def Estimate1(n=7, m=100000):
"""Mean error for xbar and median as estimators of population mean.
n: sample size
m: number of iterations
"""
mu = 0
sigma = 1
means = []
medians = []
for _ in range(m):
xs = [random.gauss(mu, sigma) for i in range(n)]
xbar = np.mean(xs)
median = np.median(xs)
means.append(xbar)
medians.append(median)
print('Experiment 1')
print('mean error xbar', MeanError(means, mu))
print('mean error median', MeanError(medians, mu))
def SimulateManyGames(lam, iters=1000000):
lam_est = []
for _ in np.arange(iters):
lam_est.append(SimulateGame(lam))
print('Mean Error =', MeanError(lam_est, lam))
print('RMSE =', RMSE(lam_est, lam))
lam_cdf = thinkstats2.Cdf(lam_est)
ci = lam_cdf.Percentile(5), lam_cdf.Percentile(95)
lam_pmf = thinkstats2.Pmf(lam_est)
thinkplot.Cdf(lam_cdf)
thinkplot.Plot([ci[0], ci[0]], [0, 1], linewidth=2, color='0.8')
thinkplot.Plot([ci[1], ci[1]], [0, 1], linewidth=2, color='0.8')
thinkplot.Config(xlabel='Goals per game', ylabel='CDF', legend=False)
def Estimate4(lam=2, m=1000000):
estimates = []
for i in range(m):
L = SimulateGame(lam)
estimates.append(L)
print('Experiment 4')
print('rmse L', RMSE(estimates, lam))
print('mean error L', MeanError(estimates, lam))
pmf = thinkstats2.Pmf(estimates)
thinkplot.Hist(pmf)
thinkplot.Show()