def meanDeviation(data):
b = Mean.mean(data)
total = 0
v = len(data)
for i in data:
total = total + absolute(i - b)
return total / v
def variance(data):
m = Mean.mean(data)
total = 0
l = len(data)
for i in data:
total = total + (i - m)**2
return total / l
def confidenceInterv(conf, data):
lngth = len(data)
mean = Mean.mean(data)
std_err = sem(data)
high = std_err * t.ppf((1 + conf) / 2, lngth - 1)
start = mean - high
end = mean + high
return start, end
def confidence_interval(confidence, data):
ld = len(data)
mn = Mean.mean(data)
std_er = sem(data)
high = std_er * t.ppf((1 + confidence) / 2, ld - 1)
start = mn - high
end = mn + high
return start, end
def zscore(data, seed):
n = ItemReturnType.random_num_seed(data, seed)
mn = Mean.mean(data)
sd = StandardDeviation.standard_deviance(data)
zs = (n - mn) / sd
return zs
def test_StatisticFunctions_Mean(self):
self.assertEqual(2.5, Mean.mean(self.testData))
def __init__(self):
self.result = Mean.mean(data)
def zscore(sd, data):
X = PickSeed.pickSeed(sd, data)
meanData = Mean.mean(data)
sd = StandardDeviation.standardDeviation(data)
z = Division.divide(X - meanData, sd)
return z
def test_mean(self):
mean = Mean.mean(data=self.testData)
self.assertEqual(mean, 25.466666666666665)