# ESTIMATING POPULATION MEAN USING RATIO ESTIMATOR FOR MEAN IS LESS THAN VARIANCE IN SIMPLE RANDOM SAMPLING

## Main Article Content

## Abstract

*This paper presents a thorough investigation into the estimation of population mean using ratio estimators within the framework of simple random sampling, with a particular focus on scenarios where the population mean is less than the variance. **The paper underscores the importance of including auxiliary variables and their specific measures in the construction of ratio estimators. Factors such as sample size, coefficient of variation, kurtosis, and correlation coefficients prove to be valuable in improving estimation accuracy across different sample sizes, particularly when the population mean is less than the variance. The proposed estimator is based on the ratio of the sample mean and the sample median of the variable of interest, and it Incorporates the information on the population size and the sample size. We derive the bias and the mean squared error of the proposed estimator, and compare its performance with some existing ratio estimators using simulated and real data sets. The results show that the proposed estimator has smaller bias and mean squared error than the conventional ratio estimator and some other ratio estimators under certain conditions. We also provide some recommendations for choosing an appropriate ratio estimator depending on the characteristics of the population.*

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