根据样本数据计算置信区间 [英] Compute a confidence interval from sample data

问题描述

我有一些样本数据，假设正态分布，我想为它们计算一个置信区间.

I have sample data which I would like to compute a confidence interval for, assuming a normal distribution.

我已经找到并安装了numpy和scipy软件包，并已获取numpy以返回均值和标准差(numpy.mean(data)，其中data为列表).对于获得样本置信区间的任何建议将不胜感激.

I have found and installed the numpy and scipy packages and have gotten numpy to return a mean and standard deviation (numpy.mean(data) with data being a list). Any advice on getting a sample confidence interval would be much appreciated.

推荐答案

import numpy as np
import scipy.stats


def mean_confidence_interval(data, confidence=0.95):
    a = 1.0 * np.array(data)
    n = len(a)
    m, se = np.mean(a), scipy.stats.sem(a)
    h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)
    return m, m-h, m+h

您可以像这样计算.

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