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We now continue the study of the
sum of a random number of

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independent random variables.

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We already figured out what is
the expected value of this

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sum, and we found a fairly
simple answer.

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When it comes to the variance,
however, it's pretty hard to

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guess what the answer will be,
and it turns out that the

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answer is not as simple.

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So this is what we will
try to calculate now.

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The way to proceed will be to
use the law of total variance,

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which effectively breaks down
the problem by conditioning on

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the value of the random
variable capital

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N. So let us start.

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We have already figured out that
if I tell you the value

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of capital N, then the expected
value of the random

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variable Y is just this number,
capital N, the number

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of stores you are visiting,
times how much you are

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spending in each one
of the stores.

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Using this information, we can
now calculate this term, the

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variance of the conditional
expectation.

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What is it?

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It's the variance of capital N
times the expected value of X.

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Now, the expected value of X
is a constant, and when we

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multiply a random variable with
a constant, what that

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does to the variance is it
multiplies the variance with

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the square of that constant.

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And this gives us this term in
the law of total variance.

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Let us now work towards
the second term.

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If I tell you the number of
stores, then the random

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variable Y is just
a sum of a given

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number of random variables.

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And as we discussed before, the
conditioning that we have

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here may be eliminated because
these random variables are now

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independent of this random
variable, capital N. Their

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distribution does not change
based on this information, and

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so we obtain the unconditional
variance.

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Now, the unconditional variance
of a sum of n random

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variables is just n times the
variance of each one of them,

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which we denote with
this notation.

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Now, let us take this equality,
which is an equality

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between numbers, and it's true
for any particular choice of

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little n, and turn it
into an equality

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between random variables.

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This is the random variable that
takes this specific value

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when capital N is equal
to little n.

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So this is a random variable
that takes this specific value

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when capital N is equal to
little n, but this is also the

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same as this random variable,
n times the variance of X,

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because this random variable
takes this particular

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numerical value when capital
N is equal to little n.

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Now that we have an expression
for the conditional variance

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as a random variable, we can
take the next step and

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calculate the expected value of
the conditional variance.

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The expected value of the
conditional variance is simply

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the expected value of this
expression that we

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calculated up here.

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And now the variance of X is a
constant and can be pulled

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outside the expectation,
which leaves us with

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this expression here.

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Now that we have calculated both
terms that go into the

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law of total variance, we
can add these two terms.

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We have one contribution from
here, this is this term, and

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another contribution from here,
which is this term.

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What this expression tells us
is that the variance of the

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total amount that you spend,
which is a certain measure of

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the amount of randomness in
how much you are spending

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overall, this amount
of randomness

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is due to two causes.

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One cause is the randomness
that there is in how much

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money you spend in any given
store, and that's captured by

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the variance of X. It's the
variance of the distribution

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of the amount of money that you
spend in a typical store.

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But there is another source of
randomness, and that source of

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randomness comes from the fact
that the number of stores

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itself is random, and this gives
us this contribution to

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the variance of Y.

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By taking into account these two
sources of randomness, we

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can figure out the overall
variance of the random

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variable Y. As you can see, this
is a formula that would

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be hard to guess by just
reasoning intuitively.

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And so it's a demonstration of
the power of the law of the

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total variance.