def confidenceInterval(array, ci):
df = len(array) - 1
alpha = 1 - ci
tval = stats.t.ppf(1 - alpha, df)
stdev = Stats.standardDeviation(array)
se = stdev / math.pow(len(array), .5)
numerate = se * tval
mean = Stats.mean(array)
return [mean - numerate, mean + numerate]
def known_pop_sample(data):
zs = Stats.zscore(data)
me = Sampling.marginError(data)
sd = Stats.standardDeviation(data)
value = (zs * sd) / me
popSample = value**0.5
return popSample
def marginError(data):
zs = Stats.zscore(data)
sd = Stats.standardDeviation(data)
margin = zs * sd
return margin
def Standarddeviation(self, a):
self.Result = Stats.standardDeviation(a)
return self.Result
def test_Stats_Standard_Deviation(self):
self.assertEqual(6.109737219299115,
Stats.standardDeviation(self.testData))