def zscore(numbers):
row_value = 151
sd = psd(numbers)
m = mean(numbers)
result = subtraction(row_value, m)
z_score = division(result, sd)
print(z_score)
return z_score
def pop_corr_coeff(numbers):
num_value = len(numbers)
# Calculation of covariance
result1 = subtraction(numbers, sample_mean)
result2 = subtraction(numbers, sample_mean)
result3 = multiplication(result1, result2)
covariance = division(num_value, sum(result3))
# denominator
data1 = CsvReader('Tests/Data/pop_corr_data1').numbers
data2 = CsvReader('Tests/Data/pop_corr_data2').numbers
result4 = psd(data1)
result5 = psd(data2)
result6 = multiplication(result4, result5)
population_corr_coeff = division(result6, covariance)
return population_corr_coeff
def conf_interval(data):
x = mean(data)
dev = psd(data)
z = 1.96 # for 95% confidence
standard_error = division(dev, squareroot(len(data)))
conf_upper_level = round(addition(x, multiplication(z, standard_error)), 2)
conf_lower_level = round(subtraction(multiplication(z, standard_error), x), 2)
return conf_upper_level, conf_lower_level
def psd(self):
d = []
for row in self.data.data:
d.append(row['v'])
self.result = psd(d)
return self.result
def Psd(self, po):
self.result = psd(po)
return self.result
def zscore(numbers):
row_value = 484
sd = psd(numbers)
m = mean(numbers)
result = subtraction(m, row_value)
return division(sd, result)