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")
