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In this segment, we will go
through the calculation of the

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variances of some familiar
random variables, starting

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with the simplest one that we
know, which is the Bernoulli

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random variable.

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So let X take values 0 or 1, and
it takes a value of 1 with

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probability p.

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We have already calculated the
expected value of X, and we

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know that it is equal to p.

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Let us now compute
its variance.

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One way of proceeding is to use
the definition and then

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the expected value rule.

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So if we now apply the expected
value rule, we need

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the summation over all possible
values of X. There

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are two values--

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x equal to 1 or x equal to 0.

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The contribution when X is
equal to 1 is 1 minus the

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expected value, which
is p squared.

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And the value of 1 is taken
with probability p.

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There is another contribution
to this sum when little x is

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equal to 0.

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And that contribution is going
to be 0 minus p, all of this

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squared, times the probability
of 0, which is 1 minus p.

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And now we carry out
some algebra.

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We expand the square here, 1
minus 2p plus p squared.

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And after we multiply with this
factor of p, we obtain p

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minus 2p squared plus p
to the third power.

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And then from here we have a
factor of p squared times 1, p

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squared times minus p.

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That gives us a minus p cubed.

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Then we notice that this term
cancels out with that term.

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p squared minus 2p
squared leaves us

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with p minus p squared.

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And we factor this as
p times 1 minus p.

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An alternative calculation
uses the formula that we

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provided a little earlier.

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Let's see how this will go.

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We have the following
observation.

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The random variable X squared
and the random variable X--

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they are one and the same.

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When X is 0, X squared
is also 0.

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When X is 1, X squared
is also 1.

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So as random variables, these
two random variables are equal

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in the case where X
is a Bernoulli.

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So what we have here is just the
expected value of X minus

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the expected value of X squared,
to the second power.

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And this is p minus p squared,
which is the same answer as we

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got before--

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p times 1 minus p.

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And we see that the calculations
and the algebra

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involved using this formula
were a little simpler than

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they were before.

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Now the form of the variance
of the Bernoulli random

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variable has an interesting
dependence on p.

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It's instructive to plot
it as a function of p.

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So this is a plot of the
variance of the Bernoulli as a

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function of p, as p ranges
between 0 and 1.

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p times 1 minus p
is a parabola.

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And it's a parabola that is
0 when p is either 0 or 1.

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And it has this particular
shape, and the peak of this

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parabola occurs when p is equal
to 1/2, in which case

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the variance is 1/4.

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In some sense, the variance is
a measure of the amount of

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uncertainty in a random
variable, a measure of the

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amount of randomness.

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A coin is most random if it
is fair, that is, when

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p is equal to 1/2.

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And in this case, the variance
confirms this intuition.

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The variance of a coin flip is
biggest if that coin is fair.

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On the other hand, in
the extreme cases

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where p equals 0--

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so the coin always results in
tails, or if p equals to 1 so

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that the coin always results in
heads-- in those cases, we

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do not have any randomness.

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And the variance,

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correspondingly, is equal to 0.

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Let us now calculate
the variance of a

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uniform random variable.

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Let us start with a simple case
where the range of the

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uniform random variable starts
at 0 and extends up to some n.

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So there is a total of n plus 1
possible values, each one of

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them having the same
probability--

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1 over n plus 1.

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We calculate the variance using
the alternative formula.

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And let us start with
the first term.

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What is it?

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We use the expected value rule,
and we argue that with

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probability 1 over n plus 1, the
random variable X squared

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takes the value 0 squared, with
the same probability,

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takes the value 1 squared.

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With the same probability, it
takes the value 2 squared, and

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so on, all of the way
up to n squared.

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And then there's
the next term.

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The expected value of the
uniform is the midpoint of the

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distribution by symmetry.

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So it's n over 2, and we take
the square of that.

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Now to make progress here, we
need to evaluate this sum.

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Fortunately, this has
been done by others.

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And it turns out to be equal
to 1 over 6 n, n plus 1

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times 2n plus 1.

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This formula can be proved by
induction, but we will just

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take it for granted.

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Using this formula, and after a
little bit of simple algebra

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and after we simplify, we obtain
a final answer, which

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is of the form 1 over
12 n times n plus 2.

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How about the variance
of a more general

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uniform random variable?

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So suppose we have a uniform
random variable whose range is

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from a to b.

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How is this PMF related to the
one that we already studied?

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First, let us assume that n
is chosen so that it is

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equal to b minus a.

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So in that case, the difference
between the last

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and the first value of the
random variable is the same as

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the difference between the last
and the first possible

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value in this PMF.

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So both PMFs have the same
number of terms.

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They have exactly
the same shape.

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The only difference is that the
second PMF is shifted away

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from 0, and it starts at a
instead of starting at 0.

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Now what does shifting
a PMF correspond to?

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It essentially amounts to taking
a random variable--

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let's say, with this PMF--

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and adding a constant to
that random variable.

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So if the original random
variable takes the value of 0,

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the new random variable
takes the value of a.

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If the original takes the value
of 1, this new random

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variable takes the value
of a plus 1, and so on.

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So this shifted PMF is the PMF
associated to a random

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variable equal to the
original random

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variable plus a constant.

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But we know that adding
a constant does

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not change the variance.

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Therefore, the variance of this
PMF is going to be the

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same as the variance of the
original PMF, as long as we

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make the correspondence that
n is equal to b minus a.

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So doing this substitution in
the formula that we derived

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earlier, we obtain 1 over
12 b minus a times b

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minus a plus 2.