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5. Recall the heuristics when applying the CLT tell us that when the sample size is n 30 the
sample
mean approximately follows the normal distribution. In this question you are asked to come up
with
counter-examples, i.e., examples that completely violate this rule of thumb.
a. Construct a random variable (i.e., create a vector of values, as we did in class) where the
population
distribution is not normal, but that when n = 5 the sample mean is already very close to normal.
b. Construct another random variable such that when you obtain random samples of size n =
2000,
the distribution of the sample mean, .X 2000 , does not approximate at all the normal
distribution.
For both parts, use the function clt() to obtain graphs that justify your findings.

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5- Recall the heuristics when applying the CLT tell us that when the s.pdf

  • 1. 5. Recall the heuristics when applying the CLT tell us that when the sample size is n 30 the sample mean approximately follows the normal distribution. In this question you are asked to come up with counter-examples, i.e., examples that completely violate this rule of thumb. a. Construct a random variable (i.e., create a vector of values, as we did in class) where the population distribution is not normal, but that when n = 5 the sample mean is already very close to normal. b. Construct another random variable such that when you obtain random samples of size n = 2000, the distribution of the sample mean, .X 2000 , does not approximate at all the normal distribution. For both parts, use the function clt() to obtain graphs that justify your findings.