多元线性回归获得最佳拟合

Question

我正在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())

结果如下：

                            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数据帧由从1到149的多个列组成。有没有一种方法可以循环遍历Output数据帧中的所有149列，并最终在例如R-squared上显示最佳匹配和最差匹配？或者获取变量B的最大coef？你知道吗