def sample_size_ci(z, p, w):
var_1 = divide(z, 2)
z_score = get_z_score(var_1)
e = divide(w, 2)
q = subtract(1, p)
var_2 = multiply(p, q)
var_3 = divide(float(z_score), e)
var_5 = square(var_3)
return multiply(var_2, var_5)
def conf_inter(x, cl, s, n):
var_1 = n - 1
var_2 = subtract(1, cl)
var_3 = divide(var_2, 2)
var_4 = get_t_score(float(var_1), float(var_3))
var_5 = get_square_root(n)
var_6 = divide(s, var_5)
var_7 = multiply(float(var_4), var_6)
low = subtract(x, var_7)
high = add(x, var_7)
return [low, high]
def variance(data):
if is_valid(data):
size = len(data)
x = mean(data)
tmp = []
for i in data:
diff = subtract(i, x)
tmp.append(square(diff))
total = add_list(tmp)
return divide(total, size)
else:
raise TypeError("Data contains non-numeric values")
def z_score(x, u, s):
var_1 = subtract(x, u)
return divide(var_1, s)
def cochran(z, p, q, e):
var_1 = square(z)
var_2 = multiply(p, q)
var_3 = multiply(var_1, var_2)
var_4 = square(e)
return divide(var_3, var_4)
def mar_err(z, s, n):
var_1 = get_square_root(n)
var_2 = divide(s, var_1)
return multiply(z, var_2)