参数估计(python实现)

求置信区间

抽取样本, 样本量为200

np.random.seed(42)

coffee_full = pd.read_csv('coffee_dataset.csv')
coffee_red = coffee_full.sample(200) #this is the only data you might actually get in the real world.
coffee_red.head()

计算样本中喝咖啡的均值

(coffee_red[coffee_red['drinks_coffee'] == True]['height'].mean()
>68.11962990858618

重复抽取样本,计算其他样本中喝咖啡的均值,得到抽样分布

boot_means = []
for _ in range(10000):
    bootsample = coffee_full.sample(200, replace=True)
    mean = bootsample[bootsample['drinks_coffee'] == False]['height'].mean()
    boot_means.append(mean)

抽样分布

计算抽样分布的置信区间以估计总体均值, 置信度95%

np.percentile(boot_means, 2.5), np.percentile(boot_means, 97.5)

输出：

(65.7156685999191, 67.17367777514218)