International Journal of Mathematical, Engineering and Management Sciences Vol. 4, No. 5, 1228–1238, 2019 https://dx.doi.org/10.33889/IJMEMS.2019.4.5-097 1228 Improved Estimators for Estimating Average Yield Using Auxiliary Variable S. K. Yadav Department of Statistics Babasaheb Bhimrao Ambedkar University, Lucknow-226025, U.P., India M. K. Dixit Department of Statistics Babasaheb Bhimrao Ambedkar University, Lucknow-226025, U.P., India H. N. Dungana School of Mathematical Sciences University of Technology Sydney, Ultimo NSW 2007, Australia S. S. Mishra Department of Mathematics and Statistics (Centre of Excellence) Dr. Rammanohar Lohia Avadh University, Ayodhya-224001, U.P., India Corresponding author: sant_x2003@yahoo.co.in (Received January 20, 2019; Accepted May 21, 2019) Abstract In this paper, we consider the improved estimation of average production of peppermint at block level of Barabanki district of Uttar Pradesh State (India). We suggest certain estimators for population-mean. Here, population refers to production population as study variable and auxiliary-variable refers to Area of field. We study the sampling properties naming bias and MSE of estimators, which are presently proposed by us in the paper. We compare our proposed estimators with other ones existing in literature. For the support of the theoretical findings, we carry out a numerical study for the natural population on primary data collected from Banikodar Block of Barabanki District situated in Uttar Pradesh State. Keywords- Population variable, Auxiliary variable, Ratio-estimator, MSE, PRE. 1. Introduction Literature-review reveals that applying auxiliary-information enhances estimator’s efficiency under consideration whenever we estimate any parameter. It has been now evident that auxiliary- variable technique improves the estimation process for target-population. Primary and the secondary variables have a high correlation to each other. They may have both negative and positive correlations. Ratio type estimators are preferred when primary and secondary variables are highly positively correlated while product types estimators when they have high negative correlation. As production (primary) and the area (secondary) are highly positively correlated so we consider the ratio types estimators only in the present study. Watson (1937) used subsidiary variable and suggested the traditional regression-estimator of population mean of main variable. Usual Ratio estimator utilizing positively correlated auxiliary- information was given in Cochran (1940). The well-known product estimators was independently introduced by Robson (1957) and Murthy (1964) using negatively correlated auxiliary variable.