def Cochrans(set): set = list((1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) p = 0.5 q = 1 - p me = marginerror(set) samplesize = (square(1.96) * p * q) / (square(me)) return samplesize
def ConfInt(set): set = list((1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ci_mean = mean(set) s_dev = sd(set) ci_list = [] ci1 = round((ci_mean + 1.96*(s_dev / square(len(set)))), 4) ci2 = round((ci_mean - 1.96*(s_dev / square(len(set)))), 4) ci_list.append(ci1) ci_list.append(ci2) return ci_list
def cochran(sample, confidence_level): # Validations check_for_valid_numbers(sample) empty_list_check(sample) # Formula: (Z^2)(p)(q) / (e^2) -> https://www.statisticshowto.com/probability-and-statistics/find-sample-size/ # Assuming p is 0.5 # calculate z from a given confidence interval z = CalculateZValue.calculate_zvalue(confidence_level) margin_of_error_result = square(margin_of_error(sample, confidence_level)) return division(margin_of_error_result, multiplication(multiplication(square(z), 0.5), 0.5))
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 ssk(set): set = list((1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) s_dev = sd(set) width = 0.6 me = width / 2 samplesize = square((1.96 * s_dev) / me) return samplesize
def StdDevSample(data): Sum1 = 0 for i in data: x = abs(i - mean(data)) Sum1 = square(x) + Sum1 n = len(data) stdDev = math.sqrt(Sum1 / (n - 1)) return stdDev
def StdDevPop(data): Sum2 = 0 for i in data: x = abs(i - mean(data)) Sum2 = square(x) + Sum2 n = len(data) stdDev = math.sqrt(Sum2) / n return stdDev
def ssu(set): set = list((1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) p = 0.5 q = 1 - p width = 0.6 me = width / 2 samplesize = square(1.96 / me) * p * q return samplesize
def st_dev(lst): diffs = 0 m = mean(lst) for l in lst: diffs = addition(diffs, square(subtraction(l, m))) sd = division(diffs, subtraction(1, len(lst))) x = root(sd) return x
def cochranSample(data): p = 0.5 q = 1 - p p_q = product(p, q) List1 = [] List2 = [] for i in marginErr(data): List1.append(square(i)) for i in zScores(zValues(data)): List2.append(square(i)) i = 0 n = [] while i < len(List1): n.append(round(product(List1[i], p_q) / List2[i])) i += 1 return n
def sample_size_ci(z, p, w): var_1 = divide(z, 2) z_score = get_z_score(var_1) e = divide(w, 2) q = subtract(1, p) var_2 = multiply(p, q) var_3 = divide(float(z_score), e) var_5 = square(var_3) return multiply(var_2, var_5)
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 standard_deviation(numbers): # complete n = len(numbers) c = 0 t = 0 for i in range(0, n, 1): c = subtraction(mean(numbers), numbers[i]) t = addition(square(c), t) x = division((n - 1), t) return root(x)
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 popstand(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 squar_rot(division(num_values, total))
def sample_size_unknown_pop(confidence_level, width): p = 0.5 q = 1 - p return round( multiplication( square( division(division(2, width), calculate_zvalue(confidence_level))), multiplication(p, q)), 2)
def samplevariance(num): try: pop_mean = populationmean(num) num_values = len(num) x = 0 for i in num: x = x + square(i - pop_mean) return round(division(x, num_values), 7) except ZeroDivisionError: print("Error: Enter numbers greater than 0") except ValueError: print("Error: insert correct data type")
def variance(data): # Validations empty_list_check(data) check_for_valid_numbers(data) n = len(data) calculated_mean = mean(data) result = 0 for x in data: result = addition(square(subtraction(calculated_mean, x)), result) return division(n, result)
def variance(num): try: pop_mean = populationmean(num) num_values = len(num) x = 0 for i in num: x = x + square(i - pop_mean) return division(x, num_values) except ZeroDivisionError: print("Error: Can't Divide by 0") except ValueError: print("Error: Check your data inputs")
def samp_st_dev(numbers): ss = random.randint(1, len(numbers)) new_values = getSample(numbers, ss) c = 0 t = 0 n = len(new_values) for i in range(0, n, 1): c = subtraction(new_values[i], mean(new_values)) t = addition(square(c), t) x = division(subtraction(1, n), t) actual_sd = statistics.stdev(new_values) # Calculated using stat library to compare return root(x), actual_sd
def variance(data): if is_valid(data): size = len(data) x = mean(data) tmp = [] for i in data: diff = subtract(i, x) tmp.append(square(diff)) total = add_list(tmp) return divide(total, size) else: raise TypeError("Data contains non-numeric values")
def sample_std_dev(data): total = 0 samples = random.randint(1, len(data)) new_samples = get_sample(data, samples) new_mean = population_mean(new_samples) for number in new_samples: result = subtraction(number, new_mean) sq = square(result) total = addition(total, sq) n = len(new_samples) d = division(subtraction(1, n), total) sample_sd = sq_rt(d) return sample_sd
def sample_st_deviation(data, sample_size): dev = 0 sample = getSample(data, sample_size) sample_values = len(sample) x_bar = sample_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) divide = division(add, n) return squareroot(divide)
def variance(num): try: pop_mean = populationmean(num) num_values = len(num) x = 0 for i in num: # x = x + square(i-pop_mean) x = addition(x, square(subtraction(i, pop_mean))) return division(x, num_values) except ZeroDivisionError: print("Error: Enter number greater than 0") except ValueError: print("Error: Enter correct data type")
def ssd(data): total = 0 sample = random.randint(1, len(data)) new_sample = Getsample(data, sample) new_mean = mean(new_sample) for numb in new_sample: result = subtraction(numb, new_mean) sq = square(result) total = addition(total, sq) n = len(new_sample) d = division(subtraction(1, n), total) samp_sd = squareroot(d) return samp_sd
def get_variance(data): x1 = get_mean(data) num_values = len(data) total = 0 total1 = 0 data1 = [] for i in range(0, len(data)): a = data[i - 1] total_sum = subtraction(a, x1) total = square(total_sum) data1.append(total) for i in range(0, len(data1)): total1 = total1 + addition(0, data1[i]) return round(division(num_values - 1, total1), 1)
def variance(num): Mean_var = mean(num) Man = [] for n in num: Variables = subtraction(n, Mean_var) Man.append(Variables) Squares = [] for Variables in Man: Square_var = square(Variables) Squares.append(Square_var) return mean(Squares)
def sample_yes_std(data): List1 = [] List2 = [] e = marginErr(data) k = c_i(data) for i in k: Z = i[1] / 2 List1.append(scipy.stats.norm.cdf(Z)) i = 0 while i < len(List1): x = product(List1[i], StdDevSample(data)) y = round(division(x, e[i])) List2.append((square(y))) i += 1 return List2
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], StdDevSample(data)) y = round(division(x, E[i])) List1.append((square(y))) i += 1 return List1
def variance(data): try: # 1. find the mean of the data calculatedMean = mean(data) # pprint(calculatedMean) distanceArray = [] meanDeviationValue = 0 for item in data: distanceArray.append(abs(subtraction(item, calculatedMean))) num_values = len(data) x = 0 for i in distanceArray: x = x + square(i) return int(division(num_values, x)) except ZeroDivisionError: print("Error - Cannot divide by 0") except ValueError: print("Error - Invalid data inputs")