def confidence_interval(num):
x1 = population_mean(num)
c = 0.95
z_value = (1 - c) / 2
d1 = population_standard_deviation(num)
l1 = square_root(len(num))
return [x1 - z_value * d1 / l1, x1 + z_value * d1 / l1]
def z_score(num):
average1 = population_mean(num)
deviation1 = population_standard_deviation(num)
score_list = []
for x in num:
f = (x - average1) / deviation1
score_list.append(f)
return score_list
def population_variance(num):
deviation = population_standard_deviation(num)
return square(deviation)
def Sample_Standard_Deviation(num):
sample_average = sample_mean(num)
return population_standard_deviation(sample_average)
def population_standard_deviation(self, data):
self.result = population_standard_deviation(data)
return self.result
def Sample_Standard_Deviation(num):
return population_standard_deviation(num)
def population(self, a, b):
self.result = population_standard_deviation(a, b)
return self.result