6.3, due on November 10

The central limit theorem is really cool, because until now I had no idea why so many real-life examples had a normal distribution. It's weird to think that the sum of any number and type of distributions eventually reaches a normal distribution. The statement of the theorem itself is a little confusing, and I'd like to go over how all the variables interact and what each of the equations mean. But on the whole I find it very interesting how powerful this theorem is.

Also, I struggle to see why certain things are put into the normal distribution. Are they \mu and \sigma^2 for each distribution? If so, why? (E.g. N(np,np(1-p)) for the binomial distribution.)

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