def _make_normal_model(self,
weights,
root,
xmax=175,
xlabel='adult weight (kg)',
axis=None):
cdf = _13_Cdf._make_cdf_from_list(weights)
pyplot.clf()
t = weights[:]
t.sort()
mu, var = _03_thinkstats._trimmed_mean_var(t)
print('n, Mean, Var', len(weights), mu, var)
sigma = math.sqrt(var)
print('Sigma', sigma)
xs, ps = continuous._render_normal_cdf(mu, sigma, xmax)
pyplot.plot(xs, ps, label='model', linewidth=4, color='0.7')
xs, ps = cdf._render()
pyplot.plot(xs, ps, label='data', linewidth=2, color='blue')
_05_myplot._save(root,
title='Adult weight',
xlabel=xlabel,
ylabel='CDF',
axis=axis or [0, xmax, 0, 1])
def _summarize_weight(self):
"""Print summary statistics for male and female weight."""
# make a dictionary that maps from gender code to list of weights
d = {1: [], 2: [], 'all': []}
[d[r.sex].append(r.weight2) for r in self.records if r.weight2 != 'NA']
[d['all'].append(r.weight2) for r in self.records if r.weight2 != 'NA']
print('Weight (kg):')
print('key n mean var sigma cv')
for key, t in d.iteritems():
mu, var = _03_thinkstats._trimmed_mean_var(t)
sigma = math.sqrt(var)
cv = sigma / mu
print(key, len(t), mu, var, sigma, cv)
def _make_normal_model(weights):
"""Plot the CDF of birthweights with a normal model."""
# estimate parameters: trimming outliers yields a better fit
mu, var = _03_thinkstats._trimmed_mean_var(weights, p=0.01)
print('Mean, Var', mu, var)
# plot the model
sigma = math.sqrt(var)
print('Sigma', sigma)
xs, ps = _render_normal_cdf(mu, sigma, 200)
pyplot.clf()
pyplot.plot(xs, ps, label='model', linewidth=4, color='0.8')
# plot the data
cdf = _13_Cdf._make_cdf_from_list(weights)
xs, ps = cdf._render()
pyplot.plot(xs, ps, label='data', linewidth=2, color='blue')
_05_myplot._save('nsfg_birthwgt_model',
title='Birth weights',
xlabel='birth weight (oz)',
ylabel='CDF')
