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 Sample_Correlation(list1, list2): n = len(list1) avg_x = average(list1) avg_y = average(list2) rod = 0 x2 = 0 y2 = 0 for i in range(n): x = subtraction(list1[i], avg_x) y = subtraction(list2[i], avg_y) rod += product(x, y) x2 += square(x) y2 += square(y) return rod / squareRoot(x2 * y2)
def samplestand(data, sample_size): dev = 0 sample = sampledata(data, sample_size) sample_values = len(sample) x_bar = mean() x = sample_values n = subtraction(sample_values, 1) for dev in sample: dev = subtraction(x, x_bar) square_x_bar = square(dev) add = addition(square_x_bar, square_x_bar) result = division(add, n) return squareroot(result)
def StandardDeviationPopulation(data): Sum1 = 0 for i in data: x = abs(i - mean(data)) Sum1 = square(x) + Sum1 n = len(data) stand_dev = math.sqrt(Sum1) / n return stand_dev
def StandardDeviationSample(data): Sum = 0 for i in data: x = abs(i - mean(data)) Sum = square(x) + Sum n = len(data) stand_dev = math.sqrt(Sum / (n - 1)) return stand_dev
def popuvar(numbers): num_value = len(numbers) total = 0 for numb in numbers: result = subtraction(numb, mean(numb)) result1 = square(result) result2 = division(num_value, result1) return result2
def population_variance(a, b, c, d, e, f): try: popstd = population_standard_deviation(a, b, c, d, e, f) answer = float(square(popstd)) return answer except ZeroDivisionError: print("Cannot divide by zero") except ValueError: print("Numbers are not valid")
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 CochranSampleSize(data): p = 0.5 q = 1 - p PQ = product(p, q) List = [] List1 = [] for i in MarginError(data): List.append(square(i)) for i in Z_scores(z_values(data)): List1.append(square(i)) i = 0 n = [] while i < len(List): n.append(round(product(List[i], PQ) / List1[i])) i += 1 return n
def vpop(numbers): num_values = len(numbers) result = mean(numbers) total = 0 for numb in numbers: result2 = subtraction(numb, result) sq = square(result2) total = addition(total, sq) return division(num_values, total)
def variance(data): try: mean_of_data = mean(data) total_values = len(data) v = 0 for a in data: v = v + square(a - mean_of_data) result = division(v, total_values) return result except ValueError: print("ERROR: That is an emtpy array! Try again.")
def SampleSize_withStd(data): List = [] List1 = [] E = MarginError(data) K = mean_confidence_interval(data) for i in K: Z = i[1] / 2 List.append(scipy.stats.norm.cdf(Z)) i = 0 while i < len(List): x = product(List[i], StandardDeviationSample(data)) y = round(division(x, E[i])) List1.append((square(y))) i += 1 return List1
def SampleSize_withoutStd(data): E = MarginError(data) # return a list p = 0.5 q = 1 - p PQ = product(q, p) List = [] List1 = [] x = mean_confidence_interval(data) for i in x: Z = i[1] / 2 List.append(scipy.stats.norm.cdf(Z)) ME = [] for i in E: ME.append(i / 2) i = 0 while i < len(ME): ZE = List[i] / ME[i] x = round(square(ZE) * PQ) List1.append(x) i += 1 return List1
def square(self, a): if not Helper.check_number(a): raise ValueError self.result = square(a) return self.result
def square(self, a): self.result = square(a) return self.result