def popcorrelation(data1, data2):
covxy = 0
n = 0
xmean = Mean.mean(data1)
ymean = Mean.mean(data2)
xstd = StandardDeviation.stardardDev(data1)
ystd = StandardDeviation.stardardDev(data2)
if (len(data1) == len(data2)):
for x, y in zip(data1, data2):
n += 1
covxy += abs((int(x - xmean) * int(y - ymean)) / n)
return covxy / (xstd * ystd)
def samplecorrelation(data1, data2):
numerator = 0
n = 0
meanofx = Mean.mean(data1)
meanofy = Mean.mean(data2)
stdevx = StandardDeviation.stardardDev(data1)
stdevy = StandardDeviation.stardardDev(data2)
if len(data1) == len(data2):
for x, y in zip(data1, data2):
n += 1
numerator += abs(
(int(x - meanofx) * int(y - meanofy)) / (n - 1))
return numerator / (stdevx * stdevy)
def setUp(self):
self.sample = Sample()
self.list1 = Randm.randList(1, 100, 30, 99)
self.size = len(self.list1)
self.standarddev = StandardDeviation.stardardDev(self.list1)
self.merror = 3
self.mean = Mean.mean(self.list1)
def confInterval(data, cval=0.95):
mean = Mean.mean(data)
standardDev = StandardDeviation.stardardDev(data)
n = len(data)
zscr = ZScore.zscore(cval)
num = round(zscr * (standardDev / nthroot.root(2, n)), 2)
return round((mean - num), 2), round(mean), round((mean + num), 2)
def meanDeviation(data):
m = Mean.mean(data)
total = 0
l = len(data)
for i in data:
total = total + absolute(i - m)
return (total / l)
def confInterval(data):
zvalue = ZScore.zscore(data)
stdDev = StandardDeviation.stardardDev(data)
mean = Mean.mean(data)
n = len(data)
num = round(zvalue * (stdDev / nthroot.root(2, n)), 2)
return round((mean - num), 2), round(mean), round((mean + num), 2)
def zscore(data):
newData = []
mean = Mean.mean(data)
stdDev = StandardDeviation.stardardDev(data)
for i in data:
newData.append((i - mean)/stdDev)
return newData
def stardardDev(data):
n = len(data)
mn = Mean.mean(data)
newlist = []
for i in data:
newlist.append(Exponentiation.power(i - mn, 2))
total = Addition.sumList(newlist)
return nthroot.root(2, Division.divide(total, n))
def meanDev(data):
newlist = []
meanOfData = Mean.mean(data)
for i in data:
newlist.append(abs(i - meanOfData))
total = Addition.sumList(newlist)
return Division.divide(total, len(data))
def confidenceIntervalPop(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 test_StatisticFunctions_Mean(self):
self.assertEqual(38.95, Mean.mean(self.testData))
def meanDeviation(data):
return Mean.mean(absolute(asarray(data) - Mean.mean(data)))
def mean(self, data):
self.result = Mean.mean(data)
return self.result
def variance(data):
return Mean.mean(absolute(asarray(data) - Mean.mean(data))**2)
def skewness(data):
mean = Mean.mean(data)
median = Median.med(data)
stdDev = StandardDeviation.stardardDev(data)
return (3 * (mean - median)) / stdDev