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 vsp(data): random_data = random.randint(1, len(data)) new_data = Getsample(data, random_data) prop = proportion(new_data) result1 = multiplication(prop, subtraction(prop, 1)) y = subtraction(len(new_data), 1) x = division(result1, y) return x
def var_sample_prop(numbers, size): result = 0 data = dataList(numbers, size) num_value = len(data) result1 = proportion(numbers) result2 = subtraction(1, result1) result3 = subtraction(num_value, 1) for data2 in data: result4 = multiplication(result1, result2) return division(result4, result3)
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 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 zscore(data): x=64 u=mean(data) sample_sd=samplestand(data) y=subtraction(x,u) return division(sample_sd,y)
def zscore(numbers): row_value = 151 sd = psd(numbers) m = mean(numbers) result = subtraction(row_value, m) z_score = division(result, sd) print(z_score) return z_score
def pop_corr_coeff(numbers): num_value = len(numbers) # Calculation of covariance result1 = subtraction(numbers, sample_mean) result2 = subtraction(numbers, sample_mean) result3 = multiplication(result1, result2) covariance = division(num_value, sum(result3)) # denominator data1 = CsvReader('Tests/Data/pop_corr_data1').numbers data2 = CsvReader('Tests/Data/pop_corr_data2').numbers result4 = psd(data1) result5 = psd(data2) result6 = multiplication(result4, result5) population_corr_coeff = division(result6, covariance) return population_corr_coeff
def conf_interval(data): x = mean(data) dev = psd(data) z = 1.96 # for 95% confidence standard_error = division(dev, squareroot(len(data))) conf_upper_level = round(addition(x, multiplication(z, standard_error)), 2) conf_lower_level = round(subtraction(multiplication(z, standard_error), x), 2) return conf_upper_level, conf_lower_level
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 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 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 confidence_interval(numbers): num_value = len(numbers) result = popstand(numbers) result2 = squareroot(num_value) sample_error = division(result2, result) margin_error = multiplication( 1.96, sample_error) # 1.96=z_value for 95% confidence interval result4 = addition(result, margin_error) result5 = subtraction(margin_error, result) return result4, result5
def median(data): try: total_values = len(data) list_of_nums = [data[i] for i in range(total_values)] list_of_nums.sort() if total_values % 2 == 0: median1 = list_of_nums[int(total_values // 2)] median2 = list_of_nums[int(subtraction((total_values // 2), 1))] result = division(addition(median1, median2), 2) else: result = list_of_nums[int(division(total_values, 2))] return result except ValueError: print("ERROR: That is an emtpy array! Try again.")
def z_score(data): try: zscore_list = [] for x in data: zmean = mean(data) standard_dev = standard_deviation(data) diff = subtraction(x, zmean) z = division(diff, standard_dev) roundedz = round(z, 5) zscore_list.append(roundedz) return zscore_list except ValueError: print("ERROR: That is an emtpy array! Try again.")
def subtract(self, a, b): self.result = subtraction(a, b) return self.result
def var_pop_prop(data): prob_poss = proportion(data) prob_imposs = subtraction(prob_poss, 1) result = multiplication(prob_imposs, prob_poss) vpp = division(result, len(data)) return vpp