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In all of the examples that we
have seen so far, we have

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calculated the distribution of a
random variable, Y, which is

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defined as a function of another
random variable, X.

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What about the case where we
define a random variable, Z,

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as a function of multiple
random variables?

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For example, here
is the function

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of two random variables.

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How can we find a distribution
of Z?

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The general methodology
is exactly the same.

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We somehow calculate the CDF of
the random variable Z and

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then differentiate
to find its PDF.

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Let us illustrate
this methodology

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with a simple example.

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So suppose that X and Y are
independent random variables

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and each one of them is uniform
on the unit interval.

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So their joint distribution is
going to be a uniform PDF on

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the unit square.

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We're interested in the random
variable, which is defined as

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the ratio of Y divided by X.

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So we will now calculate
the CDF of Z and then

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differentiate.

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It is useful to work in
terms of a diagram.

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This is essentially our sample
space, the unit square.

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The PDF of X is 1 on
the unit interval.

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The PDF of Y is 1 on
the unit interval.

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Because of independence, the
joint PDF is the product of

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their individual PDFs.

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So the joint PDF is equal to 1
throughout this unit square.

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So now let us write an
expression for the CDF of Z,

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which, by definition, is the
probability that the random

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variable Z, which in our case
is Y divided by X, is less

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than or equal than a certain
number, little z.

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What is the probability
of this event?

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Let us consider a few
different cases.

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Suppose that z is negative.

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What is the probability that
this ratio is negative?

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Well, since X and Y are
non-negative numbers, there's

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no way that the ratio is
going to be negative.

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So if little z is a negative
number, the probability of

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this event is going
to be equal to 0.

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This is the easier case.

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Now suppose that z is
a positive number.

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Let us draw a line that has
a slope of little z.

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y/z being less than or equal
to little z is the same as

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saying that y is less than or
equal to little z times x.

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This is the line on which
y is equal to z times x.

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So below that line, y is going
to be less than or

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equal to z times x.

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So the event of interest is
actually this triangle here.

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And the probability of this
event, since we're dealing

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with a uniform distribution on
the unit square, is just the

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area of this triangle.

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Now, since this line rises at
slope z, this point here, this

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intercept is at z.

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And so the sides of the
triangle are 1 and z.

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And so this formula here gives
us the value of the CDF for

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the case where little
z is positive.

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And the same formula would also
be true if z also were

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equal to 0, in which case,
we get 0 probability.

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But is this correct for
all positive z's?

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Well, not really.

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This calculation was based
on this picture.

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And in this picture, this line
intercepted this side of the

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unit square.

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And for that to happen, this
slope must be less than or

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equal to 1.

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So this formula is only correct
in the case where we

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have a slope of less
than or equal to 1.

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And now we need to deal with
the remaining case in which

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little z is strictly
larger than 1.

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In this case, we get a somewhat
different picture.

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If we draw a line with slope,
again, little z, because

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little z is bigger than 1, it's
going to intercept this

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side of the rectangle.

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Now, the event that Y/X is less
than or equal to little z

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is, again, the event that the
pair, X, Y, lies below this

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line that has a slope of z.

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So all we need is to find
the area of this region.

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One way of finding the area of
this region is to take the

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area of the entire unit square,
which is equal to 1,

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and subtract the area
of this triangle.

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What is the area of
this triangle?

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Well, since this line has a
slope of z, in order for it to

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rise to a value of 1, x must be
equal to 1 over little z.

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Therefore, this side of
the triangle is 1/z.

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And therefore, the area of the
triangle is 1/2 times 1/z,

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which is this expression here.

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And so we have found the value
of the CDF for all possible

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choices of little z.

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We can draw the CDF.

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And the picture is as follows.

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For z negative, the
CDF is equal to 0.

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For z between 0 and 1, the
CDF rises linearly

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at a slope of 1/2.

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And so when z is equal to
1, the CDF has risen

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to a value of 1/2.

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And then as z goes to infinity,
this term disappears

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and the CDF will
converge to 1.

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So it converges to 1
monotonically but in a

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non-linear fashion.

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So we get a picture
of this type.

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The next step, the final step,
is to differentiate the CDF

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and obtain the PDF.

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In this region, the CDF is
constant, so its derivative is

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going to be equal to 0.

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In this region, the CDF is
linear, so its derivative is

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equal to this factor of 1/2.

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So the CDF is equal to 1/2
for z's between 0 and 1.

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And finally, in this
region, this is the

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formula for the CDF.

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When we take the derivative, we
get the expression 1 over

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2z squared, which is a function
that decreases as z

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goes to infinity.

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So it has a shape
like this one.

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So we have completed the
solution to this problem.

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We found the CDF, and we found
the corresponding PDF.

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This methodology works more
generally for more complicated

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functions of X and Y and for
more complicated distributions

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for X and Y. Of course, when
the functions or the

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distributions are more
complicated, the calculus

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involved and the geometry may
require a lot more work.

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But conceptually,
the methodology

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is exactly the same.