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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157
157
11.4 データマイニング
この表の列は
3
つの説明変数と決定係数
R
2
です。各項目は推定パラメータで、そ
の後ろのカッコ内は
p
値です。後ろにアスタリスク（
*
）があるのは
p
値が
0.001
よ
り小さいことを意味します。
結論として、出生時体重の見かけ上の差は、少なくとも部分的に、母親の年齢差と
して説明されます。モデルに母親の年齢を入れると、
isfirst
の影響は小さくなり、
残った影響は偶然によるものかもしれません。
この例では、母親の年齢が制御変数（
control variable
）として働きます。モデル
に
agepreg
を含めると、初産の母親とそれ以外の母親との年齢差が「制御」されて、
isfirst
の影響が（もしあったとしても）分離されます。
11.4
データマイニング
ここまでは、説明に回帰モデルを使ってきました。例えば、前節では、出生時体重
の見かけ上の差が実際には母親の年齢差によることを発見しました。しかし、モデル
の
R
2
値は大変低く、予測能力はあまりないことを意味します。本節では、さらに精
度を上げます。
同僚が出産を控えていて、会社で赤ちゃんの体重を当てる賭けがあると仮定しま
しょう（このような賭け（
betting pool
）については、
https://en.wikipedia.org/
wiki/Betting_pool
を参照してください）。
さらに、この賭けに本当に勝ちたいと仮定しましょう。勝つ確率を増やすにはどう
すればよいでしょうか。
NSFG
のデータセットには、妊娠に関して
244
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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.
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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.