def knownSamplesize(seed, data):
z = Zsc.zsc(seed, data)
Mar = MarginOfError.marginOfError(seed, data)
StDe = Stddev.stddev(data)
value = (z * StDe) / Mar
sam = Exponential.exponential(value, 2)
return sam
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 populationcorrelation(data1, data2):
cov = Covariance.covariance(data1, data2)
stdDev1 = Stddev.stddev(data1)
stdDev2 = Stddev.stddev(data2)
return cov / (stdDev1 * stdDev2)
def test_stddev(self):
self.assertEqual(1.5118578920369088, Stddev.stddev(self.test))
def zsc(seed, data):
X = RandPick.randPickSeed(seed, data)
mean = Mean.mean(data)
stddev = Stddev.stddev(data)
return (X - mean) / stddev
def samplecorrelation(seed, sample_size, data1, data2):
dataA = RandPick.randPickListSeed(seed, sample_size, data1)
dataB = RandPick.randPickListSeed(seed, sample_size, data2)
return Covariance.covariance(dataA, dataB) / (Stddev.stddev(dataA) * Stddev.stddev(dataB))