def main():
""" TestingHypothesisMath.py """
results = flip_some_coins_lots_of_times(number_of_times=100000,
number_of_flips=1000,
fairness=0.5)
print 'Testing a single mean:', example_test_a_mean(
results, target=500, significance_level=0.95)
results_1 = flip_some_coins_lots_of_times(number_of_times=100000,
number_of_flips=1000,
fairness=0.5)
results_2 = flip_some_coins_lots_of_times(number_of_times=100000,
number_of_flips=1000,
fairness=0.5)
print 'Comparing two equal means:', example_compare_two_means(
results_1, results_2)
results_2 = flip_some_coins_lots_of_times(number_of_times=100000,
number_of_flips=1000,
fairness=0.3)
print 'Comparing two unequal means:', example_compare_two_means(
results_1, results_2)
print 'Testing a single proportion:', example_test_a_proportion(p=0.1,
n=100,
target=0.1)
print 'Comparing two equal proportions:', example_compare_two_proportions(
p1=0.1, n1=1000, p2=0.1, n2=1000)
print 'Comparing two unequal proportions:', example_compare_two_proportions(
p1=0.1, n1=1000, p2=0.15, n2=1000)
def main():
""" NonParametricMethods.py """
print '---------- Normal data ---------------'
normal_data = flip_some_coins_lots_of_times(10000, number_of_flips=1000, fairness=0.5, plot=True)
normal_lower_bound, normal_upper_bound = get_confidence_intervals_using_the_normal_distribution(
normal_data)
print 'normal: len', len(normal_data), 'mean', mean(normal_data)
print 'Normality test for normal data:', test_for_normality(normal_data)
print 'Confidence intervals for normal data:', normal_lower_bound, normal_upper_bound
normal_lower_bound, normal_upper_bound = bootstrap_confidence_intervals(normal_data)
print 'Bootstrapping confidence intervals for normal data:', normal_lower_bound, normal_upper_bound
print
print '---------- Powerlaw data -------------'
powerlaw_data = create_random_non_normal_data(sample_size=10000, plot=True)
print 'powerlaw: len', len(powerlaw_data), 'mean', mean(powerlaw_data)
powerlaw_lower_bound, powerlaw_upper_bound = get_confidence_intervals_using_the_normal_distribution(
powerlaw_data)
print 'Normality test for powerlaw data:', test_for_normality(powerlaw_data)
print 'Confidence intervals for powerlaw data:', powerlaw_lower_bound, powerlaw_upper_bound
powerlaw_lower_bound, powerlaw_upper_bound = bootstrap_confidence_intervals(powerlaw_data)
print 'Bootstrapping confidence intervals for powerlaw data:', powerlaw_lower_bound, powerlaw_upper_bound
print
print '---------- Two equal normal means -----------------'
data_1 = flip_some_coins_lots_of_times(10000, number_of_flips=1000, fairness=0.5)
data_2 = flip_some_coins_lots_of_times(10000, number_of_flips=1000, fairness=0.5)
print 'Normality based test:', compare_two_means(data_2, data_1)
print 'Ranksum test :', non_parametric_test_for_difference_of_means(data_2, data_1)
print
print '---------- Two unequal normal means ---------------'
data_1 = flip_some_coins_lots_of_times(10000, number_of_flips=1000, fairness=0.5)
data_2 = flip_some_coins_lots_of_times(10000, number_of_flips=1000, fairness=0.45)
print 'Normality based test:', compare_two_means(data_2, data_1)
print 'Ranksum test :', non_parametric_test_for_difference_of_means(data_2, data_1)
print
print '---------- Two equal non-normal means -------------'
data_1 = create_random_non_normal_data(sample_size=100000)
data_2 = create_random_non_normal_data(sample_size=100000)
print 'Normality based test:', compare_two_means(data_2, data_1)
print 'Ranksum test :', non_parametric_test_for_difference_of_means(data_2, data_1)
print
print '---------- Two unequal non-normal means -------------'
data_1 = create_random_non_normal_data(sample_size=100000, a=11.3)
data_2 = create_random_non_normal_data(sample_size=100000, a=12)
print 'Normality based test:', compare_two_means(data_2, data_1)
print 'Ranksum test :', non_parametric_test_for_difference_of_means(data_2, data_1)
def main():
""" ConfidenceIntervals.py """
results = flip_some_coins_lots_of_times(number_of_times=100000,
number_of_flips=1000,
fairness=0.5)
print 'Three-sigma:', get_confidence_intervals_using_the_three_sigma_rule(results)
