PENAKSIR RASIO REGRESI UNTUK RATA-RATA POPULASI MENGGUNAKAN MINIMUM DAN MAKSIMUM VARIABEL TAMBAHAN

Abstract

This article studies three type of regression ratio estimators for the population mean on simple random sampling using minimum and maximum auxiliary variables. This study is a review from the article of Singh et. al. [Statistics in Transition, 10 (2009): 85-100]. Then, the mean square error of this three estimators are compared. Estimator with the smallest mean square error is the most efficient estimator. An example is given at the end of the discussion.This article studies three type of regression ratio estimators for the population mean on simple random sampling using minimum and maximum auxiliary variables. This study is a review from the article of Singh et. al. [Statistics in Transition, 10 (2009): 85-100]. Then, the mean square error of this three estimators are compared. Estimator with the smallest mean square error is the most efficient estimator. An example is given at the end of the discussion.

This article studies three type of regression ratio estimators for the population mean on simple random sampling using minimum and maximum auxiliary variables. This study is a review from the article of Singh et. al. [Statistics in Transition, 10 (2009): 85-100]. Then, the mean square error of this three estimators are compared. Estimator with the smallest mean square error is the most efficient estimator. An example is given at the end of the discussion.