L12.2 The Sum of Independent Discrete Random Variables
https://youtu.be/zbu8KQx9bqM

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L12.3 The Sum of Independent Continuous Random Variables
https://youtu.be/d2M4LNSeIn4

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L12.4 The Sum of Independent Normal Random Variables
https://youtu.be/aGbP_7yAiEk

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covariance 와 correlation Coefficient에 대해 쉽게 설명한 한국어 블로그 링크

https://m.blog.naver.com/PostView.nhn?blogId=sw4r&logNo=221025662499&proxyReferer=https%3A%2F%2Fwww.google.com%2F

L12.5 Covariance
https://youtu.be/K2Tlj27nkjs

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L12.6 Covariance Properties
https://youtu.be/RQKJBpaCCeo

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L12.7 The Variance of the Sum of Random Variables
https://youtu.be/GH7dwoXSD0s

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L12.8 The Correlation Coefficient
https://youtu.be/HTs6Zhc2S1M

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L12.9 Proof of Key Properties of the Correlation Coefficient

https://youtu.be/uxVRfj60z98

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L11.2 The PMF of a Function of a Discrete Random Variable
https://youtu.be/NRnAuKxx6XA

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L11.3 A Linear Function of a Continuous Random Variable
https://youtu.be/11iF2ovjKOg

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L11.4 A Linear Function of a Normal Random Variable
https://youtu.be/eFDU7t6Jxzc

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L11.5 The PDF of a General Function
https://youtu.be/X-AzW70e2M0

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L11.6 The Monotonic Case
https://youtu.be/PaI-oaOBHKU

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상승곡선일때를 보여주고 있다.

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하강 곡선일때를 보여주고 있다.

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상승 하강 곡선에 대한 공식을 하나로 합친 경우이다.

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L11.7 The Intuition for the Monotonic Case
https://youtu.be/zM39sZL9oGE

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위 그림에서 우상단의 내용을 살펴보면

델타2는 델타1에 g(x)를 미분해서 얻어진 기울기를 곱한 값만큼 변하게 된다. 또 거꾸로 델타1은 델타2에 h(x)를 미분해서 얻어진 기울기를 곱한 값만큼 변하게 된다. 

L11.8 A Nonmonotonic Example
https://youtu.be/uFx7fWujWsU

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L11.9 The PDF of a Function of Multiple Random Variables

https://youtu.be/X-krLprDrOI

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