OPTIMUM FAMILY OF ESTIMATORS IN SIMPLE RANDOM SAMPLING OF FINITE POPULATION MEAN USING TWO AUXILIARY VARIABLES WITH APPLICATIONS IN FISHERIES AND ECONOMICS SECTORS

To estimate a finite population mean when the population mean of the auxiliary variable is known, we propose a new family of finite population mean estimators designed for use in simple random sampling situations. We performed first-order approximations of these new estimates and presented mathematical models for their biases and mean square error (MSE). To evaluate the effectiveness of these predictions, we made theoretical and empirical comparisons under different conditions and compared them with traditional predictions. This article demonstrates the advantages and benefits of these new estimators in the forecasting process by providing information on their performance in various simulation configurations.