def correlation(data, data1): z = zscore(data) z1 = zscore(data1) ztot = list(map(lambda x, y: x * y, z, z1)) corr = division(sum(ztot), len(ztot)) return round(corr, 4)
def correlation(data, data1): try: z1 = zscore(data) z2 = zscore(data1) z1_z2 = list(map(lambda x, y: x * y, z1, z2)) corr = sum(z1_z2) / len(z1_z2) return round(corr, 7) except ZeroDivisionError: print("Error: Can't Divide by 0") except ValueError: print("Error: Check your data inputs")
def p_value(numbers): x = [] n = len(numbers) z = zscore(numbers) for i in y: if i == z: x.append(y) return y
def confidence_interval(number_list, stand_dev): size = len(number_list) z_val = zscore(number_list) value_sum = 0 for x in number_list: value_sum = value_sum + x mean = value_sum / size result = mean + (z_val * (stand_dev / math.sqrt(size))) return result
def population_correlation_coefficient(numbers, numbers1): m = zscore(numbers) n = zscore(numbers1) value = list(map(lambda a, b: a * b, m, n)) p = division(len(value), sum(value)) return p
def zscore(self, data): validate(data) self.result = zscore(data) return self.result
def z_score(self, a): self.result = zscore(a) return self.result
def population_z_score(self, a): self.result = zscore(a) return self.result
def pvalue(num): return zscore(num)
def zscore(self, data): self.result = zscore(data) return self.result
def zscore(self, number_list): self.result = zscore(number_list) return self.result
def zscore(self, nums): self.data = zscore(nums) return self.data