January 2015
Beginner to intermediate
628 pages
13h 33m
Japanese
Content preview from 統計クイックリファレンス 第2版
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184
7 章 ピアソンの相関係数
7.3
ピアソンの相関係数
散布図は変数同士の関係を調べるために重要な視覚ツールである。有意性を調べたり、関
係性の統計的推測をしたいときもある。間隔や比尺度レベルの
2
変数において、関係性の最
も一般的な尺度はピアソンの相関係数であり、積率相関係数（
product-moment correlation
coefficient
）とも呼ばれ、母集団ではρ（ギリシャ文字のロー）、標本では
r
で書かれる。
ピアソンの
r
は
(
−
1, 1)
の範囲を動く。
0
は変数間に関係がないことを示し、絶対値は変
数同士の関係性の強さを示す（どちらの変数も図
7-5
や図
7-6
のような定数ではないと仮定
する）。ピアソンの
r
値は、データが非線形関係だと、誤解を生む。それこそがデータをグラ
フ化する理由となる。「強い」や「弱い」といった言葉には厳密な数値的定義はないが、「強い」
関係性は「弱い」関係性よりも線形関係である。つまり、強い関係性ではより多くの点が直
線の近くに集まっている。「強い」や「弱い」の定義には調査や実践の場による変動もあるため、
自身の分野での慣習を学ぶ必要がある。異なる強さがどんなものかについて、異なる
r
値の
散布図のいくつかの例を図
7-11
、図
7-12
、図
7-13
に示す。
図
7-11
散布図（
r
＝
0.84
）
0
0
10
20
30
40
50
5 10 15 20
25
-10
185
185
7.3 ピアソンの相関係数
図
7-12
散布図（
r
＝
0.55
）
図
7-13
散布図（
r
＝
0.09
）
相関係数はコンピュータソフトウェアで計算されることが多いが、手でも行える。ピアソ ...
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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.