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Hey guys.

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Welcome back.

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Today we're going to do a fun
problem that will test your

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knowledge of the law
of total variance.

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And in the process, we'll also
get more practice dealing with

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joint PDFs and computing
conditional expectations and

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conditional variances.

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So in this problem, we are given
a joint PDF for x and y.

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So we're told that x and y can
take on the following values

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in the shape of this

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parallelogram, which I've drawn.

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And moreover, that x and y are
uniformly distributed.

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So the joint PDF is just flat
over this parallelogram.

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And because the parallelogram
has an area of 1, the height

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of the PDF must also be 1 so
that the PDF integrates to 1.

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OK.

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And then we are asked
to compute the

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variance of x plus y.

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So you can think of x plus y as
a new random variable whose

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variance we want to compute.

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And moreover, we're told we
should compute this variance

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by using something called the
law of total variance.

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So from lecture, you should
remember or you should recall

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that the law of total variance
can be written

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in these two ways.

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And the reason why there's two
different forms for this case

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is because the formula
always has you

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conditioning on something.

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Here we condition on x, here
we condition on y.

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And for this problem, the
logical choice you have for

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what to condition
on is x or y.

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So again, we have this option.

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And my claim is that we
should condition on x.

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And the reason has to do with
the geometry of this diagram.

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So notice that if you freeze an
x and then you sort of vary

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x, the width of this
parallelogram stays constant.

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However, if you condition on y
and look at the width this

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way, you see that the width
of the slices you get by

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conditioning vary with y.

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So to make our lives easier,
we're going to condition on x.

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And I'm going to erase this
bottom one, because

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we're not using it.

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So this really can seem quite
intimidating, because we have

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nested variances and
expectations going on, but

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we'll just take it slowly
step by step.

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So first, I want to focus
on this term--

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the conditional expectation of
x plus y conditioned on x.

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So coming back over to this
picture, if you fix an

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arbitrary x in the interval,
0 to 1, we're restricting

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ourselves to this universe.

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So y can only vary between this
point and this point.

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Now, I've already written down
here that the formula for this

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line is given by y
is equal to x.

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And the formula for this
line is given by y is

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equal to x plus 1.

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So in particular, when we
condition on x, we know that y

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varies between x and x plus 1.

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But we actually know
more than that.

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We know that in the
unconditional universe, x and

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y were uniformly distributed.

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So it follows that in the
conditional universe, y should

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also be uniformly distributed,
because conditioning doesn't

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change the relative frequency
of outcomes.

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So that reasoning means that
we can draw the conditional

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PDF of y conditioned
on x as this.

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We said it varies between
x and x plus 1.

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And we also said that it's
uniform, which means that it

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must have a height of 1.

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So this is py given
x, y given x.

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Now, you might be concerned,
because, well, we're trying to

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compute the expectation of
x plus y and this is the

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conditional PDF of y, not of the
random variable, x plus y.

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But I claim that we're OK, this
is still useful, because

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if we're conditioning
on x, this x

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just acts as a constant.

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It's not really going to change
anything except shift

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the expectation of y
by an amount of x.

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So what I'm saying in math terms
is that this is actually

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just x plus the expectation
of y given x.

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And now our conditional
PDF comes into play.

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Conditioned on x, this
is the PDF of y.

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And because it's uniformly
distributed and because

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expectation acts like center
of mass, we know that the

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expectation should be
the midpoint, right?

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And so to compute this point, we
simply take the average of

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the endpoints, x plus 1 plus
x over 2, which gives us

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2x plus 1 over 2.

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So plugging this back up here,
we get 2x/2 plus 2x plus 1

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over 2, which is 4x plus 1
over 2, or 2x plus 1/2.

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OK.

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So now I want to look at the
next term, the next inner

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term, which is this guy.

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So this computation is
going to be very

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similar in nature, actually.

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So we already discussed
that the joint--

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sorry, not the joint, the
conditional PDF of y given x

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is this guy.

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So the variance of x plus y
conditioned on x, we sort of

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have a similar phenomenon
occurring.

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x now in this conditional
world just acts like a

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constant that shifts the PDF but
doesn't change the width

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of the distribution at all.

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So this is actually just equal
to the variance of y given x,

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because constants don't
affect the variance.

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And now we can look at this
conditional PDF to figure out

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what this is.

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So we're going to take a quick
tangent over here, and I'm

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just going to remind you guys
that we have a formula for

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computing the variance of a
random variable when it's

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uniformly distributed between
two endpoints.

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So say we have a random variable
whose PDF looks

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something like this.

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Let's call it, let's say, w.

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This is pww.

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We have a formula that says
variance of w is equal to b

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minus a squared over 12.

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So we can apply that
formula over here.

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b is x plus 1, a is x.

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So b minus a squared over
12 is just 1/12.

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So we get 1/12.

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So we're making good progress,
because we have this inner

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quantity and this
inner quantity.

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So now all we need to do is take
the outer variance and

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the outer expectation.

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So writing this all down, we
get variance of x plus y is

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equal to variance of this guy,
2x plus 1/2 plus the

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expectation of 1/12.

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So this term is quite simple.

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We know that the expectation of
a constant or of a scalar

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is simply that scalar.

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So this evaluates to 1/12.

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And this one is not
bad either.

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So similar to our discussion up
here, we know constants do

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not affect variance.

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You know they shift your
distribution, they don't

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change the variance.

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So we can ignore the 1/2.

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This scaling factor
of 2, however,

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will change the variance.

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But we know how to handle
this already

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from previous lectures.

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We know that you can just take
out this scalar scaling factor

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as long as we square it.

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So this becomes 2 squared,
or 4 times the

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variance of x plus 1/12.

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And now to compute the variance
of x, we're going to

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use that formula again,
and we're

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going to use this picture.

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So here we have the joint PDF of
x and y, but really we want

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now the PDF of x, so we
can figure out what

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the variance is.

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So hopefully you remember a
trick we taught you called

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marginalization.

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To get the PDF of x given
a joint PDF, you simply

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marginalize over the
values of y.

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So if you freeze x is equal to
0, you get the probability

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density line over x by
integrating over this

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interval, over y.

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So if you integrate over
this strip, you get 1.

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If you move x over a little
bit and you integrate over

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this strip, you get 1.

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This is the argument I was
making earlier that the width

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of this interval stays the same,
and hence, the variance

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stays the same.

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So based on that argument, which
was slightly hand wavy,

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let's come over here
and draw it.

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We're claiming that the PDF of
x, px of x, looks like this.

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It's just uniformly distributed
between 0 and 1.

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And if you buy that, then we're
done, we're home free,

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because we can apply this
formula, b minus a squared

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over 12, gives us
the variance.

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So b is 1, a is 0, which
gives variance of

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x is equal to 1/12.

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So coming back over here, we
get 4 times 1/12 plus 1/12,

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which is 5/12.

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And that is our answer.

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So this problem was
straightforward in the sense

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that our task was very clear.

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We had to compute this, and we
had to do so by using the law

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of total variance.

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But we sort of reviewed a lot
of concepts along the way.

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We saw how, given a joint
PDF, you marginalize to

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get the PDF of x.

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We saw how constants don't
change variance.

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We got a lot of practice
finding conditional

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distributions and computing
conditional

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expectations and variances.

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And we also saw this trick.

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And it might seem like cheating
to memorize formulas,

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but there's a few important
ones you should know.

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And it will help you sort of
become faster at doing

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computations.

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And that's important,
especially if you

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guys take the exams.

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So that's it.

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See you next time.

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