def unknown_pop_stand_deviation(data, confidenceZscore, marginOfError,
percentSample):
try:
z = confidenceZscore
if isinstance(marginOfError, float):
moe = marginOfError
else:
moe = division(marginOfError, 100)
if isinstance(percentSample, float):
percent = percentSample
else:
percent = division(percentSample, 100)
e = division(moe, 2)
p = subtraction(1, percent)
sample_muliply = multiplication(p, percent)
z_by_e = division(z, e)
squared = square(z_by_e)
result = multiplication(sample_muliply, squared)
return result
except ValueError:
print("ERROR: That is an emtpy array! Try again.")
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 vsp(data):
random_data = random.randint(1, len(data))
new_data = Getsample(data, random_data)
prop = proportion(new_data)
result1 = multiplication(prop, subtraction(prop, 1))
y = subtraction(len(new_data), 1)
x = division(result1, y)
return x
def pop_corr_coeff(numbers):
num_value = len(numbers)
# Calculation of covariance
result1 = subtraction(numbers, sample_mean)
result2 = subtraction(numbers, sample_mean)
result3 = multiplication(result1, result2)
covariance = division(num_value, sum(result3))
# denominator
data1 = CsvReader('Tests/Data/pop_corr_data1').numbers
data2 = CsvReader('Tests/Data/pop_corr_data2').numbers
result4 = psd(data1)
result5 = psd(data2)
result6 = multiplication(result4, result5)
population_corr_coeff = division(result6, covariance)
return population_corr_coeff
def cochran_sample_size(data, confidenceLevel, confidencelevelZscore,
testVaribility):
try:
numvalues = len(data)
precision = subtraction(1.00, confidenceLevel)
z = confidencelevelZscore
p = testVaribility
recommendation = division(
multiplication(square(z), multiplication(p, p)), square(precision))
cochran = division(
recommendation,
addition(1, (division(subtraction(recommendation, 1), numvalues))))
return round(cochran, 2)
except ValueError:
print("ERROR: That is an emtpy array! Try again.")
def var_sample_prop(numbers, size):
result = 0
data = dataList(numbers, size)
num_value = len(data)
result1 = proportion(numbers)
result2 = subtraction(1, result1)
result3 = subtraction(num_value, 1)
for data2 in data:
result4 = multiplication(result1, result2)
return division(result4, result3)
def confidence_interval(numbers):
num_value = len(numbers)
result = popstand(numbers)
result2 = squareroot(num_value)
sample_error = division(result2, result)
margin_error = multiplication(
1.96, sample_error) # 1.96=z_value for 95% confidence interval
result4 = addition(result, margin_error)
result5 = subtraction(margin_error, result)
return result4, result5
def margin_of_error (data, confidence_Zscore):
try:
size = len(data)
std = standard_deviation(data)
z = confidence_Zscore
moe = multiplication(z, division(std, squareRoot(size)))
return round(moe, 5)
except ValueError:
print("ERROR: That is an emtpy array! Try again.")
def multiply(self, a, b):
self.result = multiplication(a, b)
return self.result
def var_pop_prop(data):
prob_poss = proportion(data)
prob_imposs = subtraction(prob_poss, 1)
result = multiplication(prob_imposs, prob_poss)
vpp = division(result, len(data))
return vpp
