def testEvalNormalCdf(self):
p = thinkstats2.EvalNormalCdf(0)
self.assertAlmostEqual(p, 0.5)
p = thinkstats2.EvalNormalCdf(2, 2, 3)
self.assertAlmostEqual(p, 0.5)
p = thinkstats2.EvalNormalCdf(1000, 0, 1)
self.assertAlmostEqual(p, 1.0)
p = thinkstats2.EvalNormalCdf(-1000, 0, 1)
self.assertAlmostEqual(p, 0.0)
x = thinkstats2.EvalNormalCdfInverse(0.95, 0, 1)
self.assertAlmostEqual(x, 1.64485362695)
x = thinkstats2.EvalNormalCdfInverse(0.05, 0, 1)
self.assertAlmostEqual(x, -1.64485362695)
def Prob(self, x):
"""Cumulative probability of x.
x: numeric
"""
return thinkstats2.EvalNormalCdf(x, self.mu, self.sigma)
print(r"mean interarrival time = 1/$\lambda$: {:.1f} minutes".format(
-1.0 / lr.coef_[0]))
print("score: {:.3f} ".format(lr.score(X, ys)))
#%% [markdown]
# ## 5.2 Normal distribution
#%%
import scipy.stats
scipy.stats.norm.cdf(0)
#%%
# plot normal cdf
x = np.linspace(-1, 4, 100)
for mu, sigma in zip([1, 2, 3], [0.5, 0.4, 0.3]):
p = thinkstats2.EvalNormalCdf(x, mu=mu, sigma=sigma)
thinkplot.plot(x, p, label=(r"$\mu$={} $\sigma$={}".format(mu, sigma)))
thinkplot.Config(xlabel='x', ylabel='CDF')
#%%
import nsfg
preg = nsfg.ReadFemPreg()
live = preg[preg.outcome == 1]
#%%
# without trimming
totalwgt_lb = live.totalwgt_lb.dropna()
cdf = thinkstats2.Cdf(totalwgt_lb)
mu = totalwgt_lb.mean()
sigma = totalwgt_lb.std()
