def f_0(self):
data_list = self.data_list
z_instance = ZScore()
mean_instance = ArithMean()
varc_instance = Variance()
std_instance = Std_dev()
mean = mean_instance.mean(data_list)
varc = varc_instance.varc(data_list, mean)
std_dev = std_instance.Std_calc(varc)
z_list = []
for i in range(0, len(data_list)):
z = z_instance.z_score(mean, std_dev, data_list[i])
z_list.insert(i, z)
return z_list
def asymp_sig(self):
dif = self.differnte()
value_list = self.z_value()
value = value_list[1]
print(value)
mean_instance = ArithMean()
std_instance = Std_dev()
var_instance = Variance()
mean = mean_instance.mean(dif)
varc = var_instance.varc(dif, mean)
std = std_instance.Std_calc(varc)
z_instance = ZScore()
Asymp = 1 - (2 * z_instance.z_score_value(value))
print(z_instance.z_score_value(value))
return Asymp