Relating probability distributions huh?

AI Thread Summary
A variable X has an expected value of 0.002 meters, and when scaled to millimeters, it transforms into Y defined as Y = 1000X - 2. The discussion highlights the need to express the relationship between the cumulative distribution functions FX and FY, as well as the probability density functions fX and fY. Participants express uncertainty about how to relate these distributions, particularly in terms of their mathematical expressions. The solution involves calculating the cumulative distribution function for Y in terms of X and differentiating it to find the corresponding density function.
Dafydd
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Homework Statement



Problem description:
A variable X has expected value 0.002 in meters. Consider X - 0.002, scale to millimeter, and we get Y.

Tasks:
a) Express Y as a function of X
b) Relate the probability distributions FX and FY
c) Relate the probability density functions fX and fY

Homework Equations



F(x) = \operatorname P ( X \leq x ) = \int_{-\infty}^x f(t) \, \mathrm{d}t

\int_{-\infty}^{\infty} f(x) \, \mathrm{d}x = 1

f(x) = F'(x) \geq 0

The Attempt at a Solution



a) Y = 1000X - 2
(I think)

b) I have no clue. I mean, I could make a wild guess, but I don't see any reason to.

c) I suppose we get fX and fY by differentiating FX and FY... somehow.

I also don't really know what it means to "relate" these things. Is it to express the one in terms of the other, so that for example if a*b = c then relating a to b I either say that a = c/b or that b = c/a? Or what?
 
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Dafydd said:

Homework Statement



Problem description:
A variable X has expected value 0.002 in meters. Consider X - 0.002, scale to millimeter, and we get Y.

Tasks:
a) Express Y as a function of X
b) Relate the probability distributions FX and FY
c) Relate the probability density functions fX and fY

Homework Equations



F(x) = \operatorname P ( X \leq x ) = \int_{-\infty}^x f(t) \, \mathrm{d}t

\int_{-\infty}^{\infty} f(x) \, \mathrm{d}x = 1

f(x) = F'(x) \geq 0

The Attempt at a Solution



a) Y = 1000X - 2
(I think)

That looks good.

b) I have no clue. I mean, I could make a wild guess, but I don't see any reason to.

c) I suppose we get fX and fY by differentiating FX and FY... somehow.

Start by looking at the cumulative distribution function for Y:

G(y) = P(Y\le y) = P(1000 X - 2 \le y) = P(X \le\ ??)\ ...

You should be able to calculate this in terms of fX and it's derivative with respect to y will give you the density function for y.
 

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