Learn How to Calculate Var(Y) and Var(Y2) with Statistics Equation Help"

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To calculate Var(Y^2), the formula is given as Var(Y^2) = E(Y^4) - (E(Y^2))^2. The discussion emphasizes that this follows the same principles used for any random variable. A correction was acknowledged regarding the initial formula presented. The importance of understanding these calculations in statistics is highlighted. Accurate computation of variance is crucial for statistical analysis.
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If Var(Y)=\sigma^2, then Var ( Y2 ) = ?
 
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vary(Y^2) = E(Y^4) - E(Y^2) <br />

(same basic patten for the random variable Y^2 as for any other random variable).
 


statdad said:
vary(Y^2) = E(Y^4) - E(Y^2) <br />

(same basic patten for the random variable Y^2 as for any other random variable).
should be
vary(Y^2) = E(Y^4) - (E(Y^2))^2 <br />
 


Crap - yes, mathman is correct. Time to stop posting for a while.
 

Thread 'Hypothesis testing: Defining H0, HA hypotheses so that ( H_A)_A' makes sense'

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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