请问如果得到Logistic Regression的 P 值和置信区间？

以下代码可以实现线性回归（LinearRegression）的P值，Standard Errors。
但应用在（Logistic Regression）的P值和Standard Errors是错误的。
求大神指教：做分类统计检验时，Logistic Regression怎么得到像 SPSS、SAS 等软件中的 P 值和置信区间？

import pandas as pd
import numpy as np
from sklearn import datasets, linear_model
from sklearn.linear_model import LinearRegression
import statsmodels.api as sm
from scipy import stats
lm = LinearRegression()
lm.fit(X,y)
params = np.append(lm.intercept_,lm.coef_)
predictions = lm.predict(X)

newX = pd.DataFrame({"Constant":np.ones(len(X))}).join(pd.DataFrame(X))
MSE = (sum((y-predictions)**2))/(len(newX)-len(newX.columns))

# Note if you don't want to use a DataFrame replace the two lines above with
# newX = np.append(np.ones((len(X),1)), X, axis=1)
# MSE = (sum((y-predictions)**2))/(len(newX)-len(newX[0]))

var_b = MSE*(np.linalg.inv(np.dot(newX.T,newX)).diagonal())
sd_b = np.sqrt(var_b)
ts_b = params/ sd_b

p_values =[2*(1-stats.t.cdf(np.abs(i),(len(newX)-1))) for i in ts_b]

sd_b = np.round(sd_b,3)
ts_b = np.round(ts_b,3)
p_values = np.round(p_values,3)
params = np.round(params,4)

myDF3 = pd.DataFrame()
myDF3["Coefficients"],myDF3["Standard Errors"],myDF3["t values"],myDF3["Probabilites"] = [params,sd_b,ts_b,p_values]
print(myDF3)

代码来自：www.baidu.com/link?url=m6xeL0lx5vw...