摘要
分层抽样技术是在实际工作中应用得非常广泛的抽样技术之一。但在文献中,还没有方便地可以用于在分层随机抽样中仅给定基本的样本数据时就能解决总体均值和总体比例的点估计和区间估计问题,计算总体均值时样本量的确定及分配问题,计算总体比例时样本量的确定及分配问题,事后分层抽样下总体均值和总体比例的点估计和区间估计等问题通用的R函数。本文自编了九个通用的R函数:Compute_Y_bar_st()、Compute_Y_bar_prop_from_y_bar_h_s_h_st()、Compute_Y_bar_srs_pst()、Compute_P_st()、Compute_P_from_a_h_st()、Compute_P_srs_pst()、Compute_nh_given_n_Y_bar_st()、Compute_n_nh_Y_bar_st()及Compute_n_nh_P_st(),它们将会为需要使用分层抽样技术以提高估计精度进行实际问题分析的使用者提供极大的方便。
Stratified sampling technique is one of the sampling techniques widely used in practical work. But in the literature, there are no convenient generic R functions to solve the problem of point estimation and interval estimation of population mean and population proportion, the problem of total sample size and each layer sample size when calculating population mean, the problem of total sample size and each layer sample size when calculating population proportion, and the problem of point estimation and interval estimation of population mean and population proportion in post-stratification sampling, in stratified random sampling when only basic sample data are given. We compile nine generic R functions: Compute_Y_bar_st(), Compute_Y_bar_prop_from_y_bar_h_s_h_st(), Compute_Y_bar_srs_pst(), Compute_P_st(), Compute_P_from_a_h_st(), Compute_P_srs_pst(), Compute_nh_given_n_Y_bar_st(), Compute_n_nh_Y_bar_st(), and Compute_n_nh_P_st(), which will provide great convenience for users who need to use stratified sampling technology to improve the estimation accuracy for practical problem analysis.
出处
《应用数学进展》
2022年第1期546-565,共20页
Advances in Applied Mathematics
