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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14
■
1
章 探索的データ分析
Python
では、加重平均は
NumPy
に用意されている。加重中央値には、パッケージ
wquantiles
（
https://pypi.org/project/wquantiles/
）が 使 え る 。
（
Python
）
np.average(state['Murder.Rate'], weights=state['Population'])
wquantiles.median(state['Murder.Rate'], weights=state['Population'])
この場合には、加重平均と加重中央値はほぼ同じ値となる。
基本事項
3
■
代表値の基本的な指標は平均値だが、極端な値（外れ値）の影響を受けやすい。
■
他の指標（中央値、トリム平均）の方が外れ値や異常な分布の影響を受けにく
く、より頑健である。
1.3.4
さらに学ぶために
●
Wikipedia
の
central tendency
（
https://en.wikipedia.org/wiki/Central_tendency
）
は、代表値に関するさまざまな指標を説明している。
●
テューキーの古典的な『
Exploratory Data Analysis
』［
Tuke y-1977
］は い ま だ に 広 く
読まれている。
1.4
散らばりの推定
位置は、特徴量を要約する次元の
1
つにすぎない。第
2
の次元である散らばりは、広
がりとも呼ばれるが、データ値がまとまっているか広がっているかを測る。散らばりは
統計学の
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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.