def skewness(data):
try:
List1 = []
List2 = []
List3 = []
List4 = []
x = 0
nStddev = stddev(data)
# pprint(nStddev)
nMean = mean(data)
nCount = len(data)
for n in data:
List1.append(subtraction(nMean, n))
# pprint(List1)
for n2 in List1:
List2.append(division(nStddev, n2))
# pprint(List2)
for n3 in List2:
List3.append(n3**3)
# pprint(List3)
for n4 in List3:
x = x + n4
# pprint(x)
# pprint(nCount)
nskewness = division(nCount, x)
# pprint(float(nskewness))
return nskewness
except ZeroDivisionError:
print("Error - Cannot divide by 0")
except ValueError:
print("Error - Invalid data inputs")
def zscore(a):
zmean = mean(a)
sd = stddev(a)
zlist = []
for x in a:
z = round(((x - zmean) / sd), 6)
zlist.append(z)
return zlist
def z_score(num):
z_mean = populationmean(num)
sd = stddev(num)
zlist = []
for x in num:
z = round(division(subtraction(x, z_mean), sd), 6)
zlist.append(z)
return zlist
def zscore(nums):
mn = mean(nums)
sd = stddev(nums)
data = []
for x in nums:
z = round((x - float(mn) / float(sd)), 6)
data.append(z)
return data
def zscore(num):
z_mean = populationmean(num)
sd = stddev(num)
z_list = []
for x in num:
z = round(((x - z_mean) / sd), 6)
z_list.append(z)
return z_list
def confidence_intervalUpper(sample,z,N):
#sample = simple_rand_sampling(data, N)
#z = float(z)
sample_mean = float(mean(sample))
stddeviation = float(stddev(sample))
rootN = (N**.5)
return round(sample_mean + (z*(stddeviation/rootN)), 5)
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 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 confidence_interval_bottom(num):
try:
num_values = len(num)
z = 1.96
sd = stddev(num)
avg = populationmean(num)
return round(avg - (z * sd / root(num_values)), 5)
except ZeroDivisionError:
print("Can't Divide by 0 Error")
except ValueError:
print("Please Check your 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 / squarerooting(num_values)), 5)
except ZeroDivisionError:
print("Error: Can't Divide by 0")
except ValueError:
print("Error: Check your data inputs")
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 z_score(num):
try:
z_mean = mean(num)
sd = stddev(num)
z_list = []
z_list1 = []
for x in num:
z = round(((float(x) - float(z_mean)) / float(sd)), 6)
z_list.append(z)
nFinal = z_list[0]
return nFinal
except ZeroDivisionError:
print("Error - Cannot divide by 0")
except ValueError:
print("Error - Invalid data inputs")
def skewness(num):
mean_num = mean(num)
median_num = median(num)
stddev_num = stddev(num)
skew = ((mean_num - median_num) * 3) / stddev_num
return skew
def stddev(self, nums):
self.data = stddev(nums)
return self.data
def stddev(self, data):
self.result = stddev(data)
return self.result
def confidence_low(num):
values = len(num)
z = 1.96
stdev1 = stddev(num)
avg = populationmean(num)
return (avg - (z * stdev1)) / (squarerooting(values))
def margin_of_error(sample, z, N):
stddeviation = float(stddev(sample))
rootN = (N**.5)
return round(z * (stddeviation / rootN), 5)
