Variance Properties: Understanding Var(aX+bY+c) & Solving for Var(aX+bY)

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The variance of a linear combination Var(aX + bY + c) is indeed equal to Var(aX + bY), as adding a constant does not affect variance. The formula Var(aX + bY) can be applied, which is a^2Var(X) + b^2Var(Y) + 2abCov(X,Y). This confirms the initial reasoning presented in the discussion. The conclusion reached is accurate, and the topic is resolved.
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[SOLVED] Properties of variance

Would the Var(aX + bY + c) just be the Var(aX+bY) since adding a single number to the function doesn't change the variance. I would then be able to use the property:

Var(aX+bY)= a^2Var(X)+b^2Var(Y)+2abCov(X,Y)

Just wondering if anyone can confirm my reasoning here. Thanks.
 
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Correct.
 
Thank you, that's all I need. Mods can close.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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