def cochran(data, seeds, nums):
ZScore = Zsc.zsc(seeds,data)
pp = PopulationProportion.populationPorportion(seeds, nums, data)
MarOfError = MarginOfError.marginOfError(seeds, data)
sub = Subtraction.difference(1, pp)
cochran = (Exponential.exponential(ZScore, 2) * pp * sub) / Exponential.exponential(MarOfError, 2)
return cochran
def cochran(data, seeds, nums):
z_score = Z_score.z_score(seeds, data)
prop = PopProportion.popProportion(seeds, nums, data)
marginError = MarginOfError.marginOfError(seeds, data)
diff = Subtraction.difference(1, prop)
return (Exponent.power(z_score, 2) * prop * diff) / Exponent.power(
marginError, 2)
def unknownPopulationStdev(seed, data, percent):
ZScore = Zsc.zsc(seed, data)
MarOfError = MarginOfError.marginOfError(seed, data)
p = percent
q = Subtraction.difference(1, p)
pq = Division.division(ZScore, MarOfError)
samplePopulation = Exponential.exponential(pq, 2) * p * q
return samplePopulation
def unknownPopStdDev(seed, data, percent):
z_score = Z_score.z_score(seed, data)
marginError = MarginOfError.marginOfError(seed, data)
x = percent
y = Subtraction.difference(1, x)
xy = Division.quotient(z_score, marginError)
sample = Exponent.power(xy, 2) * x * y
return sample
def test_MathOperations_subtraction(self):
self.assertEqual(-1, Subtraction.difference(1, 2))
def Difference(self, a, b):
self.Result = Subtraction.difference(a, b)
return self.Result
def medianskewness(data):
ni = (Subtraction.difference(Mean.mean(data), Median.median(data)))
nii = Division.division(ni, Stddev.stddev(data))
return nii
def modeskewness(data):
n = (Subtraction.difference(Mean.mean(data), Mode.mode(data)))
nn = Division.division(n, Stddev.stddev(data))
return nn
def test_calculator_subtraction(self):
self.assertEqual(-1, Subtraction.difference(1, 2))
def test_MathOperations_diff_list(self):
sublist = [8, 4, 2]
self.assertEqual(2, Subtraction.difference(sublist))
def Difference(a, b):
return Subtraction.difference(a, b)
