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import math |
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from pysie.stats.distributions import DistributionFamily |
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from scipy.stats import norm, t |
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class MeanTesting(object): |
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sampling_distribution = None |
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p_value_one_tail = None |
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p_value_two_tail = None |
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mean_null = None |
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test_statistic = None |
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significance_level = None |
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reject_mean_null = None |
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View Code Duplication |
def __init__(self, sampling_distribution, mean_null, significance_level=None): |
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self.sampling_distribution = sampling_distribution |
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self.mean_null = mean_null |
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if significance_level is not None: |
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self.significance_level = significance_level |
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if self.sampling_distribution.distribution_family == DistributionFamily.normal: |
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standard_error_null = sampling_distribution.standard_error |
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Z = (sampling_distribution.point_estimate - mean_null) / standard_error_null |
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self.test_statistic = Z |
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pf = norm.cdf(Z) |
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if Z < 0: |
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pf -= 0.5 |
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self.p_value_one_tail = 1 - pf |
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self.p_value_two_tail = self.p_value_one_tail * 2 |
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else: |
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standard_error_null = sampling_distribution.standard_error |
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td_df = (sampling_distribution.point_estimate - mean_null) / standard_error_null |
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self.test_statistic = td_df |
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pf = t.cdf(td_df, sampling_distribution.df) |
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if td_df < 0: |
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pf -= 0.5 |
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self.p_value_one_tail = 1 - pf |
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self.p_value_two_tail = self.p_value_one_tail * 2 |
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if significance_level is not None: |
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self.reject_mean_null = (self.p_value_one_tail < significance_level, |
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self.p_value_two_tail < significance_level) |
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def will_reject(self, significance_level): |
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return self.p_value_one_tail < significance_level, self.p_value_two_tail < significance_level |
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class ProportionTesting(object): |
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sampling_distribution = None |
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p_value_one_tail = None |
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p_value_two_tail = None |
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mean_null = None |
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test_statistic = None |
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significance_level = None |
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reject_mean_null = None |
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View Code Duplication |
def __init__(self, sampling_distribution, p_null, significance_level=None): |
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self.sampling_distribution = sampling_distribution |
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self.p_null = p_null |
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if significance_level is not None: |
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self.significance_level = significance_level |
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if self.sampling_distribution.distribution_family == DistributionFamily.normal: |
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standard_error_null = math.sqrt(p_null * (1 - p_null) / sampling_distribution.sample_size) |
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Z = (sampling_distribution.point_estimate - p_null) / standard_error_null |
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self.test_statistic = Z |
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pf = norm.cdf(Z) |
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if Z < 0: |
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pf -= 0.5 |
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self.p_value_one_tail = 1 - pf |
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self.p_value_two_tail = self.p_value_one_tail * 2 |
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else: |
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standard_error_null = math.sqrt(p_null * (1 - p_null) / sampling_distribution.sample_size) |
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td_df = (sampling_distribution.point_estimate - p_null) / standard_error_null |
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self.test_statistic = td_df |
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pf = t.cdf(td_df, sampling_distribution.df) |
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if td_df < 0: |
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pf -= 0.5 |
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self.p_value_one_tail = 1 - pf |
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self.p_value_two_tail = self.p_value_one_tail * 2 |
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if significance_level is not None: |
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self.reject_mean_null = (self.p_value_one_tail < significance_level, |
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self.p_value_two_tail < significance_level) |
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chen0040 /
pysie
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