November 2020
Intermediate to advanced
396 pages
8h 34m
Japanese
book
データサイエンスのための統計学入門 第2版 ―予測、分類、統計モデリング、統計的機械学習とR/Pythonプログラミング
by Peter Bruce, Andrew Bruce, Peter Gedeck, 黒川 利明, 大橋 真也
Content preview from データサイエンスのための統計学入門 第2版 ―予測、分類、統計モデリング、統計的機械学習とR/Pythonプログラミング
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4.5
回帰式の解釈
■
175
多 重 共 線 性（
multicollinearity
）
予測変数が完全または準完全相関だと、回帰が不安定または計算不能にな
る こ と（ 関 連 語 ： 共 線 性 ）
交絡変数（
confounding variable
）
除外すると、回帰式が誤った関係を導いてしまう、重要な予測変数。
主 効 果（
main effect
）
他の変数とは独立な、予測変数と目的変数との関係。
交互作用（
interactions
）
複数の予測変数と応答変数との相互依存関係。
4.5.1
予測変数の相関
重回帰においては、予測変数が互いに相関していることも多い。例えば、「4.2.4 モ
デル選択と段階的回帰」で適合した
step_lm
モデルの回帰係数を調べてみよう。
（
R
）
step_lm$coefficients
(Intercept) SqFtTotLiving Bathrooms
6.178645e+06 1.992776e+02 4.239616e+04
Bedrooms BldgGrade PropertyTypeSingle Family
-5.194738e+04 1.371596e+05 2.291206e+04
PropertyTypeTownhouse SqFtFinBasement YrBuilt
8.447916e+04 7.046975e+00 -3.565425e+03
（
Python
）
print(f'Intercept: {best_model.intercept_:.3f}') ...
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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.