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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164
11 章 回帰
>>> like = np.prod(likes)
0.18
beta
の先ほどの値について、データの尤度は
0.18
です。ロジスティック回帰の
目標は、尤度を最大化するパラメータを見つけることです。そのために、ほとんど
の統計パッケージは、ニュートン法に似た反復ソルバーを使っています（
https://
en.wikipedia.org/wiki/Logistic_regression#Model_fitting
参照）
†
。
11.8
実装
StatsModels
は、
logit
と呼ばれるロジスティック回帰を実装しています。この名
前は、確率を対数オッズに変換する関数の名前から来ています。使用法を示すために、
性別比に影響する変数を探してみます。
再度、
NSFG
データをロードして、
30
週を超える妊娠データを選びます。
live, firsts, others = first.MakeFrames()
df = live[live.prglngth>30]
logit
では、従属変数が（論理型ではなく）二値である必要があるので、
astype(int)
を使って二値整数に変換した、
boy
という名の新しい列を作ります。
df['boy'] = (df.babysex==1).astype(int)
性別比に影響するものとして見つかった因子には、両親の年齢、出生順、人種、社
会的地位が含まれます。ロジスティック回帰を使って、これらの影響が
NSFG
デー
タに現れるか調べることができます。母親の年齢から始めましょう。 ...
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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.