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 sample_size_unknown_pop(confidence_level, width):
p = 0.5
q = 1 - p
return round(
multiplication(
square(
division(division(2, width),
calculate_zvalue(confidence_level))),
multiplication(p, q)), 2)
def cochran(p, q, z, e):
try:
n1 = multiplication(p, p)
n2 = z * z
n3 = multiplication(n1, n2)
n4 = e * e
nCochran = division(n4, n3)
return int(nCochran)
except ZeroDivisionError:
print("Error: Can't Divide by 0")
except ValueError:
print("Error: Check your data inputs")
def population_correlation_coefficient(list_x, list_y):
total = 0
x = standard_deviation(list_x)
y = standard_deviation(list_y)
for i in range(len(list_x)):
diff_x = subtraction(list_x[i], mean(list_x))
diff_y = subtraction(list_y[i], mean(list_y))
total = total + multiplication(division(diff_x, x), division(
diff_y, y))
return round(
float(
multiplication(division(1, addition(len(list_x), len(list_y))),
total)), 4)
def cochran(sample, confidence_level):
# Validations
check_for_valid_numbers(sample)
empty_list_check(sample)
# Formula: (Z^2)(p)(q) / (e^2) -> https://www.statisticshowto.com/probability-and-statistics/find-sample-size/
# Assuming p is 0.5
# calculate z from a given confidence interval
z = CalculateZValue.calculate_zvalue(confidence_level)
margin_of_error_result = square(margin_of_error(sample, confidence_level))
return division(margin_of_error_result,
multiplication(multiplication(square(z), 0.5), 0.5))
def variance_of_population_proportion(numbers):
n = len(numbers)
prop = proportion(numbers)
prop_2 = subtraction(prop, 1)
x = multiplication(prop, prop_2)
variance_of_pp = division(x, n)
return variance_of_pp
def Pop_correlation_coefficient():
lst = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
x_data = [1, 25, 34, 4, 51]
y_data = [6, 7, 8, 9, 10]
x_mean = mean(x_data)
y_mean = mean(y_data)
a = []
b = []
ab = []
x = st_dev(x_data)
y = st_dev(y_data)
divisor = multiplication(x, y)
z = len(lst)
for i in x_data:
new1 = subtraction(x_mean, i)
zx = division(new1, x)
a.append(zx)
# (zx)i = (xi – x̄) / s x
for i in y_data:
new2 = subtraction(y_mean, i)
zy = division(new2, y)
b.append(zy)
ab = [a[i] * b[i] for i in range(len(x_data))]
tot_sum = sum(ab)
result = tot_sum / 4
return result
def Pop_correlation_coefficient(x_data, y_data):
x_mean = mean(x_data)
y_mean = mean(y_data)
a = []
b = []
tot_sum = 0
x = st_dev(x_data)
y = st_dev(y_data)
for i in x_data:
new1 = subtraction(x_mean, i)
zx = division(new1, x)
a.append(zx)
for i in y_data:
new2 = subtraction(y_mean, i)
zy = division(new2, y)
b.append(zy)
for i in range(len(x_data)):
ab = multiplication(a[i], b[i])
tot_sum = addition(tot_sum, ab)
cal_result = division(tot_sum, subtraction(1, len(x_data)))
return cal_result
def variance_samp_prop(numbers):
ran = random.randint(1, len(numbers))
values = getSample(numbers, ran)
p = proportion(values)
m = multiplication(p, subtraction(p, 1))
x = subtraction(len(values), 1)
h = division(x, m)
return h
def v_samp_proportion(lst):
ss = random.randint(1, len(lst))
new_values = getSample(lst, ss)
p = proportion(new_values)
c = multiplication(p, subtraction(p, 1))
y = subtraction(len(new_values), 1)
x = division(c, y)
return x
def confidenceinterval(lst, conf):
x = mean(lst)
std = standard_deviation(lst)
if conf == 95:
t = 1.96
elif conf == 90:
t = 1.64
elif conf == 99:
t = 2.58
else: # 95 default confidence percentage
t = 1.96
std_error = division(std, root(len(lst)))
conf_upper = addition(x, multiplication(t, std_error))
conf_upper = round(conf_upper, 2)
conf_lower = subtraction(multiplication(t, std_error), x)
conf_lower = round(conf_lower, 2)
return conf_upper, conf_lower
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 var_sample_proportion(data):
sample_data = data[0:999]
sample_prop_data = []
for x in sample_data:
if x > 64:
sample_prop_data.append(x)
sample_len = len(sample_prop_data)
sample_len_data = len(sample_data)
p = round(sample_len / sample_len_data, 6)
q = subtraction(1, p)
return round(multiplication(p, q) / (sample_len_data - 1), 6)
def cimarginerror(n, x, s):
