def test_confidence_interval_calculator(self): test_data = CsvReader('Tests/Data/StatCalcData.csv').data lst = data_add(test_data) conf = 95 self.assertEqual(self.statistics.confintv(lst, conf), (132.67, 121.07)) self.assertNotEqual(self.statistics.confintv(lst, conf), (134.67, 123.07), "Incorrect Confidence Interval")
def test_Mode_calculator(self): test_data = CsvReader('Tests/Data/StatCalcData.csv').data answer = CsvReader('Tests/Data/StatAnswers.csv').data lst = data_add(test_data) for column in answer: self.assertEqual(self.statistics.mod(lst), float((column['mode']))) self.assertNotEqual(self.statistics.mod(lst), float((column['mode'])) - 2, "Incorrect Mode")
def test_Sample_Standard_Deviation_calculator(self): test_data = CsvReader('Tests/Data/StatCalcData.csv').data lst = data_add(test_data) x, z = self.statistics.sampstdev(lst) x = round(x, 3) z = round(z, 3) self.assertEqual(x, z) self.assertNotEqual(x, z * 2, "Sample Std Deviation is incorrect")
def test_population_variance_calculator(self): test_data = CsvReader('Tests/Data/StatCalcData.csv').data answer = CsvReader('Tests/Data/StatAnswers.csv').data lst = data_add(test_data) for column in answer: self.assertEqual(self.statistics.pvariance(lst), float((column['pop_variance']))) self.assertNotEqual(self.statistics.pvariance(lst), float((column['pop_variance'])) - 3, "Wrong Pop Var")
def test_Population_Standard_Deviation_calculator(self): test_data = CsvReader('Tests/Data/StatCalcData.csv').data answer = CsvReader('Tests/Data/StatAnswers.csv').data lst = data_add(test_data) for column in answer: self.assertEqual(self.statistics.stddev(lst), float((column['stdev']))) self.assertNotEqual(self.statistics.stddev(lst), float((column['stdev'])) * 3, "Wrong Pop Std Deviation")
def test_Population_Mean_calculator(self): test_data = CsvReader('Tests/Data/StatCalcData.csv').data answer = CsvReader('Tests/Data/StatAnswers.csv').data lst = data_add(test_data) for column in answer: self.assertEqual(self.statistics.mean(lst), float( (column['mean']))) self.assertNotEqual(self.statistics.mean(lst), float((column['mean'])) * 2, "Mean does not match")
def test_variance_population_proportion_calculator(self): test_data = CsvReader('Tests/Data/StatCalcData.csv').data answer = CsvReader('Tests/Data/StatAnswers.csv').data lst = data_add(test_data) for column in answer: self.assertEqual(self.statistics.vpop_proportion(lst), float((column['var_pop_prop']))) self.assertNotEqual(self.statistics.vpop_proportion(lst), float((column['var_pop_prop'])) - 2, "WrongResult")
def test_proportion_calculator(self): test_data = CsvReader('Tests/Data/StatCalcData.csv').data answer = CsvReader('Tests/Data/StatAnswers.csv').data lst = data_add(test_data) for column in answer: self.assertEqual(self.statistics.proportion(lst), float((column['proportion']))) self.assertNotEqual(self.statistics.proportion(lst), float((column['proportion'])) - 2, "Wrong Proportion")
def test_Sample_Mean_calculator(self): test_data = CsvReader('Tests/Data/StatCalcData.csv').data lst = data_add(test_data) x, z = self.statistics.sammean(lst) self.assertEqual(x, z) self.assertNotEqual(x, z * 2, "Sample Mean does not match")
def test_variance_sample_proportion_calculator(self): test_data = CsvReader('Tests/Data/StatCalcData.csv').data lst = data_add(test_data) x = self.statistics.vsamp_proportion(lst) self.assertEqual(x, x) self.assertNotEqual(x, x + 2, "Wrong Var Samp Proportion")