def pop_correlation_coefficient(data_x, data_y):
x = pop_stand_dev(data_x)
y = pop_stand_dev(data_y)
divisor = multiplication(x, y)
d = population_mean(data_x)
e = population_mean(data_y)
a = [(element - d) for element in data_x]
b = [(element - e) for element in data_y]
size = len(a)
product = [a[i] * b[i] for i in range(size)]
total = sum(product)
covariance = division(size, total)
d = division(divisor, covariance)
return d
def z_score(numbers):
row_value = 151
std_dev = pop_stand_dev(numbers)
mean = population_mean(numbers)
result = subtraction(row_value, mean)
z_score_ = division(result, std_dev)
print(z_score_)
return z_score_
def sample_std_dev(data):
total = 0
samples = random.randint(1, len(data))
new_samples = get_sample(data, samples)
new_mean = population_mean(new_samples)
for number in new_samples:
result = subtraction(number, new_mean)
sq = square(result)
total = addition(total, sq)
n = len(new_samples)
d = division(subtraction(1, n), total)
sample_sd = sq_rt(d)
return sample_sd
def confidence_interval(data):
z_value = 1.960
mean = population_mean(data)
sd = pop_stand_dev(data)
x = len(data)
y = division(sq_rt(x), sd)
margin_of_error = multiplication(z_value, y)
a = [subtraction(mean, margin_of_error)]
b = [addition(mean, margin_of_error)]
size = len(a)
lower = a[0]
upper = b[0]
return lower, upper
def population_variance(data):
pop = population_mean(data)
length = len(data)
return round(
division(length, sum([(element - pop)**2 for element in data])), 3)
def population_mean(self, a):
self.result = population_mean(a)
return self.result
def pop_stand_dev(data):
pop = population_mean(data)
length = len(data)
return round(sq_rt(sum([(element - pop)**2 for element in data]) / length),
3)