class MyTestCase(unittest.TestCase):
def setUp(self) -> None:
self.statistics = Statistics('Tests/Data/StatCalcData.csv')
def test_instantiate_calculator(self):
self.assertIsInstance(self.statistics, Statistics)
def test_decorator_calculator(self):
self.assertIsInstance(self.statistics, Statistics)
def test_Population_Mean_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
answer = CsvReader('Tests/Data/StatAnswers.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
for column in answer:
result = float((column['mean']))
self.assertEqual(self.statistics.newmean(lst), result)
def test_Sample_Mean_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
x, z = self.statistics.sammean(lst)
self.assertEqual(x, z)
def test_Median_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
answer = CsvReader('Tests/Data/StatAnswers.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
for column in answer:
result = float((column['median']))
self.assertEqual(self.statistics.med(lst), result)
def test_Mode_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
answer = CsvReader('Tests/Data/StatAnswers.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
for column in answer:
result = float((column['mode']))
self.assertEqual(self.statistics.mod(lst), result)
def test_Population_Standard_Deviation_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
answer = CsvReader('Tests/Data/StatAnswers.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
for column in answer:
result = float((column['stdev']))
self.assertEqual(self.statistics.stddev(lst), result)
def test_Sample_Standard_Deviation_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
x, z = self.statistics.sampstdev(lst)
x = round(x, 3)
z = round(z, 3)
self.assertEqual(x, z)
def test_confidence_interval_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
conf = 95
self.assertEqual(self.statistics.confintv(lst, conf), (132.67, 121.07))
def test_zscore_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
answer = CsvReader('Tests/Data/StatAnswers.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
for column in answer:
result = float((column['zscore']))
self.assertEqual(self.statistics.z_score(lst), result)
def test_population_variance_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
answer = CsvReader('Tests/Data/StatAnswers.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
for column in answer:
result = float((column['pop_variance']))
self.assertEqual(self.statistics.pvariance(lst), result)
def test_proportion_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
answer = CsvReader('Tests/Data/StatAnswers.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
for column in answer:
result = float((column['proportion']))
self.assertEqual(self.statistics.proportion(lst), result)
def test_variance_population_proportion_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
answer = CsvReader('Tests/Data/StatAnswers.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
for column in answer:
result = float((column['var_pop_prop']))
self.assertEqual(self.statistics.vpop_proportion(lst), result)
def test_variance_sample_proportion_calculator(self):
test_data = CsvReader('Tests/Data/StatCalcData.csv').data
lst = []
for row in test_data:
y = int(row['Value 1'])
lst.append(y)
x = self.statistics.vsamp_proportion(lst)
self.assertEqual(x, x)
def test_corr(self):
x = self.statistics.corcof()
self.assertEqual(x, 25)
class MyTestCase(unittest.TestCase):
def setUp(self) -> None:
self.statistics = Statistics('Tests/Data/StatCalcData.csv')
def test_instantiate_calculator(self):
self.assertIsInstance(self.statistics, Statistics)
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.newmean(lst),
float((column['mean'])))
self.assertNotEqual(self.statistics.newmean(lst),
float((column['mean'])) * 2,
"Mean does not match")
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_Median_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.med(lst), float(
(column['median'])))
self.assertNotEqual(self.statistics.med(lst),
float((column['median'])) + 2,
"Incorrect Median")
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_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_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_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_zscore_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.z_score(lst),
float((column['zscore'])))
self.assertNotEqual(self.statistics.z_score(lst),
float((column['zscore'])) * 2,
"Incorrect Z Score")
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_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_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_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")
def test_correlation_coefficient_calculator(self):
test_data = CsvReader('Tests/Data/SampleData.csv').data
answer = CsvReader('Tests/Data/StatAnswers.csv').data
x_data = []
y_data = []
for row in test_data:
x = int(row['x'])
x_data.append(x)
y = int(row['y'])
y_data.append(y)
z = self.statistics.corcof(x_data, y_data)
for column in answer:
self.assertEqual(z, float((column['corr_coff'])))
self.assertNotEqual(z,
float((column['corr_coff'])) + 1,
"Wrong Corr Coefficient")
def test_pvalue_calculator(self):
test_data = CsvReader('Tests/Data/Ztable.csv').data
answer = CsvReader('Tests/Data/StatAnswers.csv').data
z_data = []
p_data = []
for row in test_data:
x = float(row['z_score'])
z_data.append(x)
y = float(row['p_value'])
p_data.append(y)
x = self.statistics.p_value(z_data, p_data)
for column in answer:
self.assertEqual(x, float((column['p_value'])))
self.assertNotEqual(x,
float((column['p_value'])) - 3,
"Incorrect P Value")