try:
zValue = 1.96
n1 = square_root(n)
n2 = division(n1, s)
n3 = multiplication(n2, zValue)
return round(float(n3), 2)
except ZeroDivisionError:
print("Error: Can't Divide by 0")
except ValueError:
print("Error: Check your data inputs")
def margin_of_error(sample, confidence_level):
# Validations
empty_list_check(sample)
check_for_valid_numbers(sample)
# Formula - z * (o / sqrt(n)); o is our standard deviation
# Reference - https://www.surveymonkey.com/mp/margin-of-error-calculator/
z = CalculateZValue.calculate_zvalue(confidence_level)
sample_size = len(sample)
standard_deviation_result = standard_deviation(sample)
return multiplication(
z, division(square_root(sample_size), standard_deviation_result))
def correlation(data1, data2, seed):
# population sample with seed
pop1 = ItemReturnType.aList_seed(data1, 5, seed)
pop2 = ItemReturnType.aList_seed(data2, 5, seed)
cv = Covariance.covariance(pop1, pop2)
stdDev1 = StandardDeviation.standard_deviance(pop1)
stdDev2 = StandardDeviation.standard_deviance(pop2)
sc = cv / (multiplication(stdDev1, stdDev2))
return sc
def confidence_intervals(data):
try:
zvalue = 1.960
nLenght = len(data)
nMean = mean(data)
sd = stddev(data)
pprint(sd)
CI = multiplication(zvalue, (division(square_root(nLenght), sd)))
x = round(float(CI), 1)
pprint(str(str(nMean) + "+" + str(x)))
return str(str(nMean) + "+" + str(x))
except ZeroDivisionError:
print("Error: Can't Divide by 0")
except ValueError:
print("Error: Check your data inputs")
def confidence_interval(data):
z_value = 1.960
mean = population_mean(data)
sd = pop_stand_dev(data)
x = len(data)
y = division(sq_rt(x), sd)
margin_of_error = multiplication(z_value, y)
a = [subtraction(mean, margin_of_error)]
b = [addition(mean, margin_of_error)]
size = len(a)
lower = a[0]
upper = b[0]
return lower, upper
def sample_correlation(data, data1):
try:
mean1 = mean(data)
mean2 = mean(data1)
List1 = []
List2 = []
for num in data:
a = subtraction(int(round(mean1, 0)), num)
List1.append(a)
for num in data1:
b = subtraction(mean2, num)
List2.append(b)
c = np.multiply(List1, List2)
cc = 0
for num in c:
cc = cc + num
d = 0
e = 0
# pprint(List1)
# pprint(List2)
for num in List1:
d = d + square(num)
for num in List2:
e = e + square(num)
f = multiplication(int(d), e)
g = square_root(int(f))
h = division(int(g), cc)
# pprint(float(cc))
# pprint(e)
# pprint(f)
# pprint(float(g))
# pprint(str(round(h,9)))
nCorrelation = round(h, 9)
# pprint(nCorrelation)
return nCorrelation
except ZeroDivisionError:
print("Error - Cannot divide by 0")
except ValueError:
print("Error - Invalid data inputs")
def variance_pop_proportion(data):
p = proportion(data)
s = subtraction(p, 1)
n = len(data)
return division(multiplication(p, s), n)
def multiply(self, a, b):
self.result = multiplication(a, b)
return self.result
def multiply(self, a, b):
##Multiply Function##
self.result = multiplication(a, b)
return self.result
def test_multiplication(self):
self.assertEqual(6, multiplication(2,3))
def margin(data, seed):
zs = Z_Score.zscore(data, seed)
sd = StandardDeviation.standard_deviance(data)
margin = multiplication(zs, sd)
return margin
def v_pop_proportion(lst):
prob_will = proportion(lst)
prob_wont = subtraction(prob_will, 1)
result = multiplication(prob_wont, prob_will)
vpp = division(result, len(lst))
return vpp
def var_pop_proportion(data):
p = proportion(data)
q = subtraction(1, p)
n = len(data)
return division(n, multiplication(p, q))
def confidence_interval_SUB(confidence_interval_SUB_list):
return subtraction(
population_mean(confidence_interval_SUB_list),
(multiplication(zscore(confidence_interval_SUB_list)),
division(population_standard_deviance(confidence_interval_SUB_list),
square_root(num_values))))
def multiply(self, num1, num2):
self.result = multiplication(num1, num2)
return self.result
def variance_population_proportion(variance_population_proportion_list):
return square_root(division(multiplication(subtraction(population_mean(variance_population_proportion_list), 1)), population_mean(variance_population_proportion_list), num_values))
