我正在使用以下代码进行多元线性回归：
import statsmodels.formula.api as sm
df = pd.DataFrame({"A":Output['10'],
"B":Input['Var1'],
"G":Input['Var2'],
"I":Input['Var3'],
"J":Input['Var4'],
res = sm.ols(formula="A ~ B + G + I + J", data=df).fit()
print(res.summary())
具有以下结果：
OLS Regression Results
==============================================================================
Dep. Variable: A R-squared: 0.562
Model: OLS Adj. R-squared: 0.562
Method: Least Squares F-statistic: 2235.
Date: Tue, 06 Nov 2018 Prob (F-statistic): 0.00
Time: 09:48:20 Log-Likelihood: -21233.
No. Observations: 6961 AIC: 4.248e+04
Df Residuals: 6956 BIC: 4.251e+04
Df Model: 4
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 21.8504 0.448 48.760 0.000 20.972 22.729
B 1.8353 0.022 84.172 0.000 1.793 1.878
G 0.0032 0.004 0.742 0.458 -0.005 0.012
I -0.0210 0.009 -2.224 0.026 -0.039 -0.002
J 0.6677 0.061 10.868 0.000 0.547 0.788
==============================================================================
Omnibus: 2152.474 Durbin-Watson: 0.308
Prob(Omnibus): 0.000 Jarque-Bera (JB): 5077.082
Skew: -1.773 Prob(JB): 0.00
Kurtosis: 5.221 Cond. No. 555.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
但是，我的Output数据帧由1到149的多列组成。有没有一种方法可以循环遍历Output数据帧中的所有149列，最后显示出最佳拟合和最差拟合例如R-squared上？还是为变量coef获得最大的B？