正太分布函数相关的一些概率程序

 

 

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# -*- coding:utf-8 -*- import numpy as np import scipy.stats as stats import matplotlib.pyplot as plt   import sys reload(sys) sys.setdefaultencoding('utf8')   #第一步画分布图像 mean = 36.66 std = 5.215   # 根据均值、标准差,求指定范围的正态分布概率值 def normfun(x):   pdf = stats.norm.cdf(x,mean,std)   return pdf   X = np.arange(0, int(2*mean), 0.01) Y= [normfun(x) for x in X] # 设定 y 轴，载入刚才的正态分布函数   plt.plot(X, Y) # 这里画出理论的正态分布概率曲线 plt.show()   #分别求千分之一 万分之一 十万分之一  百万分之一的特   p= stats.norm.ppf(0.001,mean,std) str = "qian fenzhiyi: " print(str,round(p,3)) p= stats.norm.ppf(0.0001,mean,std) str = "wan fenzhiyi: " print(str,round(p,3)) p= stats.norm.ppf(0.0001,mean,std) str = "shiwan fenzhiyi: " print(str,round(p,3)) p= stats.norm.ppf(0.0001,mean,std) str = "baiwan fenzhiyi: " print(str,round(p,3))   #求置信度区间 #65% conf_intveral = stats.norm.interval(0.65, loc=mean, scale=std) intveral = [round(x,3)for x in conf_intveral] print("65%: ",intveral)   conf_intveral = stats.norm.interval(0.95, loc=mean, scale=std) intveral = [round(x,3)for x in conf_intveral] print("95%: ",intveral)   conf_intveral = stats.norm.interval(0.99, loc=mean, scale=std) intveral = [round(x,3)for x in conf_intveral] print("99%: ",intveral)   conf_intveral = stats.norm.interval(0.999, loc=mean, scale=std) intveral = [round(x,3)for x in conf_intveral] print("99.9%: ",intveral)