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#reading-9-probability-concepts

The covariance between two random variables *R*_{i} and *R*_{j} is the expected value of the cross-product of the deviations of the two random variables from their respective means: Cov(*R*_{i},*R*_{j}) = *E*{[*R*_{i} − *E*(*R*_{i})][*R*_{j} − *E*(*R*_{j})]}. The covariance of a random variable with itself is its own variance.

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**Summary **

ive square root of variance. Standard deviation measures dispersion (as does variance), but it is measured in the same units as the variable. Covariance is a measure of the co-movement between random variables. <span>The covariance between two random variables R i and R j is the expected value of the cross-product of the deviations of the two random variables from their respective means: Cov(R i ,R j ) = E{[R i − E(R i )][R j − E(R j )]}. The covariance of a random variable with itself is its own variance. Correlation is a number between −1 and +1 that measures the co-movement (linear association) between two random variables: ρ(R i ,R j ) = Cov(R i ,R j )/[σ(R i ) σ(R j )

ive square root of variance. Standard deviation measures dispersion (as does variance), but it is measured in the same units as the variable. Covariance is a measure of the co-movement between random variables. <span>The covariance between two random variables R i and R j is the expected value of the cross-product of the deviations of the two random variables from their respective means: Cov(R i ,R j ) = E{[R i − E(R i )][R j − E(R j )]}. The covariance of a random variable with itself is its own variance. Correlation is a number between −1 and +1 that measures the co-movement (linear association) between two random variables: ρ(R i ,R j ) = Cov(R i ,R j )/[σ(R i ) σ(R j )

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