October 2025
Intermediate to advanced
328 pages
3h 56m
Japanese
Content preview from Think Stats 第3版 ―Pythonで学ぶ統計学入門
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3章確率質量関数
2章では、FreqTabオブジェクトを使って分布を表現しました。このオブジェクトは値の集合とその度数、つまり各値が現れる回数でできています。この章では、分布を記述する別の方法として、確率質量関数（PMF）を紹介します。
確率質量関数を表現するために、値の集合とその確率を含んだPmfと呼ばれるオブジェクトを使います。これは、値の集合とその確率を含んでいます。Pmfオブジェクトを使って、分布の平均値と分散、そして分布が左右どちらに歪んでいるかを示す歪度を計算します。最後に、「検査のパラドックス」と呼ばれる現象によって、標本自体が分布の偏った見方が生じる原因となることについて探ります。
3.1 確率質量関数
Pmfオブジェクトは、度数の代わりに確率を含むFreqTabのようなものです。したがって、Pmfを作成する1つの方法は、FreqTabから始めることです。例として、短いシーケンスにおける値の分布を表すFreqTabを以下に示します。
from empiricaldist import FreqTabftab = FreqTab.from_seq([1, 2, 2, 3, 5])ftab
| 度数 | |
|---|---|
| 1 | 1 |
| 2 | 2 |
| 3 | 1 |
| 5 | 1 |
度数の和が元のシーケンスのサイズとなっています。
n = ftab.sum()n
5
度数をnで割ると、回数ではなく割合を表します。
pmf = ftab / npmf
| 確率 | |
|---|---|
| 1 | 0.2 |
| 2 | 0.4 |
| 3 | 0.2 |
| 5 | 0.2 |
この結果は、シーケンス内の値の20%が1、40%が2、ということを示しています。
元のシーケンスからランダムな値を選ぶと、値1を選ぶ確率は0.2、値2を選ぶ確率は0.4、といった具合です。 ...
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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.