On Robust Estimation through the Use of Auxiliary Information by Ratio and Regression Estimators

Abstract

The ratio and regression estimators that make use of auxiliary information for achieving higher efficiency is applied to education data. Education is critical to our development as individuals and as societies, and it paves the way to a successful and productive future. It provides the potentials for an individual’s intellectual growth and productivity in the society. The objective of this paper is to estimate the ratio of pupils to classroom in Nigeria’s public primary schools as well as to estimate the total pupil population in Nigeria’s public primary schools using the ratio and regression estimators. The data of annual enrolment into public primary schools and the number of classrooms in 2014 were obtained from Universal Basic Education Commission.  Furthermore, the sampling design used is stratified random sampling with equal allocation. Two states were selected from each geo-political zone; making a sample of 12 states The results of the ratio estimator revealed that the estimated national pupil-classroom ratio is approximately 54 and the confidence interval shows that the ratio may lie between the inter 43 and 65 approximately. Similarly, total pupils population is estimated at 20,298,309 and the confidence interval shows that the total population may lie between the inter 16,084,553 to 24,512,065 approximately. The ratio and regression estimators will save time and cost to give reliable estimates. Similarly, using the regression estimator total pupils population is estimated at 20,412,402 and the confidence interval shows that the total population may lie between the inter 16,210,204 to 24,614,600 approximately. Based on this analysis, it is therefore recommended that effort should be intensified to improve the pupil-classroom ratio nationwide and to increase pupils’ enrolment.

Key words: Bias, Enrolment, Coefficient of Variation, Confidence interval, Ratio estimation, Regression estimation Robust estimates, Standard error, Variance

Abbreviations: EFA - Education for All, UBE - Universal Basic Education, SRS – Simple Random Sample, UNESCO-United Nations Educational, Scientific and Cultural Organization