def cochran(data, lstLen, seed):
z_s = Z_Score.zscore(data, seed)
p_p = PopulationProportion.proportion(data, lstLen, seed)
m_e = MarginError.margin(data, seed)
q = subtraction(1, p_p)
cochran = (exponentiation(z_s, 2) * p_p * q) / exponentiation(m_e, 2)
return cochran
def cochran(theSeed, data, rangeNumber):
# z = z-score
# p = proportion population
# e = margin of error
z = Z_score.z_score(theSeed, data)
p = PopulationProportion.proportion(theSeed, data, rangeNumber)
e = MarginOfError.margin(theSeed, data)
q = 1 - p
cochran = (exponentiation(z, 2) * p * q) / exponentiation(e, 2)
return cochran
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 sampleSize(theSeed, data, percentage):
'''
z = z-score
e = margin of error
p = percentage
q = 1 -p
'''
z = Z_score.z_score(theSeed, data)
e = MarginOfError.margin(theSeed, data)
p = percentage
q = 1 - p
val = z / e
sample = exponentiation(val, 2) * p * q
return sample
def test_exponentiation(self):
self.assertEqual(9, exponentiation(3, 2))