May 2022
Beginner to intermediate
448 pages
13h 7m
Korean
book
밑바닥부터 시작하는 데이터 과학 데이터 분석을 위한 파이썬 프로그래밍과 수학·통계 기초
by Joel Grus, Hangyeol Kim, Seongju Ha, Eunjung Park
Content preview from 밑바닥부터 시작하는 데이터 과학 데이터 분석을 위한 파이썬 프로그래밍과 수학·통계 기초
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from Scratch
101
hi = normal
_
upper
_
bound(0.95, mu
_
0, sigma
_
0)
#
결괏값은
526 (< 531,
분포
상위
부분에
더
높은
확률을
주기
위해서
)
type
_
2
_
probability = normal
_
probability
_
below(hi, mu
_
1, sigma
_
1)
power = 1 - type
_
2
_
probability # 0.936
이 가설검정은 더 이상
X
가
469
보다 작을 때
H
0
를 기각하는 게 아니라(
H
1
이 참
이라면 이는 거의 발생하지 않을 것이다)
X
가
526
에서
531
사이일 때
H
0
를 기각
하기 때문에(
H
1
이 참이라면 이는 가끔 발생할 것이다) 전보다 검정력이 더 좋아
졌다고 볼 수 있다.
7.3
p-value
가설을 바라보는 또 다른 관점은
p
-
value
이다. 이는 어떤 확률값을 기준으로 구
간을 선택하는 대신에,
H
0
가 참이라고 가정하고 실제로 관측된 값보다 더 극단
적인 값이 나올 확률을 구하는 것이다.
동전이 공평한지를 확인해 보기 위해 양측검정을 해 보자.
def two
_
sided
_
p
_
value(x: float, mu: float = 0, sigma: float = 1) -> float:
"""
mu(
평균
)
와
sigma(
표준편차
)
를
따르는
정규분포에서
x
같이
극단적인
값이
나올
확률은 ...
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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.