From 81ddb93456bc5449602b0c1f76818a7fcfe1e251 Mon Sep 17 00:00:00 2001 From: jgzkan <08707@naver.com> Date: Tue, 24 Sep 2024 23:37:57 +0900 Subject: [PATCH] DBRS week2 -2 --- ...354\205\230]_2\354\243\274\354\260\250.md" | 92 ++++++++++++++++++ assets/img/DBRS_week2/img27.png | Bin 0 -> 163801 bytes assets/img/DBRS_week2/img28.png | Bin 0 -> 116429 bytes assets/img/DBRS_week2/img29.png | Bin 0 -> 160929 bytes assets/img/DBRS_week2/img30.png | Bin 0 -> 182541 bytes assets/img/DBRS_week2/img31.png | Bin 0 -> 165760 bytes assets/img/DBRS_week2/img32.png | Bin 0 -> 160487 bytes assets/img/DBRS_week2/img33.png | Bin 0 -> 317529 bytes assets/img/DBRS_week2/img34.png | Bin 0 -> 202480 bytes 9 files changed, 92 insertions(+) create mode 100644 assets/img/DBRS_week2/img27.png create mode 100644 assets/img/DBRS_week2/img28.png create mode 100644 assets/img/DBRS_week2/img29.png create mode 100644 assets/img/DBRS_week2/img30.png create mode 100644 assets/img/DBRS_week2/img31.png create mode 100644 assets/img/DBRS_week2/img32.png create mode 100644 assets/img/DBRS_week2/img33.png create mode 100644 assets/img/DBRS_week2/img34.png diff --git "a/_posts/2024-09-17-[\354\213\254\355\231\224\354\204\270\354\205\230]_2\354\243\274\354\260\250.md" "b/_posts/2024-09-17-[\354\213\254\355\231\224\354\204\270\354\205\230]_2\354\243\274\354\260\250.md" index 1681e37..d8abfe1 100644 --- "a/_posts/2024-09-17-[\354\213\254\355\231\224\354\204\270\354\205\230]_2\354\243\274\354\260\250.md" +++ "b/_posts/2024-09-17-[\354\213\254\355\231\224\354\204\270\354\205\230]_2\354\243\274\354\260\250.md" @@ -252,6 +252,98 @@ customer_join.to_csv("customer_join.csv", index=False) # 부교재 # +
+ +## [4장] 추론통계 ~ 신뢰구간 ## +
+ +### 4.1 추론통계를 배우기 전에 ### + +> 전수조사 vs 표본조사 + +- 확률분포와 실현값 + +![img27](assets/img/DBRS_week2/img27.png) + +- 데이터로부터 그 발생원의 확률분포 추정하기 + +- 모집단분포 모형화 + +- 무작위추출 + +- 무작위추출 방법: 단순무작위추출법 vs. 층화추출법 + +- 편향된 추출로는 올바른 추정이 어렵다. + +- 데이터 얻는 법 - 모집단에 대해 추정한 결과를 어느 정도 일반화할 수 있는가는, 각 분야 고유의 지식(도메인 지식)에 따라 달라진다. + +> 추론통계는 된장국을 국자로 맛보는 것과 같다?! + +![img28](assets/img/DBRS_week2/img28.png) + +
+ +### 4.2 표본오차와 신뢰구간 ### + +> 모집단과 데이터 사이의 오차 고려하기 + +> 표본오차: 표본평균과 모평균이 일치하지 않기에 이런 어긋남을 표본오차(sampling error)라고 한다. + +- 주사위의 표본오차 + +- 표본오차는 확률적으로 바뀐다 + +- 큰 수의 법칙: n이 커질수록 표본평균이 모평균에 한없이 가까워진다는 법칙 + +- 중심극한정리(Central Limit Theorem): 모집단이 어떤 분포이든 간에, n이 커질수록 표본평균의 분포는 정규분포로 근사할 수 있다. + +- 추정량: 모집단의 성질을 추정하는 데 사용하는 통계량 +- 일치추정량: n을 무한대로 했을 때, 모집단의 성질과 일치하는 추정량 +- 비편향추정량: 추정량의 평균값이 모집단의 성질과 일치할 때의 추정량 + +> 신뢰구간 + +![img29](assets/img/DBRS_week2/img29.png) + +- 신뢰구간의 해석: 00%의 확률로 이 구간에 모평균이 있다. = 모집단에서 표본을 추출하여 00% 신뢰구간을 구하는 작업을 100번 반복했을 때 평균적으로 그 구간에 모평균이 포함되는 것이 00번이라는 뜻이다. + +- 신뢰구간은 표본에서 구한 모평균의 추정값을 어느 정도 신뢰할 수 있는지를 나타낸다. + + + +## [5장] 가설검정 ## + +### 5.1 가설검정의 원리 ### + +> 가설검정의 구체적인 계산: 가설검정의 개념은 다양한 검정기법에서 공통이지만, p값의 계산 방법은 서로 다르다. + +가설검정에서는 p-value를 계산하여 가설을 지지하는지 여부를 판단한다. + +- 가설 검증하기 + +![img30](assets/img/DBRS_week2/img30.png) +![img31](assets/img/DBRS_week2/img31.png) + +![img32](assets/img/DBRS_week2/img32.png) +![img33](assets/img/DBRS_week2/img33.png) + +![img34](assets/img/DBRS_week2/img34.png) + + +### 5.2 가설검정 시행 ### + +이표본 t검정(two-sample t-test): 2개 집단 간의 평균값을 비교하는 검정 + + + + + + + + + + + diff --git a/assets/img/DBRS_week2/img27.png b/assets/img/DBRS_week2/img27.png new file mode 100644 index 0000000000000000000000000000000000000000..f128fcfa9a0043b4afafd7d349c799b96229c7bc GIT binary patch literal 163801 zcmd3NWmgdxtE+aMb?WT1W7JjU(2$9dp`f796y!f>KtaK{LqS0|Bf|fEvqd!z{Ck6T z*N~Hfs-7Y}`uhN|mQ<01f~reG`C|(A_Zi7WUiWVce7gT!&rxAC{$yg`2byi*tz`-CwPdUa(j#n83} zl|ZLHdTsgC(tgUDoQE;1mM~$RK=C9xwWybsXZQH&JnJRhR=v&h{n2wDcA>89I$J#Q zF8*=g54L_y3g5rKtLoOXt}BJT6b`u5u03VI1AiV`>XEGPD@%mKSCvKn-gcmxAg%nR z_F8m6QAxn?57LtjQbRx_b6%|zSlx~iHsG+79-S*!VlYmQ@;h=7bs)n!SEYS%&t_5w zLk5X-l=c#yZ8FilngGGhJp9aPe^M)UyOv8a-AL^SOf<5RZS7I5ZG7WD)8^sz6&5mO z!sRCFP*saU2r|C$*5JapEQRL-{~iHT+EH&*zqRH=3ZMmpVBG-HfW1QltTL#imcEbt zG@`oIA%w7l2c3h?fgwoAm9@qbqJTjySID>q0L_c!hX{Gt4~W}&T67GY_RxR}ZV_v4S-n||~w zvOZ$|6}&{tjir~_y)OyMHM}WKx@TIRl{WFEKOIm*Xi?~CXufxHZJh=BCt1m~-BkwL 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