Improved Estimation of Population Variance Utilizing Known Auxiliary Parameters

Abstract

Even similar things, whether created artificially or naturally, can vary. We should therefore try to estimate this variation. For improved population variance estimate, we propose a Searls ratio type estimator in the current research employing data on the tri-mean and the third quartile of the auxiliary variable. Up to the first-degree approximation, the suggested estimator's bias and mean squared error (MSE) are determined. The characterising scalar's ideal value is discovered, and given this ideal value, the least MSE is discovered. The mean squared errors of the suggested estimator and the competing estimators are contrasted conceptually and experimentally. Given that it has the lowest MSE of the above competing estimators, the recommended estimator has been shown to be the most effective.