def created_sample_correlation(datax, datay):
meanx = created_mean(datax)
meany = created_mean(datay)
sumprod = 0.0
sumx = 0.0
sumy = 0.0
for k in range(len(datax)):
temp = multiply(subtract(datax[k], meanx), subtract(datay[k], meany))
sumprod = add(sumprod, temp)
sumx = add(sumx, square(subtract(datax[k], meanx)))
sumy = add(sumy, square(subtract(datay[k], meany)))
corr = division(sumprod, multiply(square_root(sumx), square_root(sumy)))
return "{0:.2f}".format(round(corr, 2))
def created_variance2(data):
meanx = created_mean(data)
sumx = 0.0
for k in range(len(data)):
temp = square(subtract(data[k], meanx))
sumx = add(sumx, temp)
return "{0:.2f}".format(round(sumx / (len(data)), 2))
def created_mean_deviation(datax):
meanx = created_mean(datax)
sumx = 0.0
for k in range(len(datax)):
diff = abs(subtract(datax[k], meanx))
sumx = add(sumx, diff)
mean_dev = division(sumx, len(datax))
return "{0:.2f}".format(round(mean_dev, 2))
def created_zscore(datax):
meanx = created_mean(datax)
stdx = square_root(created_variance2(datax))
zscore = []
for k in range(len(datax)):
diff = subtract(datax[k], meanx)
zscore.append(division(diff, stdx))
zscore = ["%.2f" % item for item in zscore]
return [float(k) for k in zscore]
def test_subtract(self):
self.assertEqual(subtract(30,10), 20.0)