def sampleCorrelation(dataX, dataY):
#dataX= []
#dataY = []
meanX = mean(dataX)
meanY = mean(dataY)
deviationX = standard_deviation(dataX)
deviationY = standard_deviation(dataY)
rNumerator = 0.0
for i in range(len(dataX)):
rNumerator += product(subtraction(dataX[i], meanX),
subtraction(dataY[i], meanY))
rDenominator = product(deviationX, deviationY)
r = division(rNumerator, rDenominator)
return r
def zScore(data):
x = random.choice(data)
meanData = mean(data)
standardDeviation = standard_deviation(data)
numerator = subtraction(x, meanData)
z = division(numerator, standardDeviation)
return z
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 z_score(data):
try:
for i in range(len(data)):
h = data[i] - mean(data)
g = h / standard_deviation(data)
return g
except ZeroDivisionError:
print("ERROR: Can't divide by zero")
except ValueError:
print("ERROR: Check your input value")
def zscore(numbers): # complete
u = mean(numbers)
sig = standard_deviation(numbers)
n = len(numbers)
zsc = []
for i in numbers:
z = 0
z = round(division(sig, subtraction(u, i)), 3)
# z = float((numbers[i] - u) / sig)
zsc.append(z)
return zsc
def confidence_interval(data):
try:
num_values = len(data)
z = 1.96 # random z value
stnd_dev = standard_deviation(data)
mean_result = mean(data)
return round(mean_result + (z * stnd_dev / math.sqrt(num_values)), 5)
except ZeroDivisionError:
print("ERROR: Can't divide by zero")
except ValueError:
print("ERROR: Check your input value type")
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 population_standard_deviation(self, a):
self.result = standard_deviation(a)
return self.result
def confidenceInterval(data):
mean_data = mean(data)
standardDeviation = standard_deviation(data)
return mean_data, "=-", standardDeviation
def zscore(data, x):
# Validations
empty_list_check(data)
check_for_valid_numbers(data)
return division(standard_deviation(data), subtraction(mean(data), x))
def standard_deviation(self):
self.result = standard_deviation(self.data)
return self.result
def variance(numbers):
return square(standard_deviation(numbers))
def z_score(test_score, data):
x = subtraction(mean(data), test_score)
return division(standard_deviation(data), x)
def stats_standard_deviation(self, data):
self.result = standard_deviation(data)
return self.result
def variance(numbers): # complete
return square(standard_deviation(numbers))
