ols回归结果分析表python_OLS回归结果Python中coef的VIF

我试图按coef打印VIF(方差膨胀因子)。然而，我似乎找不到任何来自statsmodels的文档来说明如何做到这一点？我有一个需要处理的n个变量的模型，所有变量的多重共线性值无助于删除共线性最高的值。在

这看起来是个答案

但是我如何在这个工作簿上运行它呢。在

下面是代码和摘要输出，这也是我现在所在的地方。在import pandas as pd

import matplotlib.pyplot as plt

import statsmodels.formula.api as smf

# read data into a DataFrame

data = pd.read_csv('somepath', index_col=0)

print(data.head())

#multiregression

lm = smf.ols(formula='Sales ~ TV + Radio + Newspaper', data=data).fit()

print(lm.summary())

OLS Regression Results

==============================================================================

Dep. Variable: Sales R-squared: 0.897

Model: OLS Adj. R-squared: 0.896

Method: Least Squares F-statistic: 570.3

Date: Wed, 15 Feb 2017 Prob (F-statistic): 1.58e-96

Time: 13:28:29 Log-Likelihood: -386.18

No. Observations: 200 AIC: 780.4

Df Residuals: 196 BIC: 793.6

Df Model: 3

Covariance Type: nonrobust

==============================================================================

coef std err t P>|t| [95.0% Conf. Int.]

------------------------------------------------------------------------------

Intercept 2.9389 0.312 9.422 0.000 2.324 3.554

TV 0.0458 0.001 32.809 0.000 0.043 0.049

Radio 0.1885 0.009 21.893 0.000 0.172 0.206

Newspaper -0.0010 0.006 -0.177 0.860 -0.013 0.011

==============================================================================

Omnibus: 60.414 Durbin-Watson: 2.084

Prob(Omnibus): 0.000 Jarque-Bera (JB): 151.241

Skew: -1.327 Prob(JB): 1.44e-33

Kurtosis: 6.332 Cond. No. 454.

==============================================================================