def var_sample_proportion(data, sample_size):
height = 0
sample = sampleData(data, sample_size)
sample_values = len(sample)
x = proportion(data)
z = subtraction(1, x)
y = subtraction(sample_values, 1)
for height_el in sample:
height = multiplication(x, z)
return division(height, y)
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 var_pop_proportion(data):
p = proportion(data)
q = subtraction(1, p)
data = [num for elem in data for num in elem]
new_data = [float(x) for x in data]
n = len(new_data)
return division(n, multiplication(p, q))
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 z_score(data):
data = [num for elem in data for num in elem]
new_data = [float(x) for x in data]
x = new_data[1]
u = population_mean(new_data)
sample_sd = sample_st_dev(new_data)
y = subtraction(x, u)
return division(sample_sd, y)
def population_variance(data):
u = population_mean(data)
deviations = subtraction(data, u)
sq_deviations = square(deviations)
x = len(data)
y = sum(sq_deviations)
d = division(x, y)
return d
def population_variance(data):
data = [num for elem in data for num in elem]
new_data = [float(x) for x in data]
u = population_mean(new_data)
deviations = subtraction(new_data, u)
sq_deviations = square(deviations)
x = len(new_data)
y = sum(sq_deviations)
d = division(x, y)
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 var_sample_proportion(data):
sample_data = data[0:999]
samp_prop_data = []
for x in sample_data:
if x > 64:
samp_prop_data.append(x)
samp_len = len(samp_prop_data)
samp_len_data = len(sample_data)
p = round(samp_len / samp_len_data, 6)
q = subtraction(1, p)
return round(multiplication(p, q) / (samp_len_data - 1), 6)
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 z_score(data):
x = 64
u = population_mean(data)
sample_sd = sample_st_dev(data)
y = subtraction(x, u)
return division(sample_sd, y)
def var_pop_proportion(data):
p = proportion(data)
q = subtraction(p, 1)
n = len(data)
return division(multiplication(p, q), n)
def subtract(self, a, b):
self.result = subtraction(float(a), float(b))
return self.result