print 'Normal dist.:', get_confidence_intervals_using_the_normal_distribution(results)
print 'Quantiles:', get_confidence_intervals_using_the_quantiles(results)
def main():
""" TestingHypothesisMath.py """
results = flip_some_coins_lots_of_times(number_of_times=100000,
number_of_flips=1000,
fairness=0.5)
print 'Testing a single mean:', example_test_a_mean(
results,
target=500,
significance_level=0.95)
results_1 = flip_some_coins_lots_of_times(number_of_times=100000,
number_of_flips=1000,
fairness=0.5)
results_2 = flip_some_coins_lots_of_times(number_of_times=100000,
number_of_flips=1000,
fairness=0.5)
print 'Comparing two equal means:', example_compare_two_means(
results_1,
results_2)
results_2 = flip_some_coins_lots_of_times(number_of_times=100000,
number_of_flips=1000,
fairness=0.3)
print 'Comparing two unequal means:', example_compare_two_means(
results_1,
results_2)
print 'Testing a single proportion:', example_test_a_proportion(p=0.1,
n=100,
target=0.1)
print 'Comparing two equal proportions:', example_compare_two_proportions(p1=0.1,
n1=1000,
p2=0.1,
n2=1000)
print 'Comparing two unequal proportions:', example_compare_two_proportions(p1=0.1,
n1=1000,
p2=0.15,
n2=1000)
def main():
""" NonParametricMethods.py """
print '---------- Normal data ---------------'
normal_data = flip_some_coins_lots_of_times(10000,
number_of_flips=1000,
fairness=0.5,
plot=True)
normal_lower_bound, normal_upper_bound = get_confidence_intervals_using_the_normal_distribution(
normal_data)
print 'normal: len', len(normal_data), 'mean', mean(normal_data)
print 'Normality test for normal data:', test_for_normality(normal_data)
print 'Confidence intervals for normal data:', normal_lower_bound, normal_upper_bound
normal_lower_bound, normal_upper_bound = bootstrap_confidence_intervals(
normal_data)
print 'Bootstrapping confidence intervals for normal data:', normal_lower_bound, normal_upper_bound
print
print '---------- Powerlaw data -------------'
powerlaw_data = create_random_non_normal_data(sample_size=10000, plot=True)
print 'powerlaw: len', len(powerlaw_data), 'mean', mean(powerlaw_data)
powerlaw_lower_bound, powerlaw_upper_bound = get_confidence_intervals_using_the_normal_distribution(
powerlaw_data)
print 'Normality test for powerlaw data:', test_for_normality(
powerlaw_data)
print 'Confidence intervals for powerlaw data:', powerlaw_lower_bound, powerlaw_upper_bound
powerlaw_lower_bound, powerlaw_upper_bound = bootstrap_confidence_intervals(
powerlaw_data)
print 'Bootstrapping confidence intervals for powerlaw data:', powerlaw_lower_bound, powerlaw_upper_bound
print
print '---------- Two equal normal means -----------------'
data_1 = flip_some_coins_lots_of_times(10000,
number_of_flips=1000,
fairness=0.5)
data_2 = flip_some_coins_lots_of_times(10000,
number_of_flips=1000,
fairness=0.5)
print 'Normality based test:', compare_two_means(data_2, data_1)
print 'Ranksum test :', non_parametric_test_for_difference_of_means(
data_2, data_1)
print
print '---------- Two unequal normal means ---------------'
data_1 = flip_some_coins_lots_of_times(10000,
number_of_flips=1000,
fairness=0.5)
data_2 = flip_some_coins_lots_of_times(10000,
number_of_flips=1000,
fairness=0.45)
print 'Normality based test:', compare_two_means(data_2, data_1)
print 'Ranksum test :', non_parametric_test_for_difference_of_means(
data_2, data_1)
print
print '---------- Two equal non-normal means -------------'
data_1 = create_random_non_normal_data(sample_size=100000)
data_2 = create_random_non_normal_data(sample_size=100000)
print 'Normality based test:', compare_two_means(data_2, data_1)
print 'Ranksum test :', non_parametric_test_for_difference_of_means(
data_2, data_1)
print
print '---------- Two unequal non-normal means -------------'
data_1 = create_random_non_normal_data(sample_size=100000, a=11.3)
data_2 = create_random_non_normal_data(sample_size=100000, a=12)
print 'Normality based test:', compare_two_means(data_2, data_1)
print 'Ranksum test :', non_parametric_test_for_difference_of_means(
data_2, data_1)
