ProportionDiffSamplingDistributionUnitTest - Code Metrics - chen0040/pysie - Measure and Improve Code Quality continuously with Scrutinizer

ProportionDiffSamplingDistributionUnitTest A
last analyzed 2017-06-16 07:13 UTC

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Complexity

Total Complexity

Size/Duplication

Total Lines	82
Duplicated Lines	62.2 %

Importance

Changes	1
Bugs	0	Features	0

Metric	Value
dl	51
loc	82
rs	10
c	1
b	0
f	0
wmc	12

4 Methods

Rating	Name	Duplication	Size	Complexity
B	test_confidence_interval_with_sample_normal()	24	24	5
A	test_confidence_interval_with_sample_stats_normal()	0	13	1
B	test_confidence_interval_with_sample_simulation()	27	27	5
A	test_confidence_interval_with_sample_stats_simulation()	0	14	1

How to fix Duplicated Code

import unittest

from numpy.random import normal, random

from pysie.stats.distributions import MeanSamplingDistribution, DistributionFamily, ProportionSamplingDistribution, \
    MeanDiffSamplingDistribution, ProportionDiffSamplingDistribution
from pysie.stats.samples import Sample, SampleDistribution


class MeanSamplingDistributionUnitTest(unittest.TestCase):
    def test_confidence_interval_with_sample_stats_normal(self):
        sample_mean = 0
        sample_sd = 1
        sample_size = 31
        sampling_distribution = MeanSamplingDistribution(sample_mean=sample_mean, sample_sd=sample_sd,
                                                         sample_size=sample_size)
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error=' + str(sampling_distribution.standard_error) + ')')
        print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))


    def test_confidence_interval_with_sample_normal(self):
        mu = 0.0
        sigma = 1.0
        sample_size = 31
        sample = Sample()

        for i in range(sample_size):
            sample.add_numeric(normal(mu, sigma))

        sampling_distribution = MeanSamplingDistribution(sample_distribution=SampleDistribution(sample))
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_stats_student(self):
        sample_mean = 0
        sample_sd = 1
        sample_size = 29
        sampling_distribution = MeanSamplingDistribution(sample_mean=sample_mean, sample_sd=sample_sd,
                                                         sample_size=sample_size)
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.student_t)

        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_student(self):
        mu = 0.0
        sigma = 1.0
        sample_size = 29
        sample = Sample()

        for i in range(sample_size):
            sample.add_numeric(normal(mu, sigma))

        sampling_distribution = MeanSamplingDistribution(sample_distribution=SampleDistribution(sample))
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.student_t)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))


class MeanDiffSamplingDistributionUnitTest(unittest.TestCase):
    def test_confidence_interval_with_sample_stats_normal(self):
        grp1_sample_mean = 0
        grp1_sample_sd = 1
        grp1_sample_size = 31
        grp2_sample_mean = 0.001
        grp2_sample_sd = 2.1
        grp2_sample_size = 36
        sampling_distribution = MeanDiffSamplingDistribution(grp1_sample_mean=grp1_sample_mean,
                                                             grp1_sample_sd=grp1_sample_sd,
                                                             grp1_sample_size=grp1_sample_size,

                                                             grp2_sample_mean=grp2_sample_mean,
                                                             grp2_sample_sd=grp2_sample_sd,
                                                             grp2_sample_size=grp2_sample_size)
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error=' + str(sampling_distribution.standard_error) + ')')
        print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_normal(self):
        grp1_mu = 0.0
        grp1_sigma = 1.0
        grp1_sample_size = 31
        grp1_sample = Sample()

        grp2_mu = 0.09
        grp2_sigma = 2.0
        grp2_sample_size = 36
        grp2_sample = Sample()

        for i in range(grp1_sample_size):
            grp1_sample.add_numeric(normal(grp1_mu, grp1_sigma))

        for i in range(grp2_sample_size):
            grp2_sample.add_numeric(normal(grp2_mu, grp2_sigma))

        sampling_distribution = MeanDiffSamplingDistribution(grp1_sample_distribution=SampleDistribution(grp1_sample),

