Related Experiment Video
Updated: Sep 6, 2025

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
Published on: May 22, 2015
Use of an efficient unbiased estimator for finite population mean
Javid Shabbir1, Ronald Onyango2
1Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan.
Abstract:
In this study, we propose an improved unbiased estimator in estimating the finite population mean using a single auxiliary variable and rank of the auxiliary variable by adopting the Hartley-Ross procedure when some parameters of the auxiliary variable are known. Expressions for the bias and mean square error or variance of the estimators are obtained up to the first order of approximation. Four real data sets are used to observe the performances of the estimators and to support the theoretical findings. It turns out that the proposed unbiased estimator outperforms as compared to all other considered estimators. It is also observed that using conventional measures have significant contributions in achieving the efficiency of the estimators.
Related Concept Videos
Confidence Interval for Estimating Population Mean
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the...
Distributions to Estimate Population Parameter
What are Estimates?
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
Estimating Population Mean with Known Standard Deviation
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...

