def cdf(self, val):
return stdtr(self.df, (val - self.mu) / self.scale)
avar = float(40)**2 * (na / adof)
nb = float(100)
bdof = nb - 1
bbar = float(190)
bvar = float(20)**2 * (nb / bdof)
# Use scipy.stats.ttest_ind_from_stats.
t2, p2 = ttest_ind_from_stats(abar,
sqrt(avar),
na,
bbar,
sqrt(bvar),
nb,
equal_var=False)
print("ttest_ind_from_stats: t = %g p = %g" % (t2, p2))
# Use the formulas directly.
tf = (abar - bbar - 7) / sqrt(avar / na + bvar / nb)
dof = (avar / na + bvar / nb)**2 / (avar**2 / (na**2 * adof) + bvar**2 /
(nb**2 * bdof))
#calculating p value of P(T > tf)
pval = stdtr(dof, abs(tf))
print 't = %f p = %f dof = %f' % (tf, pval, dof)
if pval < significance:
print('It is unlikely to except null hypothesis!')
else:
print('Null hypothesis cannot be rejected!')