chen0040 /
pysie
| @@ 14-38 (lines=25) @@ | ||
| 11 | p_value_two_tail = None |
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| 12 | test_statistic = None |
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| 13 | significance_level = None |
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| 14 | reject_mean_same = None |
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| 15 | ||
| 16 | def __init__(self, sampling_distribution, significance_level=None): |
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| 17 | self.sampling_distribution = sampling_distribution |
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| 18 | if significance_level is not None: |
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| 19 | self.significance_level = significance_level |
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| 20 | ||
| 21 | if self.sampling_distribution.distribution_family == DistributionFamily.normal: |
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| 22 | Z = sampling_distribution.point_estimate / sampling_distribution.standard_error |
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| 23 | self.test_statistic = Z |
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| 24 | pf = norm.cdf(Z) |
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| 25 | if Z < 0: |
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| 26 | pf = 1 - pf |
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| 27 | self.p_value_one_tail = 1 - pf |
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| 28 | self.p_value_two_tail = self.p_value_one_tail * 2 |
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| 29 | else: |
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| 30 | td_df = sampling_distribution.point_estimate / sampling_distribution.standard_error |
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| 31 | self.test_statistic = td_df |
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| 32 | pf = t.cdf(td_df, sampling_distribution.df) |
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| 33 | if td_df < 0: |
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| 34 | pf = 1 - pf |
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| 35 | self.p_value_one_tail = 1 - pf |
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| 36 | self.p_value_two_tail = self.p_value_one_tail * 2 |
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| 37 | ||
| 38 | if significance_level is not None: |
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| 39 | self.reject_mean_same = (self.p_value_one_tail < significance_level, |
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| 40 | self.p_value_two_tail < significance_level) |
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| 41 | ||
| @@ 60-83 (lines=24) @@ | ||
| 57 | significance_level = None |
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| 58 | reject_mean_null = None |
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| 59 | ||
| 60 | def __init__(self, sampling_distribution, p_null, significance_level=None): |
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| 61 | self.sampling_distribution = sampling_distribution |
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| 62 | self.p_null = p_null |
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| 63 | if significance_level is not None: |
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| 64 | self.significance_level = significance_level |
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| 65 | ||
| 66 | if self.sampling_distribution.distribution_family == DistributionFamily.normal: |
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| 67 | standard_error_null = math.sqrt(p_null * (1 - p_null) / sampling_distribution.sample_size) |
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| 68 | Z = (sampling_distribution.point_estimate - p_null) / standard_error_null |
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| 69 | self.test_statistic = Z |
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| 70 | pf = norm.cdf(Z) |
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| 71 | if Z < 0: |
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| 72 | pf = 1 - pf |
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| 73 | self.p_value_one_tail = 1 - pf |
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| 74 | self.p_value_two_tail = self.p_value_one_tail * 2 |
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| 75 | else: |
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| 76 | simulated_proportions = self.simulate() |
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| 77 | ||
| 78 | self.p_value_one_tail = sum(1.0 for x in simulated_proportions if x > sampling_distribution.point_estimate) / 1000.0 |
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| 79 | self.p_value_two_tail = self.p_value_one_tail |
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| 80 | ||
| 81 | if significance_level is not None: |
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| 82 | self.reject_mean_null = (self.p_value_one_tail < significance_level, |
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| 83 | self.p_value_two_tail < significance_level) |
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| 84 | ||
| 85 | def simulate(self): |
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| 86 | simulated_proportions = [0] * 1000 |
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