Case:
tt = (sm-m)/(sv/float(n)) # t-statistic for mean pval = ((tt), n-1)*2 # two-sided pvalue = Prob(abs(t)>tt) print 't-statistic = %6.3f pvalue = %6.4f' % (tt, pval) t-statistic = 0.391 pvalue = 0.6955
Link:
/questions/17559897/python-p-value-from-t-statistic
/doc/scipy/reference/tutorial/
Executable code
# coding: utf-8
from __future__ import division
import numpy as np
from scipy import stats
means = [0.0539, 4,8,3,6,9,1]
stds = [5,4,8,3,6,7,9]
mu = [0, 4.1, 7, 2, 5, 8, 0]
n = 20
output = []
for sm, std, m in zip(means, stds, mu):
# print("value:", sm, std)
tt = (sm-m)/(std/(float(n))) # t-statistic for mean
pval = ((tt), n-1)*2 # two-sided pvalue = Prob(abs(t)>tt)
# print('t-statistic = %6.3f pvalue = %6.4f' % (tt, pval))
(format(pval))
print("\t".join(output))
The above implementation method of p-value in python is all the content I have shared with you. I hope you can give you a reference and I hope you can support me more.