstyle\; E(Y)\backslash ,=\backslash ,\backslash nu$ while autocovariance is the covariance of a signal with another part of the same signal.

Covariance is a related term of covary.

In statistics|lang=en terms the difference between covariance and covary

is that covariance is (statistics) a statistical measure defined as $\backslash scriptstyle\backslash operatorname\{cov\}(x,\; y)\; =\; \backslash operatorname\{e\}((x\; -\; \backslash mu)\; (y\; -\; \backslash nu))$ given two real-valued random variables x'' and ''y , with expected values $\backslash scriptstyle\; e(x)\backslash ,=\backslash ,\backslash mu$ and $\backslash scriptstyle\; e(y)\backslash ,=\backslash ,\backslash nu$ while covary is (statistics) to vary together with another variable, particularly in a way that may be predictive.

As a noun covariance

is (statistics) a statistical measure defined as $\backslash scriptstyle\backslash operatorname\{cov\}(x,\; y)\; =\; \backslash operatorname\{e\}((x\; -\; \backslash mu)\; (y\; -\; \backslash nu))$ given two real-valued random variables x'' and ''y , with expected values $\backslash scriptstyle\; e(x)\backslash ,=\backslash ,\backslash mu$ and $\backslash scriptstyle\; e(y)\backslash ,=\backslash ,\backslash nu$.

As a verb covary is

(statistics) to vary together with another variable, particularly in a way that may be predictive.

As nouns the difference between covariance and covariation

is that covariance is a statistical measure defined as $\backslash scriptstyle\backslash operatorname\{Cov\}(X,\; Y)\; =\; \backslash operatorname\{E\}((X\; -\; \backslash mu)\; (Y\; -\; \backslash nu))$ given two real-valued random variables X and Y, with expected values $\backslash scriptstyle\; E(X)\backslash ,=\backslash ,\backslash mu$ and $\backslash scriptstyle\; E(Y)\backslash ,=\backslash ,\backslash nu$ while covariation is covariance.

Covariate is a related term of covariance.

In statistics terms the difference between covariance and covariate

is that covariance is a statistical measure defined as $\backslash scriptstyle\backslash operatorname\{Cov\}(X,\; Y)\; =\; \backslash operatorname\{E\}((X\; -\; \backslash mu)\; (Y\; -\; \backslash nu))$ given two real-valued random variables X and Y, with expected values $\backslash scriptstyle\; E(X)\backslash ,=\backslash ,\backslash mu$ and $\backslash scriptstyle\; E(Y)\backslash ,=\backslash ,\backslash nu$ while covariate is a variable that is possibly predictive of the outcome under study.