Asked by Muhammad

The random variable X has a standard normal distribution. Find the PDF of the random variable Y , where:
1. Y = 5X−7 .

2. Y = X2−2X . For y≥−1 ,

Answers

Answered by dz
1. fY(y)=1/5*fX(y+7 /5)

2. [[ fX(1+√(y+1)) + fX(1-√(y+1)) ]] / 2√(y+1)
Answered by Benedikt
Hey dz, would it be possible to get a more detailed explanation for you calculations?

Thanks!
Answered by Bot
Sure, I'd be happy to explain in more detail!

1. To find the PDF of Y = 5X - 7, we can use the formula for transforming a continuous random variable.

fY(y) = fX(x) * |dx/dy|

Since Y = 5X - 7, we can solve for X in terms of Y to get:

X = (Y + 7)/5

Taking the derivative with respect to y:

dx/dy = 1/5

And substituting into the formula:

fY(y) = fX((y+7)/5) * 1/5

Since X has a standard normal distribution, we know that fX(x) is the standard normal probability density function:

fX(x) = (1/√(2π)) * e^(-x^2/2)

Substituting this into the formula above, we get:

fY(y) = (1/√(2π)) * e^(-((y+7)/5)^2/2) * 1/5

Simplifying:

fY(y) = (1/√(2π) * 5) * e^(-((y+7)/5)^2/2)

2. To find the PDF of Y = X^2 - 2X for y ≥ -1, we need to first find the range of X values that correspond to this range of Y values.

Y = X^2 - 2X can be rewritten as:

Y = (X-1)^2 - 1

Adding 1 to both sides:

Y + 1 = (X-1)^2

Taking the square root (note that since we want y ≥ -1, we take the absolute value of the square root):

|X-1| = √(Y+1)

Solving for X:

X = 1 + √(Y+1) or X = 1 - √(Y+1)

Now we can use the transformation formula again:

fY(y) = fX(x) * |dx/dy|

Since we have two solutions for X, we need to take the average of the two probabilities:

fY(y) = (fX(1+√(y+1)) + fX(1-√(y+1))) / 2 * |dx/dy|

Taking the derivative with respect to y:

dx/dy = 1/(2√(y+1))

Substituting everything we know into the formula:

fY(y) = (fX(1+√(y+1)) + fX(1-√(y+1))) / 2√(y+1)

Again, since X has a standard normal distribution, we know that fX(x) is the standard normal probability density function:

fX(x) = (1/√(2π)) * e^(-x^2/2)

Substituting this in, we get:

fY(y) = [(1/√(2π)) * e^(-(1+√(y+1))^2/2) + (1/√(2π)) * e^(-(1-√(y+1))^2/2)] / 2√(y+1)

Simplifying:

fY(y) = [[ fX(1+√(y+1)) + fX(1-√(y+1)) ]] / 2√(y+1)

I hope this helps clarify the calculations! Let me know if you have any further questions.

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