from scipy import stats import numpy as np import math s = np.array([1, 2, 3, 4, 4, 4, 5, 5, 5, 5, 4, 4, 4, 6, 7, 8]) n, min_max, mean, var, skew, kurt = stats.describe(s) std = math.sqrt(var) # std为标准差, 如需求方差s.std() and s.var() # loc为mean, scale为标准差. # 求正态分布95%置信区间 CI = stats.norm.interval(0.95, loc=mean, scale=std) # 随机生成1000个样本 norm_samples = stats.norm.rvs(loc=mean, scale=std, size=1000) # 求gamma置信区间 gamma(a, b) CI_gamma = stats.gamma.interval(0.95, a, scale=1/b) # 随机生成1000个样本 gamma_samples = stats.gamma.rvs(a, scale=1/b, size=1000)
用scipy求置信区间confident interval和随机生成分布样本
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