June 2024
Intermediate to advanced
200 pages
4h 46m
Japanese
Content preview from Python2年生 データ分析のしくみ 第2版 体験してわかる！会話でまなべる！
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Chapter
6
178
21
LESSON
散布図の上に「回帰直線」を引いて、予測をしてみましょう。
散布図の上に
線を引いて予測
相関が強いと、だんだん線に近づいていくね。
予測？
線ということは、そこに法則性が見えているということだ。これを使え
ば予測ができるよ。
「横軸のある値のとき、縦軸はどんな値になるか」を線を使って予測でき
るということだ。
179
散布図の上に線を引いて予測
LESSON
21
データにはばらつきがありますが、誤差が最小になるように線を引くことができれば、
「横軸のある値（説明変数）のとき、縦軸はどんな値（目的変数）になるか」を予測できま
す。これを「回帰直線」といいます。
散布図の上に回帰直線を引くには、
seabornの「sns.regplot()」という命令が使えます。
• 必要なライブラリ：pandas、matplotlib、seaborn
•
命令
sns.regplot(data=df, x="横列", y="縦列",line_kws={"color":"
色"})
plt.show()
• 出力：散布図の上に回帰直線
「数学と理科」「 数学と社会」の、2つの散布図の上に回帰直線を表示してみましょう（リ
スト
6.5）。
でも、実際のデータにはばらつきがあるので、きっちりした線になるこ
とは ...
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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.
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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.