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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152
11 章 回帰
同様ですが、複数の説明変数も扱えるように一般化されています。詳細は、
https://
en.wikipedia.org/wiki/Ordinary_least_squares
にあります
†
。
11.1
StatsModels
前章では、
thinkstats2.LeastSquares
を説明しましたが、これは線形単回帰の実
装で読みやすいように意図したものでした。重回帰では、
StatsModels
に切り替えま
す。これは、いくつかの回帰形式だけでなく他の解析も提供する
Python
のパッケー
ジです。数値計算環境として
Anaconda
を使っているなら、すでに
StatsModels
があ
るはずです。そうでないならインストールする必要があるでしょう。
例えば、前章のモデルを
StatsModels
で次のように実行します。
import statsmodels.formula.api as smf
live, firsts, others = first.MakeFrames()
formula ='totalwgt_lb ~ agepreg'
model = smf.ols(formula, data=live)
results = model.fit()
statsmodels
には、
2
つのインターフェイス（
API
）があります。「式（
formula
）」
API
は、従属変数と説明変数とを文字列を使って識別します。
patsy
という構文を使
います。この例では、
~
演算子が左の従属変数と右の説明変数とを分けています。 ...
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