def proportion(data):
p = len(data)
height = 0
for values in data:
if height > 64:
addition(height)
return division(values, p)
def sample_st_dev(data):
mean = sampleMean(data)
sample_data = data[0:999]
tot = 0.0
for x in sample_data:
tot = addition(tot, (x - mean)**2)
return round((tot / (len(sample_data) - 1))**0.5, 2)
def sampleMean(data, sample_size):
total = 0
sample = sampleData(data, sample_size)
sample_values = len(sample)
for value in sample:
total = addition(total, value)
return division(total, sample_values)
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 sample_st_dev(data, sample_size):
dev = 0
sample = sampleData(data, sample_size)
sample_values = len(sample)
x_bar = sampleMean()
x = sample_values
n = subtraction(sample_values, 1)
for dev in sample:
dev = subtraction(x, x_bar)
square_x_bar = square(dev)
add = addition(square_x_bar, square_x_bar)
divide = division(add, n)
return square_root(divide)
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 add(self, a, b):
self.result = addition(float(a), float(b))
return self.result
def population_mean(data):
n = len(data)
total = 0
for item in data:
total = addition(total, item)
return division(total, n)