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4.7: Variance Sum Law II - Correlated Variables

  • Page ID
    2331
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    Learning Objectives

    • State the variance sum law when \(X\) and \(Y\) are not assumed to be independent
    • Compute the variance of the sum of two variables if the variance of each and their correlation is known
    • Compute the variance of the difference between two variables if the variance of each and their correlation is known

    Recall that when the variables \(X\) and \(Y\) are independent, the variance of the sum or difference between \(X\) and \(Y\) can be written as follows:

    \[ \sigma_{X \pm Y}^2 = \sigma_{X }^2 + \sigma_{Y}^2 \label{eq1}\]

    which is read: "The variance of \(X\) plus or minus \(Y\) is equal to the variance of \(X\) plus the variance of \(Y\)."

    When \(X\) and \(Y\) are correlated, the following formula should be used:

    \[ \sigma_{X \pm Y}^2 = \sigma_{X }^2 + \sigma_{Y}^2 \pm 2 \rho \sigma_X \sigma_Y \label{eq2}\]

    where \(\rho\) is the correlation between \(X\) and \(Y\) in the population.

    Example \(\PageIndex{1}\)

    If the variance of verbal SAT were \(10,000\), the variance of quantitative SAT were \(11,000\) and the correlation between these two tests were \(0.50\), what is the variance of total SAT (verbal + quantitative) and the difference (verbal - quantitative)?

    Solution

    Since the two variables are correlated, we use Equation \ref{eq2} instead of Equation \ref{eq1} for uncorrelated (independent) variables. Hence, the variance of the sum is

    \[\sigma^2_{verbal + quant} = 10,000 + 11,000 + 2\times 0.5\times \sqrt{10,000} \times \sqrt{11,000}\]

    which is equal to \(31,488\). The variance of the difference is also determined by Equation \ref{eq2}:

    \[\sigma^2_{verbal - quant} = 10,000 + 11,000 - 2\times 0.5\times \sqrt{10,000} \times \sqrt{11,000}\]

    which is equal to \(10,512\).

    If the variances and the correlation are computed in a sample, then the following notation is used to express the variance sum law:

    \[ s_{X \pm Y}^2 = s_{X }^2 + s_{Y}^2 \pm 2 r\, s_X \, s_Y\]


    This page titled 4.7: Variance Sum Law II - Correlated Variables is shared under a Public Domain license and was authored, remixed, and/or curated by David Lane via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.