                                                             grp2_sample_distribution=SampleDistribution(grp2_sample))
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_stats_student(self):
        grp1_sample_mean = 0
        grp1_sample_sd = 1
        grp1_sample_size = 29
        grp2_sample_mean = 0.001
        grp2_sample_sd = 1.3
        grp2_sample_size = 24
        sampling_distribution = MeanDiffSamplingDistribution(grp1_sample_mean=grp1_sample_mean,
                                                             grp1_sample_sd=grp1_sample_sd,
                                                             grp1_sample_size=grp1_sample_size,
                                                             grp2_sample_mean=grp2_sample_mean,
                                                             grp2_sample_sd=grp2_sample_sd,
                                                             grp2_sample_size=grp2_sample_size)
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.student_t)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_student(self):
        grp1_mu = 0.0
        grp1_sigma = 1.0
        grp1_sample_size = 29
        grp1_sample = Sample()
        grp2_mu = 0.08
        grp2_sigma = 1.1
        grp2_sample_size = 27
        grp2_sample = Sample()

        for i in range(grp1_sample_size):
            grp1_sample.add_numeric(normal(grp1_mu, grp1_sigma))
        for i in range(grp2_sample_size):
            grp2_sample.add_numeric(normal(grp2_mu, grp2_sigma))

        sampling_distribution = MeanDiffSamplingDistribution(grp1_sample_distribution=SampleDistribution(grp1_sample),
                                                             grp2_sample_distribution=SampleDistribution(grp2_sample))
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.student_t)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))


class ProportionSamplingDistributionUnitTest(unittest.TestCase):
    def test_confidence_interval_with_sample_stats_normal(self):
        sample_proportion = 0.6
        sample_size = 31
        sampling_distribution = ProportionSamplingDistribution(sample_proportion=sample_proportion,
                                                               sample_size=sample_size)
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_normal(self):
        sample = Sample()

        for i in range(100):
            if random() <= 0.6:
                sample.add_category("OK")
            else:
                sample.add_category("CANCEL")

        sampling_distribution = ProportionSamplingDistribution(sample_distribution=SampleDistribution(sample,
                                                                                                      categorical_value="OK"))
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_stats_simulation(self):
        sample_proportion = 0.6
        sample_size = 10
        sampling_distribution = ProportionSamplingDistribution(sample_proportion=sample_proportion,
                                                               sample_size=sample_size)
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_simulation(self):
        sample = Sample()

        for i in range(10):
            if random() <= 0.6:
                sample.add_category("OK")
            else:
                sample.add_category("CANCEL")

        sampling_distribution = ProportionSamplingDistribution(sample_distribution=SampleDistribution(sample,
                                                                                                      categorical_value="OK"))
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))


class ProportionDiffSamplingDistributionUnitTest(unittest.TestCase):
    def test_confidence_interval_with_sample_stats_normal(self):
        grp1_sample_proportion = 0.6
        grp1_sample_size = 31
        grp2_sample_proportion = 0.51
        grp2_sample_size = 32
        sampling_distribution = ProportionDiffSamplingDistribution(grp1_sample_proportion=grp1_sample_proportion,
                                                                   grp1_sample_size=grp1_sample_size,
                                                                   grp2_sample_proportion=grp2_sample_proportion,
                                                                   grp2_sample_size=grp2_sample_size)
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_normal(self):

        grp1_sample = Sample()
        grp2_sample = Sample()

        for i in range(100):
            if random() <= 0.6:
                grp1_sample.add_category("OK")
            else:
                grp1_sample.add_category("CANCEL")

        for i in range(100):
            if random() <= 0.61:
                grp2_sample.add_category("OK")
            else:
                grp2_sample.add_category("CANCEL")

