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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126
9 章 仮説検定
てられます。これを次のようにして検定できます。
class DiffStdPermute(DiffMeansPermute):
def TestStatistic(self, data):
group1, group2 = data
test_stat = group1.std() - group2.std()
return test_stat
これは、仮説が第一子の標準偏差が、ただ違うだけではなく、より大きいというも
のなので、片側検定です。
p
値は
0.09
で、統計的に有意ではありません。
9.5
相関を検定する
このフレームワークで、相関を検定することもできます。例えば、
NSFG
のデー
タセットでは、出生時体重と母親の年齢との相関が約
0.07
です。母親の年齢が高い
と赤ちゃんがより重くなるようです。しかし、この効果は偶然的ではないでしょうか。
検定統計量として、ピアソンの相関を使いますが、スピアマンの相関でも同様に働
くでしょう。正の相関を期待できるなら、片側検定を行います。しかし、そのような
理由がなければ、相関の絶対値を使った両側検定を行います。
帰無仮説は、母親の年齢と出生時体重との間に相関がないというものです。観察さ
れた値を混ぜ合わせることによって、年齢と出生時体重との分布が同じだという世界
をシミュレーションできますが、変数には関係がありません。
class CorrelationPermute(thinkstats2.HypothesisTest):
def TestStatistic(self, ...
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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.