# covariance

## EnglishEdit

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### NounEdit

covariance (plural covariances)

1. (statistics) A statistical measure defined as ${\displaystyle \scriptstyle \operatorname {Cov} (X,Y)=\operatorname {E} ((X-\mu )(Y-\nu ))}$  given two real-valued random variables X and Y, with expected values ${\displaystyle \scriptstyle E(X)\,=\,\mu }$  and ${\displaystyle \scriptstyle E(Y)\,=\,\nu }$ .
• 2002, Karl G. Jöreskog, Dag Sörbom, PRELIS 2 User's Reference Guide, Scientific Software International, page 28,
The elements of such a correlation matrix do not have asymptotic variances and covariances of the form (1.2), even if S has a Wishart distribution.
• 1997, Michael Patrick Allen, Understanding Regression Analysis, Plenum Press, page 31,
Consequently, it can be shown that a covariance of two binary variables measures the extent to which the observed joint distribution of these variables differs from their expected joint distribution under the assumption that they are statistically independent.
• 2005, Steven J. Janke, Frederick Tinsley, Introduction to Linear Models and Statistical Inference, Wiley, page 133,
The covariance of X and Y is the expected value of the product of two random variables, XE(X) and YE(Y). [] If two random variables tend to act like opposites, one is high when the other is low and vice versa, then the covariance will be negative. If two random variables tend to be high and low at the same time, then the covariance will be positive. In fact, the covariance measures the extent of a linear relationship between the two random variables.
2. (object-oriented programming) The conversion of data types from wider to narrower in certain situations.