def correlation(data, data1):
z = zscore(data)
z1 = zscore(data1)
ztot = list(map(lambda x, y: x * y, z, z1))
corr = division(sum(ztot), len(ztot))
return round(corr, 4)
def correlation(data, data1):
try:
z1 = zscore(data)
z2 = zscore(data1)
z1_z2 = list(map(lambda x, y: x * y, z1, z2))
corr = sum(z1_z2) / len(z1_z2)
return round(corr, 7)
except ZeroDivisionError:
print("Error: Can't Divide by 0")
except ValueError:
print("Error: Check your data inputs")
def p_value(numbers):
x = []
n = len(numbers)
z = zscore(numbers)
for i in y:
if i == z:
x.append(y)
return y
def confidence_interval(number_list, stand_dev):
size = len(number_list)
z_val = zscore(number_list)
value_sum = 0
for x in number_list:
value_sum = value_sum + x
mean = value_sum / size
result = mean + (z_val * (stand_dev / math.sqrt(size)))
return result
def population_correlation_coefficient(numbers, numbers1):
m = zscore(numbers)
n = zscore(numbers1)
value = list(map(lambda a, b: a * b, m, n))
p = division(len(value), sum(value))
return p
def zscore(self, data):
validate(data)
self.result = zscore(data)
return self.result
def z_score(self, a):
self.result = zscore(a)
return self.result
def population_z_score(self, a):
self.result = zscore(a)
return self.result
def pvalue(num):
return zscore(num)
def zscore(self, data):
self.result = zscore(data)
return self.result
def zscore(self, number_list):
self.result = zscore(number_list)
return self.result
def zscore(self, nums):
self.data = zscore(nums)
return self.data
