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@pandaLA

# 標準化回帰係数。Standardize regression coefficient。python。

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ロジスティック回帰（教師有り学習）
http://www.tsjshg.info/udemy/Lec76-79.html

http://www.u-gakugei.ac.jp/~kishilab/spss-mra.htm

よくお世話になっているサイト。やはり記載が親切。
https://bellcurve.jp/statistics/blog/14077.html

１、説明変数を先に標準化（平均０、分散1)して、回帰分析を行い、回帰係数を求める。
２、回帰分析を用いた後に、偏回帰係数から標準化回帰係数を求める。
https://www.weblio.jp/content/%E6%A8%99%E6%BA%96%E5%8C%96%E5%81%8F%E5%9B%9E%E5%B8%B0%E4%BF%82%E6%95%B0

が、それが正しいやり方には見えない）
http://enhancedatascience.com/2017/04/23/tutorial-logistic-regression-python/
https://github.com/AntoineGuillot2/Logistic-Regression-Python/

https://think-lab.github.io/d/205/

こちらは論文へのリンク
http://www.ccitonline.org/jking/homepage/standardized_paper.doc

Appendix A
A Microsoft Excel Function for Calculating a Standardized
Logistic Regression Coefficient

Cell A1 = Enter the mean predicted probability for the dataset.
Cell A2 = Enter the unstandardized beta weight for X.
Cell A3 = Enter the sample standard deviation for X.

Cell A4: Calculate a standardized coefficient for X by typing:

=(1/(1+EXP(-(LN(A1/(1-A1))+0.5*A2*A3))))-(1/(1+EXP(-(LN(A1/(1-A1))-0.5*A2*A3))))

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