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 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 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 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 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 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 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 margin_of_error (data, confidence_Zscore): try: size = len(data) std = standard_deviation(data) z = confidence_Zscore moe = multiplication(z, division(std, squareRoot(size))) return round(moe, 5) except ValueError: print("ERROR: That is an emtpy array! Try again.")
def multiply(self, a, b): self.result = multiplication(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