[lln_clt] Update editorial suggestions #415
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Resolve most of the suggestions
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mmcky
reviewed
Apr 8, 2024
| @@ -438,7 +438,7 @@ Here $\stackrel { d } {\to} N(0, \sigma^2)$ indicates [convergence in distributi | |||
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| The striking implication of the CLT is that for **any** distribution with | |||
| finite [second moment](https://en.wikipedia.org/wiki/Moment_(mathematics)), the simple operation of adding independent | |||
| copies **always** leads to a Gaussian curve. | |||
| copies **always** leads to a Gaussian(Normal) curve. | |||
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| copies **always** leads to a Gaussian(Normal) curve. | |
| copies **always** leads to a Gaussian (Normal) curve. |
This is a really minor point but @jstac re: style I think there should be space between Gaussian and (Normal). Do you agree?
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Yes please, thanks @mmcky and @SylviaZhaooo
jstac
reviewed
Apr 8, 2024
lectures/lln_clt.md
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| @@ -78,7 +78,7 @@ print(X) | |||
| ``` | |||
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| In this setting, the LLN tells us if we flip the coin many times, the fraction | |||
| of heads that we see will be close to the mean $p$. | |||
| of heads that we see will be close to the mean $p$. We use $n$ to represent the number of times the coin is flipped. | |||
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@SylviaZhaooo Would you mind to put the second sentence on a separate line -- this is our (unusual) convention for the lectures.
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Perfect, many thanks @SylviaZhaooo |
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Resolve most of the comments in #413