05/31/06
12.6.2 Reading
1. Difficult - So, is the CLT saying basically that as the sample gets larger, the better the approximation to F(x). Isn't this the same as the "Weak Law of Large Numbers"? Or wait, it says that F(x) is the dist. func. of SND so, how is it saying that it is approaching SND? How do we know that (I know it says it is not proven here)? Couldn't there theoretically be a skewed graph which would have the same mean and variance as a graph as a normally distributed graph? Isn't a skewed graph not SND?
2. Reflective - The approximating "function" of the CLT reminds me of... was is Poisson?... one of the distributions we learned about was approximating... I think it also depended on a very large n.
1. Difficult - So, is the CLT saying basically that as the sample gets larger, the better the approximation to F(x). Isn't this the same as the "Weak Law of Large Numbers"? Or wait, it says that F(x) is the dist. func. of SND so, how is it saying that it is approaching SND? How do we know that (I know it says it is not proven here)? Couldn't there theoretically be a skewed graph which would have the same mean and variance as a graph as a normally distributed graph? Isn't a skewed graph not SND?
2. Reflective - The approximating "function" of the CLT reminds me of... was is Poisson?... one of the distributions we learned about was approximating... I think it also depended on a very large n.
posted by Tina Marcroft at 9:41 PM
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