        sampling_distribution = ProportionDiffSamplingDistribution(grp1_sample_distribution=SampleDistribution(
            grp1_sample, categorical_value="OK"),
            grp2_sample_distribution=SampleDistribution(
                grp2_sample, categorical_value="OK"))
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_stats_simulation(self):
        grp1_sample_proportion = 0.6
        grp1_sample_size = 10
        grp2_sample_proportion = 0.61
        grp2_sample_size = 9
        sampling_distribution = ProportionDiffSamplingDistribution(grp1_sample_proportion=grp1_sample_proportion,
                                                                   grp1_sample_size=grp1_sample_size,
                                                                   grp2_sample_proportion=grp2_sample_proportion,
                                                                   grp2_sample_size=grp2_sample_size
                                                                   )
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))

    def test_confidence_interval_with_sample_simulation(self):

        grp1_sample = Sample()
        grp2_sample = Sample()

        for i in range(10):
            if random() <= 0.6:
                grp1_sample.add_category("OK")
            else:
                grp1_sample.add_category("CANCEL")

        for i in range(9):
            if random() <= 0.61:
                grp2_sample.add_category("OK")
            else:
                grp2_sample.add_category("CANCEL")

        sampling_distribution = ProportionDiffSamplingDistribution(
            grp1_sample_distribution=SampleDistribution(grp1_sample,
                                                        categorical_value="OK"),
            grp2_sample_distribution=SampleDistribution(
                grp2_sample,
                categorical_value="OK")
            )
        self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation)
        print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
              + ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
        print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))


if __name__ == '__main__':
    unittest.main()


