Abstract
In practical sample surveys, stratified sampling is widely used to estimate population parameters with high precision. Although point estimation for a population mean was investigated from all viewpoints, interval estimation has not been sufficiently studied. This paper is concerned with several kinds of confidence intervals based on stratified random samples. Firstly we propose a confidence interval procedure for the population mean that uses Student's t-distribution, and also show that t-statistic can be constructed from stratified random sample when the objective variable is normal distributed in each stratum and the sample is drawn with Neyman allocation(the optimum allocation). If this sample allocation method is not used, the confidence interval is improved using Satterthwaite approximation for the distribution of the statistic unless the strata are terribly skew. In the difficult situation, we obtain the aid of the nonparametric bootstrap method. The coverage probabilities and some other properties for all methods considered in this paper are investigated through the simulation study for some theoritical distributions and actualdata.
