Journal of Science and Arts Year 21, No. 2(55), pp. 347-354, 2021 http://doi.org/10.46939/J.Sci.Arts-21.2-a03 Mathematics Section ORIGINAL PAPER AN EFFICIENT CLASS OF PRODUCT ESTIMATORS USING MEASURES OF DISPERSIONS MUHAMMAD IJAZ 1 , TOLGA ZAMAN 2* , BUSHRA HAIDER 1 , SYED MUHAMMAD ASIM 1 _________________________________________________ Manuscript received: 12.11.2020; Accepted paper: 30.03.2021; Published online: 30.06.2021. Abstract. The study suggests a class of product estimators for estimating the population mean of variable under investigation in simple random sampling without replacement (SRSWOR) scheme when secondary information on standard deviation, mean deviation, and quartile deviation is available. The expression for Bias and Mean Square Error (MSE) has been derived. A comparison is made both theoretically and numerically with other existing product estimators. It is concluded that compared to other product type estimators, suggested class of estimators estimate the population mean more efficiently. Keywords: standard deviation, mean deviation, quartile deviation, bias and MSE. 1. INTRODUCTION In Survey sampling, auxiliary information plays an important role to improve the efficiency of an estimator. A lot of new methods are suggested by researchers to estimate the population mean efficiently through the use of secondary information by taking the benefit of correlation between the study variable and variable under investigation. The following notation would be considered in the research study: N – Population Size, n – Sample Size, Y – Study variable X – Variable under interest, ,Y X - Population means, ,y x - Sample means S x S y -Standard deviation, C x C y - Coefficient of variation,  - Correlation coefficient between X and Y, 2( ) x  - Kurtosis of auxiliary variable M d - Mean deviation, Q d – Quartile deviation. The concept of an auxiliary variable was suggested by Cochran [1] for the first time to improve the efficiency of variable under investigation. Robson [2] proposed the classical product estimator given as, 1 University of Peshawar, Department of Statistics, Peshawar, Pakistan. E-mail: ijaz.statistics@gmail.com ; smasim@uop.edu.pk ; kkhattak422@gmail.com . 2 Cankiri Karatekin University, Department of Statistics, 18100 Cankiri, Turkey. * Corresponding author: zamantolga@gmail.com