1		import unittest
2
3		from numpy.random import normal, random
4
5		from pysie.stats.distributions import MeanSamplingDistribution, DistributionFamily, ProportionSamplingDistribution, \
6		MeanDiffSamplingDistribution, ProportionDiffSamplingDistribution
7		from pysie.stats.samples import Sample, SampleDistribution
8
9
10		class MeanSamplingDistributionUnitTest(unittest.TestCase):
11		def test_confidence_interval_with_sample_stats_normal(self):
12		sample_mean = 0
13		sample_sd = 1
14		sample_size = 31
15		sampling_distribution = MeanSamplingDistribution(sample_mean=sample_mean, sample_sd=sample_sd,
16		sample_size=sample_size)
17		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
18		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
19		+ ', standard_error=' + str(sampling_distribution.standard_error) + ')')
20	View Code Duplication	print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
		0 ignored issues – show Duplication introduced 2017-05-31 01:43 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
21
22		def test_confidence_interval_with_sample_normal(self):
23		mu = 0.0
24		sigma = 1.0
25		sample_size = 31
26		sample = Sample()
27
28		for i in range(sample_size):
29		sample.add_numeric(normal(mu, sigma))
30
31		sampling_distribution = MeanSamplingDistribution(sample_distribution=SampleDistribution(sample))
32		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
33		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
34		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
35		print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
36
37		def test_confidence_interval_with_sample_stats_student(self):
38		sample_mean = 0
39		sample_sd = 1
40		sample_size = 29
41		sampling_distribution = MeanSamplingDistribution(sample_mean=sample_mean, sample_sd=sample_sd,
42		sample_size=sample_size)
43	View Code Duplication	self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.student_t)
		0 ignored issues – show Duplication introduced 2017-05-31 01:43 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
44		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
45		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
46		print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
47
48		def test_confidence_interval_with_sample_student(self):
49		mu = 0.0
50		sigma = 1.0
51		sample_size = 29
52		sample = Sample()
53
54		for i in range(sample_size):
55		sample.add_numeric(normal(mu, sigma))
56
57		sampling_distribution = MeanSamplingDistribution(sample_distribution=SampleDistribution(sample))
58		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.student_t)
59		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
60		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
61		print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
62
63
64		class MeanDiffSamplingDistributionUnitTest(unittest.TestCase):
65		def test_confidence_interval_with_sample_stats_normal(self):
66		grp1_sample_mean = 0
67		grp1_sample_sd = 1
68		grp1_sample_size = 31
69		grp2_sample_mean = 0.001
70		grp2_sample_sd = 2.1
71		grp2_sample_size = 36
72		sampling_distribution = MeanDiffSamplingDistribution(grp1_sample_mean=grp1_sample_mean,
73		grp1_sample_sd=grp1_sample_sd,
74	View Code Duplication	grp1_sample_size=grp1_sample_size,
		0 ignored issues – show Duplication introduced 2017-05-31 01:43 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
75		grp2_sample_mean=grp2_sample_mean,
76		grp2_sample_sd=grp2_sample_sd,
77		grp2_sample_size=grp2_sample_size)
78		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
79		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
80		+ ', standard_error=' + str(sampling_distribution.standard_error) + ')')
81		print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
82
83		def test_confidence_interval_with_sample_normal(self):
84		grp1_mu = 0.0
85		grp1_sigma = 1.0
86		grp1_sample_size = 31
87		grp1_sample = Sample()
88
89		grp2_mu = 0.09
90		grp2_sigma = 2.0
91		grp2_sample_size = 36
92		grp2_sample = Sample()
93
94		for i in range(grp1_sample_size):
95		grp1_sample.add_numeric(normal(grp1_mu, grp1_sigma))
96
97		for i in range(grp2_sample_size):
98		grp2_sample.add_numeric(normal(grp2_mu, grp2_sigma))
99
100	View Code Duplication	sampling_distribution = MeanDiffSamplingDistribution(grp1_sample_distribution=SampleDistribution(grp1_sample),
		0 ignored issues – show Duplication introduced 2017-05-31 01:43 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
101		grp2_sample_distribution=SampleDistribution(grp2_sample))
102		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
103		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
104		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
105		print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
106
107		def test_confidence_interval_with_sample_stats_student(self):
108		grp1_sample_mean = 0
109		grp1_sample_sd = 1
110		grp1_sample_size = 29
111		grp2_sample_mean = 0.001
112		grp2_sample_sd = 1.3
113		grp2_sample_size = 24
114		sampling_distribution = MeanDiffSamplingDistribution(grp1_sample_mean=grp1_sample_mean,
115		grp1_sample_sd=grp1_sample_sd,
116		grp1_sample_size=grp1_sample_size,
117		grp2_sample_mean=grp2_sample_mean,
118		grp2_sample_sd=grp2_sample_sd,
119		grp2_sample_size=grp2_sample_size)
120		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.student_t)
121		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
122		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
123		print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
124
125		def test_confidence_interval_with_sample_student(self):
126		grp1_mu = 0.0
127		grp1_sigma = 1.0
128		grp1_sample_size = 29
129		grp1_sample = Sample()
130		grp2_mu = 0.08
131		grp2_sigma = 1.1
132		grp2_sample_size = 27
133		grp2_sample = Sample()
134
135		for i in range(grp1_sample_size):
136		grp1_sample.add_numeric(normal(grp1_mu, grp1_sigma))
137		for i in range(grp2_sample_size):
138		grp2_sample.add_numeric(normal(grp2_mu, grp2_sigma))
139
140		sampling_distribution = MeanDiffSamplingDistribution(grp1_sample_distribution=SampleDistribution(grp1_sample),
141		grp2_sample_distribution=SampleDistribution(grp2_sample))
142		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.student_t)
