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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185
185
12.8 予測
な相関は統計的に有意です。低品質では、このカテゴリの残差が大きいために、そう
ではありません。ノイズに紛れることなく明確になるには、効果がもっと大きい必要
があります。
12.8
予測
時系列分析は、時間とともに変動するシステムの振る舞いを調べるため、時には説
明するために使われます。また予測にも使えます。
「
12.3
線形回帰」で扱った線形回帰は、予測に使われます。クラス
RegressionResults
には、説明変数を含む
DataFrame
を引数にとって、予測系列を返
す
predict
があります。コードは次のようになります。
def GenerateSimplePrediction(results, years):
n = len(years)
inter = np.ones(n)
d = dict(Intercept=inter, years=years)
predict_df = pandas.DataFrame(d)
predict = results.predict(predict_df)
return predict
results
は
RegressionResults
オブジェクトで、
years
は予測したい時間値の列で
す。この関数は、
DataFrame
を構成して、
predict
に渡し、結果を返します。
単独の最良の予測だけが望みなら、これで終わりです。しかし、ほとんどの目的に
は、誤差を定量化することが重要です。言い換えると、予測がどれだけ正確かを知り
たいのです。 ...
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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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