Find μ and σ^2 of Y when X~N(2,4)

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SUMMARY

When X follows a normal distribution X ~ N(2,4), the transformation Y = -X - 1 results in Y also being a normal random variable, specifically Y ~ N(μ,σ^2). The expected value of Y is calculated as E(Y) = -E(X) - 1, yielding μ = -3. The variance of Y remains unchanged from that of X, thus σ^2 = 4.

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If X ~ N(2,4) then Y = -X-1 is also a normal random variable such that Y ~ N(μ,σ^2).
Find μ and σ^2.

I know that E(X) = 2 and Var(X) = 4.

E(Y) = -E(X) - 1
E(Y) = -2 - 1 = -3
So I found μ, but I'm not sure how to find the variance. Help?
 
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dlee said:
If X ~ N(2,4) then Y = -X-1 is also a normal random variable such that Y ~ N(μ,σ^2).
Find μ and σ^2.

I know that E(X) = 2 and Var(X) = 4.

E(Y) = -E(X) - 1
E(Y) = -2 - 1 = -3
So I found μ, but I'm not sure how to find the variance. Help?

Welcome to MHB, dlee! :)

There are a couple of basic properties for expectations and variances as you can see here.

In particular $\sigma^2(X + a)=\sigma^2(X)$ and $\sigma^2(aX)=a^2\sigma^2(X)$, where $a$ is some arbitrary constant.
 
AH the variance is 4! Thank you!
 

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