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In this segment, we discuss a
few algebraic properties of

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the covariance.

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There is nothing deep here, only
some observations that

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can be useful if we want
to carry out covariance

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calculations.

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We start by looking at this
quantity, the covariance of a

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random variable with itself.

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So it's a special case of this
definition but where Y is the

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same as X. And therefore, this
second term here is the same

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as the first term.

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And so what we're left with is
the expected value of the

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square deviation of the random
variable from its mean.

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And we recognize that this is
the same as the variance of X.

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So we conclude that the
covariance of a random

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variable with itself is the
same as the variance.

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Now, for the variance, we had an
alternative formula, which

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was often convenient in
simplifying variance

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calculations.

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Is there a similar formula for
the case of the covariance?

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Let us start from the definition
and use linearity.

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We have a product here of two
terms, and we expand that

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product to obtain four
different terms.

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The expected value of the sum
of these four terms is going

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to be the sum of their
expectations.

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So let us go through
the steps involved.

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We have the expected value
of the product of this

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term with that term.

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Gives us expected value of X
times Y. Then we take the

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expected value of the product
of this term with that term.

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And because we have a minus
sign, we put it out here.

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And we have the expected value
of X times the expected value

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of Y inside the expectation.

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The next term is going to be the
product of this expected

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value with Y. And that gives us
minus the expected value of

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X times Y. And finally, the last
term comes by multiplying

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this quantity with
that quantity.

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And this is what we have by
applying linearity to the

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definition of the covariance.

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Now, remember that the expected
value of a random

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variable is a number,
it's a constant.

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And constants can be pulled
outside expectations.

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So if we do that, what we
obtain is the following.

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We pull this constant outside
the expectation, and we're

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left with the expected value of
X times the expected value

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of Y. Similarly, for the next
term, by pulling a constant

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outside the expectation, we
obtain this expression.

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And finally, for the last term,
we have the expected

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value of a constant, and
this is the same as

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the constant itself.

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We recognize here that
the same term gets

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repeated three times.

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And because here we have a minus
sign, we can cancel this

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term with that term.

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And what we're left with is just
the difference of these

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two terms, and this is an
alternative form for the

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covariance of two random
variables.

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And this form is often easier
to work with to calculate

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covariances compared with
the original definition.

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Let us now continue with some
additional algebraic

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properties.

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Suppose that we know the
covariance of X with Y, and

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we're interested in the
covariance of this linear

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function of X with Y. What is
the covariance going to be?

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To simplify the calculations,
let us just assume zero means.

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Although, the final conclusion
will be the same as in the

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case of non-zero means.

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So in the case of zero means,
the covariance of two random

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variables is just the same as
the expected value of the

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product of the two
random variables.

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And using linearity, this is the
expected value times a of

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X times Y plus b times the
expected value of Y. Now, we

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assumed zero means, so
this term goes away.

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And what we're left with is a
times the covariance of X with

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Y. So we see that multiplying
X by a increases the

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covariance by a factor of a.

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But adding a constant
has no effect.

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The reason that it has no effect
is that if we take a

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random variable and add the
constant to it, the same

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constant gets added
to its mean.

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And so this difference
is not affected.

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As our final calculation, let us
look at the covariance of a

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random variable with the sum of
two other random variables.

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Again, we assume zero means.

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And so the calculation
is as follows.

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The covariance is the
product of the two

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random variables involved.

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And then we use linearity of
expectations to write this as

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the expected value of X times Y
plus the expected value of X

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times Z.

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And we recognize that this is
the same as the covariance of

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X with Y plus the covariance
of X with Z. So in this

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respect, covariances
behave linearly.

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They behave linearly with
respect to one of the

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arguments involved.