def correlation(theSeed, dataA, dataB):
# getting sample from population
sampleDataA = PickNumbersSeed.pickNumbers(theSeed, dataA, 5)
sampleDataB = PickNumbersSeed.pickNumbers(theSeed, dataB, 5)
cov = Covariance.covariance(sampleDataA, sampleDataB)
stdDevA = StandardDeviation.standardDeviation(sampleDataA)
stdDevB = StandardDeviation.standardDeviation(sampleDataB)
return cov / (stdDevA * stdDevB)
def sampleSize(theSeed, data):
'''
z = z-score
e = margin of error
s = standard deviation
'''
z = Z_score.z_score(theSeed, data)
e = MarginOfError.margin(theSeed, data)
stdDev = StandardDeviation.standardDeviation(data)
val = (z * stdDev) / e
sample = exponentiation(val, 2)
return sample
def test_standardDeviation(self):
standardDeviation = StandardDeviation.standardDeviation(self.testData)
self.assertEqual(standardDeviation, 14.01364414498321)
def z_score(theSeed, data):
X = PickSeed.pickSeed(theSeed, data)
mean = Mean.mean(data)
stdDev = StandardDeviation.standardDeviation(data)
return (X - mean) / stdDev
def correlation(dataA, dataB):
cov = Covariance.covariance(dataA, dataB)
stdDevA = StandardDeviation.standardDeviation(dataA)
stdDevB = StandardDeviation.standardDeviation(dataB)
return cov/(stdDevA*stdDevB)
def margin(theSeed, data):
z_score = Z_score.z_score(theSeed, data)
stdDev = StandardDeviation.standardDeviation(data)
return z_score * stdDev