November 2020
Intermediate to advanced
396 pages
8h 34m
Japanese
book
データサイエンスのための統計学入門 第2版 ―予測、分類、統計モデリング、統計的機械学習とR/Pythonプログラミング
by Peter Bruce, Andrew Bruce, Peter Gedeck, 黒川 利明, 大橋 真也
Content preview from データサイエンスのための統計学入門 第2版 ―予測、分類、統計モデリング、統計的機械学習とR/Pythonプログラミング
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132
■
3
章 統計実験と有意性検定
カイ二乗統計量の値
図3-7 さまざまな自由度でのカイ二乗分布
3.9.3
フィッシャーの正確確率検定
カイ二乗分布は、度数が非常に小さい場合（一桁、特に
5
以 下 ）を 除 い て は 、 シ ャ ッ
フルしたリサンプリング検定の良い近似となる。度数が小さい場合には、リサンプリン
グ手続きでより正確な
p
値が得られる。実際、ほとんどの統計ソフトには、すべての可
能な再配置（並べ替え）を数え上げて、その度数を変えて、観測結果が正確にどのくら
い極端な値になっているかを決定する手続きが用意されている。これは、偉大な統計
学者ロナルド・
A
・フィッシャーにちなんでフィッシャーの正確確率検定と呼ばれる。
フィッシャーの正確確率検定を行う
R
コードは次のように単純だ。
（
R
）
fisher.test(clicks)
Fisher's Exact Test for Count Data
data: clicks
p-value = 0.4824
alternative hypothesis: two.sided
この
p
値は、リサンプリングを使って得られた
p
値の
0.4853
に非常に近い。
非常に小さい度数があるが他はかなり大きい場合（例：コンバージョン率の分母）、
可能な並べ替えすべてを計算するのが難しいので、正確確率検定ではなくシャッフ
ルした並べ替え検定が必要となる。先ほどの
R
関数には、この近似を使うかどうか
3.9
カイ二乗検定
■
133
（
simulate.p.value=TRUE
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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.