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Let X1, X2, …, Xn be i.i.d. lognormal random variables, with parameters μ and σ^2. That is, ln(Xi) ~ N(μ, σ^2).

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Final answer:

The subject of this question is Mathematics and it is at a College level. The question involves the concept of i.i.d. lognormal random variables and the Central Limit Theorem.

Step-by-step explanation:

The question involves the concept of i.i.d. lognormal random variables and the Central Limit Theorem. In this case, we have a set of lognormal random variables X1, X2, ..., Xn with parameters μ and σ^2. These random variables follow a lognormal distribution, where the natural logarithm of each Xi is normally distributed with mean μ and variance σ^2.

As n increases, the sample mean of these lognormal random variables tends to follow a normal distribution with mean μ and a variance of σ^2/n. This result is a consequence of the Central Limit Theorem.

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