def CochranSampleSize(data):
p = 0.5
q = 1 - p
PQ = product(p, q)
List = []
List1 = []
for i in MarginError(data):
List.append(square(i))
for i in Z_scores(z_values(data)):
List1.append(square(i))
i = 0
n = []
while i < len(List):
n.append(round(product(List[i], PQ) / List1[i]))
i += 1
return n
def SampleSize_withStd(data):
List = []
List1 = []
E = MarginError(data)
K = mean_confidence_interval(data)
for i in K:
Z = i[1] / 2
List.append(scipy.stats.norm.cdf(Z))
i = 0
while i < len(List):
x = product(List[i], StdDevSample(data))
y = round(division(x, E[i]))
List1.append((square(y)))
i += 1
return List1
def Sample_Correlation(list1, list2):
n = len(list1)
avg_x = average(list1)
avg_y = average(list2)
rod = 0
x2 = 0
y2 = 0
for i in range(n):
x = subtraction(list1[i], avg_x)
y = subtraction(list2[i], avg_y)
rod += product(x, y)
x2 += square(x)
y2 += square(y)
return rod / squareRoot(x2 * y2)
def SampleSize_withoutStd(data):
E = MarginError(data) # return a list
p = 0.5
q = 1 - p
PQ = product(q, p)
List = []
List1 = []
x = mean_confidence_interval(data)
for i in x:
Z = i[1] / 2
List.append(scipy.stats.norm.cdf(Z))
ME = []
for i in E:
ME.append(i / 2)
i = 0
while i < len(ME):
ZE = List[i] / ME[i]
x = round(square(ZE) * PQ)
List1.append(x)
i += 1
return List1
def Product(self, a, b):
if typeFunction(a) == True and typeFunction(b) == True:
self.result = product(a, b)
return self.result