def _test(actual1, actual2, model, iters=1000):
"""
Estimates p-values based on differences in the mean.
Args:
actual1:
actual2: sequences of observed values for groups 1 and 2
model: sequences of values from the hypothetical distribution
"""
n = len(actual1)
m = len(actual2)
# compute delta
mu1, mu2, delta = hypothesis._difference_in_mean(actual1, actual2)
delta = abs(delta)
print('n:', n)
print('m:', m)
print('mu1', mu1)
print('mu2', mu2)
print('delta', delta)
# compute the expected distribution of differences in sample mean
mu_pooled, var_pooled = _03_thinkstats._mean_var(model)
print('(Mean, Var) of pooled data', mu_pooled, var_pooled)
f = 1.0 / n + 1.0 / m
mu, var = (0, f * var_pooled)
print('Expected Mean, Var of deltas', mu, var)
# compute the p-value of delta in the observed distribution
sigma = math.sqrt(var)
left = _16_erf._normal_cdf(-delta, mu, sigma)
right = 1 - _16_erf._normal_cdf(delta, mu, sigma)
pvalue = left + right
print('Tails:', left, right)
print('p-value:', pvalue)
# compare the mean and variance of resamples differences
deltas = [hypothesis._resample(model, model, n, m) for i in range(iters)]
mean_var = _03_thinkstats._mean_var(deltas)
print('(Mean, Var) of resampled deltas', mean_var)
return pvalue
def _render_normal_cdf(mu, sigma, max, n=50):
"""Generates sequences of xs and ps for a normal CDF."""
xs = [max * i / n for i in range(n)]
ps = [_16_erf._normal_cdf(x, mu, sigma) for x in xs]
return xs, ps