from __future__ import division
import numpy as np
from scipy import stats
stats.norm.cdf(1.)
stats.norm.sf(1.)
stats.norm.cdf(2.)
stats.norm.cdf(1.,scale=0.5)
stats.norm.cdf(0.5,scale=0.5)
stats.norm.cdf(1.3,scale=0.5,loc=0.3)
x = np.linspace(-5,5,100)
print(x)
Fx = stats.norm.cdf(x)
from matplotlib import pyplot as plt
plt.figure()
plt.plot(x,Fx)
plt.title('Standard normal cdf')
plt.figure()
fx = stats.norm.pdf(x)
plt.plot(x,fx)
plt.title('Standard normal pdf')
from __future__ import division
import numpy as np
from scipy import stats
muscaled = np.linspace(0,5,1000)
power = stats.norm.sf(1.645-muscaled)
plot(muscaled,power)
title(r'Power for one-sided $z$ test with $\alpha=0.05$')
xlabel(r'$\frac{\mu}{\sigma/\sqrt{n}}$')
ylabel(r'$\gamma(\mu)$')
xlim(0,5)
ylim(0,1)
grid(True)
savefig('notes02_normpower.eps',bbox_inches='tight')