6.2, due on November 8

The proof of Markov's Inequality didn't make a lot of sense to me, probably because I'm not very familiar with indicator functions. The result of Chebyshev's Inequality is pretty neat, given that it works for any distribution and is intuitive to think about. I don't have a good intuition for why the law of large numbers sometimes doesn't give us any information, as in Nota Bene 6.2.10.

I was surprised by how simple the proof for the weak law of large numbers was. This could be very useful for determining how large we need our sample size to be to get the accuracy we want.


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