def z_score(data):
u = population_mean(data)
new_data = [float(x) for x in data]
x = new_data[1]
pop_sd = pop_stand_dev(new_data)
y = subtraction(x, u)
z = division(pop_sd, y)
return z
def pop_correlation_coefficient(data):
x_data = CsvReader('Tests/Data/female_height.csv').data
y_data = CsvReader('Tests/Data/male_height.csv').data
x = pop_stand_dev(x_data)
y = pop_stand_dev(y_data)
divisor = multiplication(x, y)
z = len(data)
# Covariance calculation:
a = subtraction(data, sampleMean)
b = subtraction(data, population_mean)
c = multiplication(a, b)
covariance = division(z, (sum(c)))
# Population Correlation Coefficient calculation:
d = division(divisor, covariance)
return d
def pop_correlation_coefficient(data_x, data_y):
x = pop_stand_dev(data_x)
y = pop_stand_dev(data_y)
divisor = multiplication(x, y)
# Covariance calculation:
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)
# Population Correlation Coefficient calculation:
d = division(divisor, covariance)
return d
def confidence_interval(data):
# For a Confidence Interval of 95%
z_value = 1.960
mean = sampleMean(data)
sd = pop_stand_dev(data)
x = len(data)
y = division(square_root(x), sd)
margin_of_error = multiplication(z_value, y)
a = subtraction(mean, margin_of_error)
b = addition(mean, margin_of_error)
return a, b
def pop_correlation_coefficient(data):
# x_data = CsvReader('Tests/Data/female_height.csv').data
# y_data = CsvReader('Tests/Data/male_height.csv').data
x_data = [num for elem in data for num in elem]
y_data = [num for elem in data for num in elem]
new_x_data = [float(x) for x in x_data]
new_y_data = [float(x) for x in y_data]
x = pop_stand_dev(new_x_data)
y = pop_stand_dev(new_y_data)
divisor = multiplication(x, y)
z = len(new_x_data)
# Covariance calculation:
a = subtraction(new_x_data, population_mean(new_x_data))
b = subtraction(new_y_data, population_mean(new_y_data))
c = multiplication(a, b)
covariance = division(z, (sum(c)))
# Population Correlation Coefficient calculation:
d = division(divisor, covariance)
return d
def confidence_interval(data):
data = [num for elem in data for num in elem]
new_data = [float(x) for x in data]
# For a Confidence Interval of 95%
z_value = 1.960
mean = sampleMean(new_data)
sd = pop_stand_dev(new_data)
x = len(new_data)
y = division(square_root(x), sd)
margin_of_error = multiplication(z_value, y)
a = subtraction(mean, margin_of_error)
b = addition(mean, margin_of_error)
return a, b
def confidence_interval(data):
# For a Confidence Interval of 95%
z_value = 1.960
mean = population_mean(data)
sd = pop_stand_dev(data)
x = len(data)
y = division(square_root(x), sd)
margin_of_error = multiplication(z_value, y)
a = [subtraction(mean, margin_of_error)]
b = [addition(mean, margin_of_error)]
size = len(a)
# c = [(a[i], b[i]) for i in range(size)]
lower = a[0]
upper = b[0]
# print(lower, upper)
return lower, upper
def population_st_dev(self, data):
self.result = pop_stand_dev(data)
return self.result