August 2015
Intermediate to advanced
272 pages
5h 46m
Japanese
Content preview from Think Stats 第2版 ―プログラマのための統計入門
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56
4 章 累積分布関数
この結果は自明なものではなかったかもしれませんが、
CDF
の定義から来る結果
そのものです。この図からわかるのは、標本の
10
％が
10
位パーセンタイルより下で、
20
％が
20
位パーセンタイルより下というようなことで、まったく期待どおりです。
したがって、
CDF
の形状が何であれ、パーセンタイル順位の分布は一様です。こ
の特性は、与えられた
CDF
の乱数を生成する単純かつ効率的アルゴリズムの基盤と
して、有用です。それは次のようになります。
•
範囲
0
〜
100
から一様にパーセンタイル順位を選ぶ。
•
Cdf.Percentile
を使って、選んだパーセンタイル順位に対応する分布での
値を見つける。
Cdf
は、
Random
という、このアルゴリズムの実装を提供しています。
# class Cdf:
def Random(self):
return self.Percentile(random.uniform(0, 100))
Cdf
には、整数
n
を取って、
Cdf
から無作為抽出した
n
個の値のリストを返す、
Sample
もあります。
4.8
パーセンタイル順位を比較する
パーセンタイル順位は、異なるグループに対する評価尺度を比較するときに役に立
ちます。例えば、陸上競技のレースは通常、年齢と性別でグループ分けされて行われ
ます。異なるグループに属する人同士の結果を比較するときには、レースのタイムを
パーセンタイル順位に変換して比較できます。
2
、
3
年前、マサチューセッツ州のデダムで開かれた、ジェイムズ・ジョイス ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.