143		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
144		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
145		print('confidence interval for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
146
147
148		class ProportionSamplingDistributionUnitTest(unittest.TestCase):
149		def test_confidence_interval_with_sample_stats_normal(self):
150		sample_proportion = 0.6
151		sample_size = 31
152		sampling_distribution = ProportionSamplingDistribution(sample_proportion=sample_proportion,
153		sample_size=sample_size)
154		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
155		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
156		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
157		print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
158
159		def test_confidence_interval_with_sample_normal(self):
160		sample = Sample()
161
162		for i in range(100):
163		if random() <= 0.6:
164		sample.add_category("OK")
165		else:
166		sample.add_category("CANCEL")
167
168		sampling_distribution = ProportionSamplingDistribution(sample_distribution=SampleDistribution(sample,
169		categorical_value="OK"))
170		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
171		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
172		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
173		print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
174
175		def test_confidence_interval_with_sample_stats_simulation(self):
176		sample_proportion = 0.6
177		sample_size = 10
178		sampling_distribution = ProportionSamplingDistribution(sample_proportion=sample_proportion,
179		sample_size=sample_size)
180		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation)
181		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
182		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
183		print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
184
185		def test_confidence_interval_with_sample_simulation(self):
186		sample = Sample()
187
188		for i in range(10):
189		if random() <= 0.6:
190		sample.add_category("OK")
191		else:
192		sample.add_category("CANCEL")
193
194		sampling_distribution = ProportionSamplingDistribution(sample_distribution=SampleDistribution(sample,
195		categorical_value="OK"))
196		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation)
197		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
198		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
199		print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
200
201
202		class ProportionDiffSamplingDistributionUnitTest(unittest.TestCase):
203		def test_confidence_interval_with_sample_stats_normal(self):
204		grp1_sample_proportion = 0.6
205		grp1_sample_size = 31
206		grp2_sample_proportion = 0.51
207		grp2_sample_size = 32
208		sampling_distribution = ProportionDiffSamplingDistribution(grp1_sample_proportion=grp1_sample_proportion,
209		grp1_sample_size=grp1_sample_size,
210		grp2_sample_proportion=grp2_sample_proportion,
211		grp2_sample_size=grp2_sample_size)
212		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
213		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
214		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
215		print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
216
217	View Code Duplication	def test_confidence_interval_with_sample_normal(self):
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218		grp1_sample = Sample()
219		grp2_sample = Sample()
220
221		for i in range(100):
222		if random() <= 0.6:
223		grp1_sample.add_category("OK")
224		else:
225		grp1_sample.add_category("CANCEL")
226
227		for i in range(100):
228		if random() <= 0.61:
229		grp2_sample.add_category("OK")
230		else:
231		grp2_sample.add_category("CANCEL")
232
233		sampling_distribution = ProportionDiffSamplingDistribution(grp1_sample_distribution=SampleDistribution(
234		grp1_sample, categorical_value="OK"),
235		grp2_sample_distribution=SampleDistribution(
236		grp2_sample, categorical_value="OK"))
237		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal)
238		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
239		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
240		print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
241
242		def test_confidence_interval_with_sample_stats_simulation(self):
243		grp1_sample_proportion = 0.6
244		grp1_sample_size = 10
245		grp2_sample_proportion = 0.61
246		grp2_sample_size = 9
247		sampling_distribution = ProportionDiffSamplingDistribution(grp1_sample_proportion=grp1_sample_proportion,
248		grp1_sample_size=grp1_sample_size,
249		grp2_sample_proportion=grp2_sample_proportion,
250		grp2_sample_size=grp2_sample_size
251		)
252		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation)
253		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
254		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
255		print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
256
257	View Code Duplication	def test_confidence_interval_with_sample_simulation(self):
		0 ignored issues – show Duplication introduced 2017-05-30 03:40 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
258		grp1_sample = Sample()
259		grp2_sample = Sample()
260
261		for i in range(10):
262		if random() <= 0.6:
263		grp1_sample.add_category("OK")
264		else:
265		grp1_sample.add_category("CANCEL")
266
267		for i in range(9):
268		if random() <= 0.61:
269		grp2_sample.add_category("OK")
270		else:
271		grp2_sample.add_category("CANCEL")
272
273		sampling_distribution = ProportionDiffSamplingDistribution(
274		grp1_sample_distribution=SampleDistribution(grp1_sample,
275		categorical_value="OK"),
276		grp2_sample_distribution=SampleDistribution(
277		grp2_sample,
278		categorical_value="OK")
279		)
280		self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation)
281		print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate)
282		+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')')
283		print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95)))
284
285
286		if __name__ == '__main__':
287		unittest.main()
288

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ProportionDiffSamplingDistributionUnitTest A last analyzed 2017-06-16 07:13 UTC

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ProportionDiffSamplingDistributionUnitTest A
last analyzed 2017-06-16 07:13 UTC