A New Ratio and Product Type Estimator for Mean Under Stratified Random Sampling

Nikita; Sangeeta Malik; Sumiti

doi:10.17485/IJST/v16i48.2441

Article

A New Ratio and Product Type Estimator for Mean Under Stratified Random Sampling

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i48.2441

Year: 2023, Volume: 16, Issue: 48, Pages: 4703-4709

Original Article

A New Ratio and Product Type Estimator for Mean Under Stratified Random Sampling

Nikita¹, Sangeeta Malik^2*, Sumiti³

¹Research scholar, Dept. of Mathematics, BMU, Asthal Bohar, Rohtak, 124021, Haryana, India
²Professor, Dept. of Mathematics, BMU, Asthal Bohar, Rohtak, 124021, Haryana, India
³Research scholar, Dept. of Mathematics, BMU, Asthal Bohar, Rohtak, 124021, Haryana, India

*Corresponding Author
Email: [email protected]

Received Date:25 September 2023, Accepted Date:07 November 2023, Published Date:28 December 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The present study deals with the formation of a modified ratio and product type estimator for estimating finite population mean using auxiliary variables under stratified random sampling. Methods: The expression for the bias and mean square error (MSE) of the suggested estimator was computed upto the first degree of approximation by using the ratio and product method of estimation. We used the data of Area and Production of Horticulture Crops, India obtained from the official website of National horticulture board. The another dataset was taken from a project that was undertaken by Department of Animal Husbandry of a state government, Punjab for comparison. An analysis was conducted to compare the suggested estimator with Chami's and other existing estimators. Findings: The optimum value of the proposed estimator has been obtained. The suggested estimator is reported to be more effective than the conventional mean, ratio, and product type estimators. We have used two data sets obtained through a stratified random sampling strategy to evaluate the effectiveness of the estimators discussed. The percent relative efficiency of envisaged estimator was obtained from population I is 130.05 and 141.56, from population II is 137.31 and 146.8 that is greater than existing estimators. The numerical illustration also showed that the new estimator is more efficient than simple mean, ratio, and product type estimators. Novelty: We proposed a new estimator that has practical implications. The comparative analysis has been done to set up the condition for which the suggested estimators are more efficient than other estimator with novelty.

Keywords: Estimator, Stratified, Random sampling, Mean Square Error, Bias

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Copyright

© 2023 Nikita et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)