October 2018
Beginner to intermediate
328 pages
7h 58m
Korean
Content preview from 데이터 과학을 위한 통계: 데이터 분석에서 머신러닝까지 50가지 핵심 개념
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134
데이터 과학을 위한 통계
여러 요인과 그 효과를 모델링할 수 있는 회귀와 로지스틱 회귀 같은 완전한 통계 모델을 위한
첫걸음이 바로 이
ANOVA
, 그리고 이원
ANOVA
라고 할 수 있다 (
4
장 참고 ).
주요 개념
•
ANOVA
는 여러 그룹의 실험 결과를 분석하기 위한 통계적 절차이다.
•
A
/
B
검정과 비슷한 절차를 확장하여 그룹 간 전체적인 편차가 우연히 발생할 수 있는 범위 내에 있는지를
평가하기 위해 사용한다.
•
ANOVA
의 결과 중 유용한 점 중 하나는 그룹 처리, 상호작용 효과, 오차와 관련된 분산의 구성 요소들을
구분하는 데 있다.
3.8.3
더 읽을 거리
• 피터 브루스의 『
Introductory
Statistics
and
Analytics
:
A
Resampling
Perspective
』(
Wiley
,
2014
)에
ANOVA
챕터를 참고하자.
• 조지
W
. 콥의 『
Introduction
to
Design
and
Analysis
of
Experiments
』(
Wiley
,
2008
)은 이와 관련
된 주제들을 종합적이면서도 재미있게 잘 정리했다.
3.9
카이제곱검정
웹 테스트 시, 종종 단순한
A
/
B
검정을 넘어 동시에 여러 가지 처리를 한 번에 테스트할 필요
가 있다.
카이제곱검정
chi
-
square
test
은 횟수 관련 데이터에 주로 사용되며 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.