def confidence_interval(num): x1 = population_mean(num) c = 0.95 z_value = (1 - c) / 2 d1 = population_standard_deviation(num) l1 = square_root(len(num)) return [x1 - z_value * d1 / l1, x1 + z_value * d1 / l1]
def sample_stddev(num): try: variance_float = variance(num) return round(square_root(variance_float), 5) except ZeroDivisionError: print("Error: Insert a number greater than 0") except ValueError: print("Error: Please enter correct data type")
def cimarginerror(n, x, s): try: zValue = 1.96 n1 = square_root(n) n2 = division(n1, s) n3 = multiplication(n2, zValue) return round(float(n3), 2) except ZeroDivisionError: print("Error: Can't Divide by 0") except ValueError: print("Error: Check your data inputs")
def confidence_interval_known(num): try: num_values = len(num) z = 1.96 sd = stddev(num) avg = mean(num) return round(avg + (z * sd / square_root(num_values)), 5) except ZeroDivisionError: print("Error: Can't Divide by 0") except ValueError: print("Error: Check your data inputs")
def confidence_interval_bottom(num): try: num_values = len(num) z = 1.96 sd = stddev(num) avg = populationmean(num) return round(avg - (z * sd / square_root(num_values)), 5) except ZeroDivisionError: print("Error:Insert a number greater than 0") except ValueError: print("Error: Enter correct data type ")
def stddev(num): try: # 1. Goes into Variance() to get the the mean and the variance variance_float = variance(num) # 2. Gets sqrt to get the standard Deviation x = round(square_root(variance_float), 5) return int(x) except ZeroDivisionError: print("Error - Cannot divide by 0") except ValueError: print("Error - Invalid data inputs")
def confidence_interval_top(num): try: num_values = len(num) z = 1.96 sd = stddev(num) avg = populationmean(num) return round(avg + (z * sd / square_root(num_values)), 5) except ZeroDivisionError: print("Error: Enter a value greater then 0") except ValueError: print("Error: insert correct datatype")
def margin_of_error(sample, confidence_level): # Validations empty_list_check(sample) check_for_valid_numbers(sample) # Formula - z * (o / sqrt(n)); o is our standard deviation # Reference - https://www.surveymonkey.com/mp/margin-of-error-calculator/ z = CalculateZValue.calculate_zvalue(confidence_level) sample_size = len(sample) standard_deviation_result = standard_deviation(sample) return multiplication( z, division(square_root(sample_size), standard_deviation_result))
def confidence_intervals(data): try: zvalue = 1.960 nLenght = len(data) nMean = mean(data) sd = stddev(data) pprint(sd) CI = multiplication(zvalue, (division(square_root(nLenght), sd))) x = round(float(CI), 1) pprint(str(str(nMean) + "+" + str(x))) return str(str(nMean) + "+" + str(x)) except ZeroDivisionError: print("Error: Can't Divide by 0") except ValueError: print("Error: Check your data inputs")
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 get_standard_deviation(data): value = get_variance(data) return round(square_root(value), 1)
def standard_deviation(data): # Validations empty_list_check(data) check_for_valid_numbers(data) return square_root(variance(data))
def square_root(self, a): self.result = square_root(a) return self.result
def population_standard_deviation(num): average = population_mean(num) s = 0.0 for i in num: s += (i - average) ** 2 return square_root(float(s) / len(num))