Probability that 𝑌>3𝑋 where 𝑋,𝑌 are 𝑁(0,1) random variables

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SUMMARY

The discussion focuses on calculating the probability that 𝑌 > 3𝑋 for independent standard normal random variables 𝑋 and 𝑌. A complex integral approach is proposed, involving the function f(x) = e^{-x^2/2}/√(2π), but a geometric method using angle addition is suggested as a simpler alternative. The relationship between the angles in quadrants I and II and the normal distribution is explored, with insights on converting integrals to polar coordinates to leverage the rotational symmetry of the joint distribution.

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Homework Statement
Probability that ##Y>3X## given ##Y>0## where ##X,Y## are ##N(0,1)## random variables
Relevant Equations
Nothing comes to mind
After plotting the above (not shown) I believe one way (the hard way) to solve this problem is to compute the following integral where ##f(x) = e^{-x^2/2}/\sqrt{2\pi}##: $$\frac{\int_0^\infty \int_{3X}^\infty f(X)f(Y)\, dydx + \int_{-\infty}^0 \int_0^\infty f(X)f(Y)\, dydx}{\int_{-\infty}^\infty \int_0^\infty f(X)f(Y)\, dydx}$$
But evidently there's a geometric approach that's much easier. Any help here?

To elaborate, I can see how quadrant II has angle ##\pi## and quadrant I has angle ##\arctan (1/3)## and the total region we consider is ##\pi## angle, so that the total angles considered divided by the angle allowed is the solution ##(\pi/2+\arctan(1/3))/\pi## but I don't understand how these angle additions relate to the normal distribution above.
 
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But I don't understand the reasoning here. I can picture the angles (as explained above) but fail to see the relation.

Edit by Mark44 -- I've deleted the image as it didn't match the problem here.
 
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joshmccraney said:
But I don't understand the reasoning here. I can picture the angles (as explained above) but fail to see the relation.
I've deleted my original drawing, as I mistakenly read N(0, 1) as if X and Y were constrained to the interval [0, 1]. I was also treating the distribution as being uniform, which is not the case. I'll have to think about this some more.
 
Mark44 said:
I've deleted my original drawing, as I mistakenly read N(0, 1) as if X and Y were constrained to the interval [0, 1]. I was also treating the distribution as being uniform, which is not the case. I'll have to think about this some more.
Copy all. It is interesting the integral above agrees with the angle addition I've outlined, but for the life of me I can't see why. For sure the joint distribution is cylindrically symmetric about (0,0), but beyond this I don't see how angle addition is related.
 
Consider that the fact that the joint probability distribution in the xy-plane is rotationally symmetric.
 
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joshmccraney said:
After plotting the above (not shown) I believe one way (the hard way) to solve this problem is to compute the following integral where ##f(x) = e^{-x^2/2}/\sqrt{2\pi}##: $$\frac{\int_0^\infty \int_{3X}^\infty f(X)f(Y)\, dydx + \int_{-\infty}^0 \int_0^\infty f(X)f(Y)\, dydx}{\int_{-\infty}^\infty \int_0^\infty f(X)f(Y)\, dydx}$$
But evidently there's a geometric approach that's much easier. Any help here?

To elaborate, I can see how quadrant II has angle ##\pi## and quadrant I has angle ##\arctan (1/3)## and the total region we consider is ##\pi## angle, so that the total angles considered divided by the angle allowed is the solution ##(\pi/2+\arctan(1/3))/\pi## but I don't understand how these angle additions relate to the normal distribution above.
If you convert the integrals to polar coordinates, they're not that bad, and you'll see the rotational symmetry @Orodruin mentioned explicitly.
 
Particularly note the integration boundaries in polar coordinates and note what happens to the radial integrals in both denominator and numerator.
 
  • #10
Makes tons of sense, thanks all!
 

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