Open AccessModified Ratio-Cum-Product Estimators of Population Mean Using Two Auxiliary Variables
Author(s) -
Jamiu Olasunkanmi Muili,
Eric Ndukaku Agwamba,
Yahqub Ayinde Erinola,
Mojeed Abiodun Yunusa,
Ahmed Audu,
Muslihu Adeyemi Hamzat
Publication year - 2020
Publication title -
asian journal of research in computer science
Language(s) - English
Resource type - Journals
ISSN - 2581-8260
DOI - 10.9734/ajrcos/2020/v6i130152
Subject(s) - estimator , mathematics , mean squared error , statistics , bootstrapping (finance) , ratio estimator , percentile , simple random sample , population , population mean , sample size determination , product (mathematics) , standard error , efficient estimator , econometrics , minimum variance unbiased estimator , demography , sociology , geometry
A percentile is one of the measures of location used by statisticians showing the value below which a given percentage of observations in a group of observations fall. A family of ratio-cum-product estimators for estimating the finite population mean of the study variable when the finite population mean of two auxiliary variables are known in simple random sampling without replacement (SRSWOR) have been proposed. The main purpose of this study is to develop new ratio-cum-product estimators in order to improve the precision of estimation of population mean in sample random sampling without replacement using information of percentiles with two auxiliary variables. The expressions of the bias and mean square error (MSE) of the proposed estimators were derived by Taylor series method up to first degree of approximation. The efficiency conditions under which the proposed ratio-cum-product estimators are better than sample man, ratio estimator, product estimator and other estimators considered in this study have been established. The numerical and empirical results show that the proposed estimators are more efficient than the sample mean, ratio estimator, product estimator and other existing estimators.