Covariance of a discrete distribution

Let X X and Y Y be random variables such that

  • P ( X = 0 , Y = 1 ) = 1 / 5 P(X = 0, Y = -1) = 1/5
  • P ( X = 0 , Y = 1 ) = 1 / 5 P(X = 0, Y = 1) = 1/5
  • P ( X = 1 , Y = 1 ) = 1 / 2 P(X = 1, Y = -1) = 1/2
  • P ( X = 1 , Y = 1 ) = 1 / 10 P(X = 1, Y = 1) = 1/10 .

Find Cov ( X , Y ) \text{Cov}(X, Y) .


The answer is -0.16.

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1 solution

Lawrence Chiou
Apr 7, 2016

We first compute

  • P ( X Y = 1 ) = 1 / 2 P(XY = -1) = 1/2 , P ( X Y = 0 ) = 2 / 5 P(XY = 0) = 2/5 , and P ( X Y = 1 ) = 1 / 10 P(XY = 1) = 1/10 , so E [ X Y ] = 2 / 5 E[XY] = -2/5 .
  • P ( X = 0 ) = 2 / 5 P(X = 0) = 2/5 and P ( X = 1 ) = 3 / 5 P(X = 1) = 3/5 , so E [ X ] = 3 / 5 E[X] = 3/5 .
  • P ( Y = 1 ) = 7 / 10 P(Y = -1) = 7/10 and P ( Y = 1 ) = 3 / 10 P(Y = 1) = 3/10 , so E [ Y ] = 2 / 5 E[Y] = -2/5 .

It follows that

Cov ( X , Y ) = E [ X Y ] E [ X ] E [ Y ] = 2 / 5 ( 3 / 5 ) ( 2 / 5 ) = 4 / 25. \text{Cov}(X, Y) = E[XY] - E[X] E[Y] = -2/5 - (3/5)(-2/5) = \boxed{-4/25.}

Is there a way we could intuitively have known that the covariance was going to be negative from looking at the joint distribution given?

Eli Ross Staff - 5 years, 2 months ago

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X being zero has no effect on covariance since Y is equally likely to be -1 or 1. Then, just looking at P(X=1) you can see Y is more likely to be negative with respect to X.

M B - 3 years ago

E[XY] is wrong. E[XY] = -1/5 since it is E[XY] = 0 -1 1/5 + 0 1 1/5+1 (-1) 1/2+1 1 1/10 = -1/5

Christian Willdoner - 7 months, 3 weeks ago

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It looks right to me. E[XY] = (0)(1/5) + (0)(1/5) + (-1)(1/2) + (1)(1/10) = 0 - 1/2 + 1/10 = -2/5

Tony Bai - 4 months, 1 week ago

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