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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62
5 章 分布をモデル化する
thinkplot.Cdf(cdf, complement=True)
thinkplot.Show(xlabel='minutes',
ylabel='CCDF',
yscale='log')
引数
complement=True
で、
thinkplot.Cdf
は、プロットする前に相補
CDF
を計算
します。そして、
yscale='log'
で、
thinkplot.Show
は、
y
軸を対数目盛にします。
図
5-2
の右側に結果を示します。正確な直線ではありませんが、これは指数分布
がこのデータの完全なモデルでないことを示します。
1
日のどの時間でも新生児が生
まれる確からしさは等しい、という基盤となった前提は厳密には成り立たないので
しょう。それでも、このデータセットを指数分布でモデル化するのは妥当でしょう。
この単純化で、分布を
1
つの母数で要約できるからです。
母数
λ
は比率として解釈できます。すなわち、単位時間に平均して事象の起こる
回数です。この例では、
44
人の新生児が
24
時間で生まれたので、比率は分当たり
λ
＝
0.0306
人です。指数分布の平均は
1/
λ
になるので、出産間の平均時間は
32.7
分です。
5.2
正規分布
正規分布（
normal distribution
）はガウス分布とも呼ばれ、少なくとも近似的に多
くの現象を記述できるので、非常によく使われています。実は、正規分布がなぜよく
現れるのかにはまともな理由があり、「
14.4
中心極限定理」で学びます。
正規分布は、
2
つの母数、平均μと標準偏差σで特徴付けられます。μ＝ ...
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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.