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)