Give an example to show that if not assuming independence of

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Assuming non-independence among random variables X1, X2, ..., Xn can lead to significant variance in their average. When all variables are equal, such as X1 = X2 = ... = Xn, the variance of their average becomes equal to the variance of a single variable, which is σ². This results in Var((1/n) * sum from k = 1 to n of Xk) equating to σ², demonstrating that the variance does not diminish with increasing n. Consequently, the variance of the average can exceed σ²/n, contradicting the assumption of independence. This example illustrates the critical impact of variable dependence on variance calculations.
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Give an example to show that if not assuming independence of X1, X2, ..., Xn it is possible to show that Var(1/n * sum from k = 1 to n of Xk) >> \sigma^2/n
 
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What do you get if X_1 = X_2 = ..., which is sort of the EXTREME example of non-independence?
 


Since X1 = X2 = ... = Xn.
This implies that Var((1/n)*nX1) = n2\sigma^2/n2 = \sigma^2 >> \sigma^2/n
 
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