Abstract:
In this study we have proposed a modi ed ratio type estimator for population variance of
the study variable y under simple random sampling without replacement making use of
coe cient of kurtosis and median of an auxiliary variable x. The estimator's properties
have been derived up to rst order of Taylor's series expansion. The e ciency conditions
are derived theoretically under which the proposed estimator performs better than existing
estimators. Empirical studies have been done using real populations to demonstrate the
performance of the developed estimator in comparison with the existing estimators. The
proposed estimator as illustrated by the empirical studies performs better than the existing
estimators under some speci ed conditions i.e. it has the smallest Mean Squared Error and
the highest Percentage Relative E ciency. The proposed estimator is therefore suitable to
be applied to situations in which the variable of interest has a positive correlation with the
auxiliary variable.
