def confidence_interval(numbers):
m = mean(numbers)
confidence_level = 0.95
z = (1-confidence_level) / 2
sd = standard_deviation(numbers)
n = squareroot(len(numbers))
return [subtraction(multiplication(division(n, sd), z), m), addition(multiplication(division(n, sd), z), m)]
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 standard_deviation(numbers): # complete
n = len(numbers)
c = 0
t = 0
for i in range(0, n, 1):
c = subtraction(mean(numbers), numbers[i])
t = addition(square(c), t)
x = division((n - 1), t)
return squareroot(x)
def confidence_interval(data):
z_value = 1.05
mean =sample_mean(data)
sd = pop_standard_dev(data)
x = len(data)
y = division(squareroot(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 ssd(data):
total = 0
sample = random.randint(1, len(data))
new_sample = Getsample(data, sample)
new_mean = mean(new_sample)
for numb in new_sample:
result = subtraction(numb, new_mean)
sq = square(result)
total = addition(total, sq)
n = len(new_sample)
d = division(subtraction(1, n), total)
samp_sd = squareroot(d)
return samp_sd
def sample_st_deviation(data, sample_size):
dev = 0
sample = getSample(data, sample_size)
sample_values = len(sample)
x_bar = sample_mean()
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 squareroot(divide)
def squareroot(self, a):
self.result = squareroot(a)
return self.result
def population_SD(data):
SDvalue = squareroot(variance(data))
return SDvalue
def pop_standard_dev(data):
n = len(data)
u = population_mean(data)
return squareroot(
sum([(element - u)**2 for element in data]) / (len(data) - 1))
def psd(numbers):
return squareroot(variance(numbers))