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import unittest |
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from numpy.random import normal, random |
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from pysie.stats.distributions import MeanSamplingDistribution, DistributionFamily, ProportionSamplingDistribution |
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from pysie.stats.samples import Sample, SampleDistribution |
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class MeanSamplingDistributionUnitTest(unittest.TestCase): |
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def test_confidence_interval_with_sample_stats_normal(self): |
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sample_mean = 0 |
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sample_sd = 1 |
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sample_size = 31 |
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sampling_distribution = MeanSamplingDistribution(sample_mean=sample_mean, sample_sd=sample_sd, sample_size=sample_size) |
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self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal) |
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print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate) |
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+ ', standard_error=' + str(sampling_distribution.standard_error) + ')') |
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print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
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View Code Duplication |
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def test_confidence_interval_with_sample_normal(self): |
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mu = 0.0 |
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sigma = 1.0 |
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sample_size = 31 |
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sample = Sample() |
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for i in range(sample_size): |
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sample.add_numeric(normal(mu, sigma)) |
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sampling_distribution = MeanSamplingDistribution(sample_distribution=SampleDistribution(sample)) |
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self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal) |
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print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate) |
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+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
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print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
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def test_confidence_interval_with_sample_stats_student(self): |
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sample_mean = 0 |
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sample_sd = 1 |
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sample_size = 29 |
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sampling_distribution = MeanSamplingDistribution(sample_mean=sample_mean, sample_sd=sample_sd, sample_size=sample_size) |
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self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.student_t) |
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print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate) |
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View Code Duplication |
+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
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print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
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def test_confidence_interval_with_sample_student(self): |
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mu = 0.0 |
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sigma = 1.0 |
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sample_size = 29 |
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sample = Sample() |
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for i in range(sample_size): |
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sample.add_numeric(normal(mu, sigma)) |
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sampling_distribution = MeanSamplingDistribution(sample_distribution=SampleDistribution(sample)) |
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self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.student_t) |
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print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate) |
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+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
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print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
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class ProportionSamplingDistributionUnitTest(unittest.TestCase): |
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def test_confidence_interval_with_sample_stats_normal(self): |
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sample_proportion = 0.6 |
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sample_size = 31 |
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sampling_distribution = ProportionSamplingDistribution(sample_proportion=sample_proportion, |
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sample_size=sample_size) |
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self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal) |
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print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate) |
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+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
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print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
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View Code Duplication |
def test_confidence_interval_with_sample_normal(self): |
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sample = Sample() |
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for i in range(100): |
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if random() <= 0.6: |
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sample.add_category("OK") |
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else: |
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sample.add_category("CANCEL") |
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sampling_distribution = ProportionSamplingDistribution(sample_distribution=SampleDistribution(sample, |
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categorical_value="OK")) |
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self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal) |
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print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate) |
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+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
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print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
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def test_confidence_interval_with_sample_stats_simulation(self): |
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sample_proportion = 0.6 |
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sample_size = 10 |
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sampling_distribution = ProportionSamplingDistribution(sample_proportion=sample_proportion, |
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sample_size=sample_size) |
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self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation) |
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print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate) |
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+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
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print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
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View Code Duplication |
def test_confidence_interval_with_sample_simulation(self): |
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sample = Sample() |
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for i in range(10): |
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if random() <= 0.6: |
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sample.add_category("OK") |
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else: |
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sample.add_category("CANCEL") |
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sampling_distribution = ProportionSamplingDistribution(sample_distribution=SampleDistribution(sample, |
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categorical_value="OK")) |
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self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation) |
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print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate) |
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+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
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print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
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if __name__ == '__main__': |
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unittest.main() |
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chen0040 /
pysie
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