def Systematic(data): List = [] x = len(data) / 5 i = 0 while i < len(data): List.append(i) i = addition(i, x) return List
def median(lst): lst.sort() if len(lst) % 2 == 0: first_median = lst[len(lst) // 2] second_median = lst[len(lst) // 2 - 1] median = division(addition(first_median, second_median), 2) else: median = lst[len(lst) // 2] return median
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 Median(data): n = len(data) num = n // 2 if n % 2 == 0: mid = (addition(data[num], data[num - 1])) / 2 print(data[num]) else: mid = data[num] print(data[mid]) return mid
def confidence_interval(data): z_value = 1.05 mean =sample_mean(data) sd = pop_standard_dev(data) x = len(data) y = division(squareroot(x), sd) margin_of_error = multiplication(z_value, y) a = subtraction(mean, margin_of_error) b = addition(mean, margin_of_error) return a, b
def median(numbers): n = len(numbers) numbers.sort() if n % 2 == 0: first_median = numbers[int(division(2, n))] second_median = numbers[len(numbers) // 2 - 1] med = division(2, addition(first_median, second_median)) else: med = numbers[division(2, n)] return med
def confidence_interval(numbers): m = mean(numbers) confidence_level = 0.95 z = (1 - confidence_level) / 2 sd = standard_deviation(numbers) n = root(len(numbers)) return [ subtraction(multiplication(division(n, sd), z), m), addition(multiplication(division(n, sd), z), m) ]
def psd(numbers): num_values = len(numbers) result = mean(numbers) total = 0 for numb in numbers: result2 = subtraction(numb, result) sq = squaree(result2) total = addition(total, sq) return squar_rot(division(num_values, total))
def mean(data): try: num_values = len(data) total = 0 for num in data: total = addition(total, num) return division(num_values, total) except ZeroDivisionError: print("Error - Cannot divide by 0") except ValueError: print("Error - Invalid data inputs")
def mean(data): # Validations empty_list_check(data) check_for_valid_numbers(data) num_values = len(data) total = 0 for num in data: total = addition(total, num) return division(num_values, total)
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 proportion(num): sum = 0 for n in num: sum = addition(sum, n) result = [] for n in num: value = division(n, sum) result.append(value) return result
def sample_mean(data, sample_size): total = 0 # check that get sample returns the proper number of samples # check that sample size is not 0 # check that sample size is not larger than the population # https://realpython.com/python-exceptions/ # https://stackoverflow.com/questions/129507/how-do-you-test-that-a-python-function-throws-an-exception sample = getSample(data, sample_size) num_values = len(sample) for num in sample: total = addition(total, num) return division(total, num_values)
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 population_correlation_coefficient(list_x, list_y): total = 0 x = standard_deviation(list_x) y = standard_deviation(list_y) for i in range(len(list_x)): diff_x = subtraction(list_x[i], mean(list_x)) diff_y = subtraction(list_y[i], mean(list_y)) total = total + multiplication(division(diff_x, x), division( diff_y, y)) return round( float( multiplication(division(1, addition(len(list_x), len(list_y))), total)), 4)
def quartiles(data): try: List2 = [] num_values = len(data) nNum = float(num_values) Q2 = median(data) nSort = sorted(data) a = .25 * nNum b = .75 * nNum for num in nSort: List2.append(num) Q1 = List2[int(a)] Q3 = List2[int(b)] bb = addition(addition(int(Q1), int(Q2)), int(Q3)) return int(Q1), int(Q2), int(Q3) except ZeroDivisionError: print("Error - Cannot divide by 0") except ValueError: print("Error - Invalid data inputs")
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 get_median(data): num_values = len(data) if num_values % 2 == 0: value = int(division(2, num_values)) a = data[value] value = value - 1 b = data[value] c = addition(b, a) d = division(2, c) return d else: value = int(division(2, num_values)) e = data[value] return e
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 = squaree(result) total = addition(total, sq) n = len(new_sample) d = division(subtraction(1, n), total) samp_sd = squar_rot(d) # actual_sd = statistics.stdev(new_sample) return samp_sd
def mode(num): counter = {} for n in num: if n in counter: counter[n] = addition(counter[n],1) else: counter[n] = 1 result = None maxCount = 0 for k in counter.keys(): if counter[k] > maxCount: maxCount = counter[k] result = k return float(result)
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 median(num): num1 = sorted(num) values = len(num1) num1.sort() if values % 2 == 0: median1 = num1[values // 2] median2 = num1[int(subtraction((values // 2), 1))] median = division(addition(median1, median2), 2) else: median = num1[values // 2] return median
def sample(data, sample_size): total = 0 # check that get sample returns the proper number of samples # check that sample size is not 0 # check that sample size is not larger than the population if sample_size != 0 raise Exception('sample_size cannot be 0') if sample_size > data raise NotLargerThanDataException('sample_size cannot be bigger than population') random_values = getRandomNum(data, sample_size) num_values = len(random_values) for num in random_values: total = addition(total, num) return division(total, num_values)
def median(num): try: num_values = len(num) list_num = [num[i] for i in range(num_values)] list_num.sort() if num_values % 2 == 0: median1 = list_num[int(num_values // 2)] median2 = list_num[int(subtract((num_values // 2), 1))] median_result = division(addition(median1, median2), 2) else: median_result = list_num[int(division(num_values, 2))] return median_result except ZeroDivisionError: print("Divide by 0 Error") except ValueError: print("Please Check your data inputs")
def median(a): try: n = len(a) list_num = [a[i] for i in range(n)] list_num.sort() if n % 2 == 0: median1 = list_num[int(n // 2)] median2 = list_num[int(subtraction((n // 2), 1))] median_result = division(addition(median1, median2), 2) else: median_result = list_num[int(division(n, 2))] return median_result except ZeroDivisionError: print("Error: Can't Divide by 0") except ValueError: print("Error: Check your data inputs")
def median(num): try: num_values = len(num) list_num = [num[i] for i in range(num_values)] list_num.sort() if num_values % 2 == 0: median1 = list_num[int(num_values // 2)] median2 = list_num[int(subtraction((num_values // 2), 1))] median_result = division(addition(median1, median2), 2) else: median_result = list_num[int(division(num_values, 2))] return median_result except ZeroDivisionError: print("Error: Enter numbers greater than 0") except ValueError: print("Error: insert correct data type ")
def median(num): try: num_values = len(num) list_num = [num[i] for i in range(num_values)] list_num.sort() if num_values % 2 == 0: median1 = list_num[int(num_values / 2) - 1] median2 = list_num[int(subtraction((num_values // 2), 1))] median_result = division(2, addition(median1, median2)) else: median_result = list_num[math.ceil(division(2, num_values)) - 1] return float(median_result) except ZeroDivisionError: print("Error - Cannot divide by 0") except ValueError: print("Error - Invalid data inputs")
def confidence_interval(data): z_value = 1.960 mean = population_mean(data) sd = pop_stand_dev(data) x = len(data) y = division(sq_rt(x), sd) margin_of_error = multiplication(z_value, y) a = [subtraction(mean, margin_of_error)] b = [addition(mean, margin_of_error)] size = len(a) lower = a[0] upper = b[0] return lower, upper