所以我已经粘贴了我的完整代码供您参考,我想知道ppf和cdf在这里的用途是什么?你能解释一下吗?我做了一些研究,发现ppf (百分比点函数)是cdf (累积分布函数)的逆,如果真的是这样,如果我将ppf和cdf分别替换为1/cdf和1/ppf,这段代码不是应该起作用吗?
请给我解释一下,这两者之间的区别。以及如何以及何时使用
顺便说一句,这是假设检验。很抱歉有这么多的评论,只是为了将来的参考而解释一切的习惯。(如果我的任何评论是错误的,请指出)
ball_bearing_radius = [2.99, 2.99, 2.70, 2.92, 2.88, 2.92, 2.82, 2.83, 3.06, 2.85]
import numpy as np
from math import sqrt
from scipy.stats import norm
# h1 : u != U_0
# h0 : u = u_0
#case study : ball bearing example, claim is that radius = 3, do hypothesis testing
mu_0 = 3
sigma = 0.1
#collect sample
sample = ball_bearing_radius
#compute mean
mean = np.mean(sample)
#compute n
n = len(sample)
#compute test statistic
z = (mean - mu_0) /(sigma/sqrt(n))
#set alpha
a = 0.01
#-------------------------
#calculate the z_a/2, by using percent point function of the norm of scipy
#ppf = percent point function, inverse of CDF(comulative distribution function)
#also, CDF = pr(X<=x), i.e., probability to the left of the distribution
z_critical = norm.ppf(1-a/2) #this returns a value for which the probab to the left is 0.975
p_value = 2*(1 - norm.cdf(np.abs(z)))
p_value = float("{:.4f}".format(p_value))
print('z : ',z)
print('\nz_critical :', z_critical)
print('\nmean :', mean, "\n\n")
#test the hypothesis
if (np.abs(z) > z_critical):
print("\nREJECT THE NULL HYPOTHESIS : \n p-value = ", p_value, "\n Alpha = ", a )
else:
print("CANNOT REJECT THE NULL HYPOTHESIS. NOT ENOUGH EVIDENCE TO REJECT IT: \n p-value = ", p_value, "\n Alpha = ", a )
发布于 2020-12-28 16:54:35
.ppf()
函数计算给定正态分布值的概率,而.cdf()
函数计算给定概率为所需值的正态分布值。在这个特定的意义上,这些是彼此相反的。
要演示此计算,请检查以下示例代码。
from scipy.stats import norm
print(norm.ppf(0.95))
print(norm.cdf(1.6448536269514722))
这张带有上面代码的图像应该会让你明白这一点。
谢谢!
https://stackoverflow.com/questions/65468026
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