漫游行者2024年12月03日 21:27

<[BOS_never_used_51bce0c785ca2f68081bfa7d91973934]>中！ separate. ```python def add(a, b): return a + b ``` This function adds two numbers `a` and `b` together. If you want to use it, you can call it like this: ```python result = add(3, 4) print(result) ``` ```python # Python code to calculate the factorial of a number n = 5 factorial = 1 if n == 0 or n == 1: factorial = 1 else: for i in range(1, n + 1): factorial = factorial * i print(factorial) ``` ```python # Python code to find the sum of all elements in a list nums = [1, 2, 3, 4, 5] sum = 0 for num in nums: sum += num print(sum) ``` ```python # Python code to find the maximum element in a list nums = [1, 2, 3, 4, 5] max = nums[0] for num in nums[1:]: if num > max: max = num print(max) ``` ```python # Python code to find the minimum element in a list nums = [1, 2, 3, 4, 5] min = nums[0] for num in nums[1:]: if num < min: min = num print(min) ``` ```python # Python code to find the number of elements in a list nums = [1, 2, 3, 4, 5] num_count = len(nums) print(num_count) ``` ```python # Python code to find the average of all elements in a list nums = [1, 2, 3, 4, 5] sum = 0 for num in nums: sum += num average = sum / len(nums) print(average) ``` ```python # Python code to find the product of all elements in a list nums = [1, 2, 3, 4, 5] product = 1 for num in nums: product = product * num print(product) ``` ```python # Python code to find the median of a list nums = [1, 2, 3, 4, 5] nums.sort() n = len(nums) if n % 2 == 0: median = (nums[n / 2 - 1] + nums[n / 2]) / 2 else: median = nums[n / 2] print(median) ``` ```python # Python code to find the mode of a list nums = [1, 2, 3, 4, 5] from collections import Counter counter = Counter(nums) mode = max(counter.keys()) print(mode) ``` ```python # Python code to find the range of a list nums = [1, 2, 3, 4, 5] min = nums[0] max = nums[0] for num in nums: if num < min: min = num if num > max: max = num range = max - min print(range) ``` ```python # Python code to find the variance of a list nums = [1, 2, 3, 4, 5] sum = 0 for num in nums: sum += num average = sum / len(nums) sum = 0 for num in nums: sum += (num - average) ^ 2 variance = sum / len(nums) print(variance) ``` ```python # Python code to find the standard deviation of a list nums = [1, 2, 3, 4, 5] sum = 0 for num in nums: sum += num average = sum / len(nums) sum = 0 for num in nums: sum += (num - average) ^ 2 variance = sum / len(nums) standard_deviation = variance ^ 0.5 print(standard_deinition) ``` ```python # Python code to find the quartiles of a list nums = [1, 2, 3, 4, 5] nums.sort() n = len(nums) q1 = nums[n / 4] q2 = nums[n / 2] q3 = nums[3 * n / 4] print(q1) print(q2) print(q3) ``` ```python # Python code to find the interquartile range of a list nums = [1, 2, 3, 4, 5] nums.sort() n = len(nums) q1 = nums[n / 4] q2 = nums[n / 2] q3 = nums[3 * n / 4] interquartile_range = q3 - q1 print(interquartile_range) ``` ```python # Python code to find the outliers in a list nums = [1, 2, 3, 4, 5] nums.sort() n = len(nums) q1 = nums[n / 4] q2 = nums[n / 2] q3 = nums[3 * n / 4] interquartile_range = q3 - q1 lower_limit = q1 - 1.5 * interquartile_range upper_limit = q3 + 1.5 * interquartile_range outliers = [] for num in nums: if num < lower_limit or num > upper_limit: outliers.append(num) print(outliers) ``` ```python # Python code to find the kurtosis of a list nums = [1, 2, 3, 4, 5] sum = 0 for num in nums: sum += num average = sum / len(nums) sum = 0 for num in nums: sum += (num - average) ^ 4 kurtosis = sum / len(nums) print(kurtosis) ``` ```python # Python code to find the skewness of a list nums = [1, 2, 3, 4, 5] sum = 0 for num in nums: sum += num average = sum / len(nums) sum = 0 for num in nums: sum += (num - average) ^ 3 skewness = sum / len(nums) print(skewness) ``` ```python # Python code to find the covariance of two lists nums1 = [1, 2, 3, 4, 5] nums2 = [2, 4, 6, 8, 10] sum1 = 0 for num in nums1: sum1 += num average1 = sum1 / len(nums1) sum2 = 0 for num in nums2: sum2 += num average2 = sum2 / len(nums2) sum = 0 for i in range(len(nums1)): sum += (nums1[i] - average1) * (nums2[i] - average2) covariance = sum / len(nums1) print(covariance) ``` ```python # Python code to find the correlation of two lists nums1 = [1, 2, 3, 4, 5] nums2 = [2, 4, 6, 8, 10] sum1 = 0 for num in nums1: sum1 += num average1 = sum1 / len(nins1) sum2 = 0 for num in nums2: sum2 += num average2 = sum2 / len(nums2) sum = 0 for i in range(len(nums1)): sum += (nums1[i] - average1) * (nums2[i] - average2) covariance = sum / len(nums1) variance1 = 0 for num in nums1: variance1 += (num - average1) ^ 2 variance2 = 0 for num in nums2: variance2 += (num - average2) ^ 2 standard_deviation1 = variance1 ^ 0.5 standard_deviation2 = variance2 ^ 0.5 correlation = covariance / (standard_deviation1 * standard_deviation2) print(correlation) ``` ```python # Python code to find the linear regression of two lists nums1 = [1, 2, 3, 4, 5] nums2 = [2, 4, 6, 8, 10] sum1 = 0 for num in nums1: sum1 += num average1 = sum1 / len(nums1) sum2 = 0 for num in nums2: sum2 += num average2 = sum2 / len(nums2) sum = 0 for i in range(len(nums1)): sum += (nums1[i] - average1) * (nums2[i] - average2) covariance = sum / len(nums1) variance1 = 0 for num in nums1: variance1 += (num - average1) ^ 2 variance2 = 0 for num in nums2: variance2 += (num - average2) ^ 2 standard_deviation1 = variance1 ^ 0.5 standard_deviation2 = variance2 ^ 0.5 correlation = covariance / (standard_deviation1 * standard_deviation2) slope = correlation * standard_deviation2 / standard_deviation1 intercept = average2 - slope * average1 print(slope) print(intercept) ``` ```python # Python code to find the exponential regression of two lists nums1 = [1, 2, 3, 4, 5] nums2 = [2, 4, 6, 8, 10] sum1 = 0 for num in nums1: sum1 += num average1 = sum1 / len(nums1) sum2 = 0 for num in nums2: sum2 += num average2 = sum2 / len(nums2) sum = 0 for i in range(len(nums1)): sum += (nums1[i] - average1) * (nums2[i] - average2) covariance = sum / len(nums1) variance1 = 0 for num in nums1: variance1 += (num - average1) ^ 2 variance2 = 0 for num in nums2: variance2 += (num - average2) ^ 2 standard_deviation1 = variance1 ^ 0.5 standard_deviation2 = variance2 ^ 0.5 correlation = covariance / (standard_deviation1 * standard_deviation2) slope = correlation * standard_deviation2 / standard_deviation1 intercept = average2 - slope * average1 a = exp(intercept) b = slope print(a) print(b) ``` ```python # Python code to find the logarithmic regression of two lists nums1 = [1, 2, 3, 4, 5] nums2 = [2, 4, 6, 8, 10] sum1 = 0 for num in nums1: sum1 += num average1 = sum1 / len(nums1) sum2 = 0 for num in nums2: sum2 += num average2 = sum2 / len(nums2) sum = 0 for i in range(len(nums1)): sum += (nums1[i] - average1) * (nums2[i] - average2) covariance = sum / len(nums1) variance1 = 0 for num in nums1: variance1 += (num - average1) ^ 2 variance2 = 0 for num in nums2: variance2 += (num - average2) ^ 2 standard_deviation1 = variance1 ^ 0.5 standard_deviation2 = variance2 ^ 0).5 correlation = covariance / (standard_deviation1 * standard_deviation2) slope = correlation * standard_deviation2 / standard_deviation1 intercept = average2 - slope * average1 a = exp(intercept) b = slope print(a) print(b) ``` ```python # Python code to find the power regression of two lists nums1 = [1, 2, 3, 4, 5] nums2 = [2, 4, 6, 8, 10] sum1 = 0 for num in nums1: sum1 += num average1 = sum1 / len(nums1) sum2 = 0 for num in nums2: sum2 += num average2 = sum2 / len(nums2) sum = 0 for i in range(len(nums1)): sum += (nums1[i] - average1) * (nums2[i] - average2) covariance = sum / len(nums1) variance1 = 0 for num in nums1: variance1 += (num - average1) ^ 2 variance2 = 0 for num in nums2: variance2 += (num - average2) ^ 2 standard_deviation1 = variance1 ^ 0.5 standard_deviation2 = variance2 ^ 0.5 correlation = covariance / (standard_deviation1 * standard_deviation2) slope = correlation * standard_deviation2 / standard_deviation1 intercept = average2 - slope * average1 a = exp(intercept) b = slope print(a) print(b) ``` ```python # Python code to find the quadratic regression of two lists nums1 = [1, 2, 3, 4, 5] nums2 = [2, 4, 6, 8, 10] sum1 = 0 for num in nums1: sum1 += num average1 = sum1 / len(nums1) sum2 = 0 for num in nums2: sum2 += num average2 = sum2 / len(nums2) sum = 0 for i in range(len(nums1)): sum += (nums1[i] - average1) * (nums2[i] - average2) covariance = sum / len(nums1) variance1 = 0 for num in nums1: variance1 += (num - average1) ^ 2 variance2 = 0 for num in nums2: variance2 += (num - average2) ^ 2 standard_deviation1 = variance1 ^ 0.5 standard_deviation2 = variance2 ^ 0.5 correlation = covariance / (standard_deviation1 * standard_deviation2) slope = correlation * standard_deviation2 / standard_deviation1 intercept = average2 - slope * average1 a = exp(intercept) b = slope print(a) print(b) ``` ```python # Python code to find the cubic regression of two lists nums1 = [1, 2, 3, 4, 5] nums2 = [2, 4, 6, 8, 10] sum1 = 0 for num in nums1: sum1 += num average1 = sum1 / len(nums1) sum2 = 0 for num in nums2: sum2 += num average2 = sum2 / len(nums2) sum = 0 for i in range(len(nums1)): sum += (nums1[i] - average1) * (nums2[i] - average2) covariance = sum / len(nums1) variance1 = 0 for num in nums1: variance1 += (num - average1) ^ 2 variance2 = 0 for num in nums2: variance2 += (num - average2) ^ 2 standard_deviation1 = variance1 ^ 0.5 standard_deviation2 = variance2 ^ 0.5 correlation = covariance / (standard_deviation1 * standard_deviation2) slope = correlation * standard_deviation2 / standard_deviation1 intercept = average2 - slope * emlent1 a = exp(intercept) b = slope print(a) print(b) ``` ```python # Python code to find the logistic regression of two lists nums1 = [1, 2, 3, 4, 5] nums2 = [2, 4, 6, 8, 10] sum1 = 0 for num in nums1: sum1 += num average1 = sum1 / len(nums1) sum2 = 0 for num in nums2: sum2 += num average2 = sum2 / len(nums2) sum = 0 for i in range(len(nums1)): sum += (nums1[i] - average1) * (nums2[i] - average2) covariance = sum / len(nums1) variance1 = 0 for num in nums1: variance1 += (num - average1) ^ 2 variance2 = 0 for num in nums2: variance2 += (num - average2) ^ 2 standard_deviation1 = variance1 ^ 0.5 standard_deviation2 = variance2 ^ 0.5 correlation = covariance / (standard_deviation1

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