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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L07.2 Conditional PMFs
https://youtu.be/T_Q3M_HV94w

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L07.3 Conditional Expectation & the Total Expectation Theorem
https://youtu.be/vJAG4EzSQZA

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L07.4 Independence of Random Variables
https://youtu.be/F6H50Hbulbk

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L07.5 Example
https://youtu.be/JsEvwRGa1JA

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L07.6 Independence & Expectations
https://youtu.be/R4nGGs0m7lo

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L07.7 Independence, Variances & the Binomial Variance
https://youtu.be/YQ26hzI4OJk

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L03.2 A Coin Tossing Example
https://youtu.be/rZKUmNvCjis

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위 그림에서 P(H2) 는 첫번째 어느 것을 선택하든 상관없이 두번째에 head가 나오는 경우를 말한다. 그러므로 첫번째 tail, head나온 경우의 경우수를 합해준다. 

P(H2 | H1) 의 경우는 첫번째에는 head가 나오고 그 다음에 두번째에 head가 나온 경우를 말한다. 

L03.3 Independence of Two Events
https://youtu.be/w423ypsUHf0

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disjoint는 independent가 아니다.

L03.4 Independence of Event Complements
https://youtu.be/JZkT3NU2mPM

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A 와 B가 independent이면 A 와 B complement도 independent이다.

L03.5 Conditional Independence
https://youtu.be/7B3cDe39lwY

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L03.7 Independence of a Collection of Events
https://youtu.be/UbQcqFH33G0

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내 추측:

A1과 A2, A1과 A3, A2와 A3가 pairwise independence라고 하더라도 A1, A2, A3가 다같이 indepedent하다고 할수 없다. 다음 그림을 보고 확인할것. 위의 그림에서 맨밑 빨간글씨는 오해를 불러 일으키는 것 같다.

L03.8 Independence Versus Pairwise Independence
https://youtu.be/aJXfyfQs2Mc

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pairwise independent라고 해서 independent라는 이야기는 아니다.