def confidence_interval(num):
x1 = population_mean(num)
c = 0.95
z_value = (1 - c) / 2
d1 = population_standard_deviation(num)
l1 = square_root(len(num))
return [x1 - z_value * d1 / l1, x1 + z_value * d1 / l1]
def sample_stddev(num):
try:
variance_float = variance(num)
return round(square_root(variance_float), 5)
except ZeroDivisionError:
print("Error: Insert a number greater than 0")
except ValueError:
print("Error: Please enter correct data type")
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 confidence_interval_known(num):
try:
num_values = len(num)
z = 1.96
sd = stddev(num)
avg = mean(num)
return round(avg + (z * sd / square_root(num_values)), 5)
except ZeroDivisionError:
print("Error: Can't Divide by 0")
except ValueError:
print("Error: Check your data inputs")
def confidence_interval_bottom(num):
try:
num_values = len(num)
z = 1.96
sd = stddev(num)
avg = populationmean(num)
return round(avg - (z * sd / square_root(num_values)), 5)
except ZeroDivisionError:
print("Error:Insert a number greater than 0")
except ValueError:
print("Error: Enter correct data type ")
def stddev(num):
try:
# 1. Goes into Variance() to get the the mean and the variance
variance_float = variance(num)
# 2. Gets sqrt to get the standard Deviation
x = round(square_root(variance_float), 5)
return int(x)
except ZeroDivisionError:
print("Error - Cannot divide by 0")
except ValueError:
print("Error - Invalid data inputs")
def confidence_interval_top(num):
try:
num_values = len(num)
z = 1.96
sd = stddev(num)
avg = populationmean(num)
return round(avg + (z * sd / square_root(num_values)), 5)
except ZeroDivisionError:
print("Error: Enter a value greater then 0")
except ValueError:
print("Error: insert correct datatype")
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 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 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 get_standard_deviation(data):
value = get_variance(data)
return round(square_root(value), 1)
def standard_deviation(data):
# Validations
empty_list_check(data)
check_for_valid_numbers(data)
return square_root(variance(data))
def square_root(self, a):
self.result = square_root(a)
return self.result
def population_standard_deviation(num):
average = population_mean(num)
s = 0.0
for i in num:
s += (i - average) ** 2
return square_root(float(s) / len(num))