✨
AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
In addition, theoretical findings are veried with the help of real data sets.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:This paper deals with efficient ratio type estimators for estimating finite population mean under simple random sampling scheme by using the knowledge of known median of a study and an auxiliary variable.
📊
Key Findings & Data:It is found that our proposed estimators perform better as compared to the traditional ratio estimator, regression estimator, Subramani and Kumarapandiyan [23], Subramani and Prabavathy [24] and Yadav et al. [28] estimators.
💡
Conclusion & Impact:In addition, theoretical findings are veried with the help of real data sets.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Auxiliary variables · Bias · Eciency
Abstract
This paper deals with efficient ratio type estimators for estimating finite population mean under simple random sampling scheme by using the knowledge of known median of a study and an auxiliary variable. Expressions for the bias and mean squared error of the proposed ratio type estimators are derived up to first order of approximation. It is found that our proposed estimators perform better as compared to the traditional ratio estimator, regression estimator, Subramani and Kumarapandiyan [23], Subramani and Prabavathy [24] and Yadav et al. [28] estimators. In addition, theoretical findings are veried with the help of real data sets.
🔖
Cite This Article
LİN, Z., IRFAN, M., ABİD, M., JAVED, M. (2022). Improved ratio type estimators of population mean based on median of a study variable and an auxiliary variable. Hacettepe Journal of Mathematics and Statistics, Cilt: 47 - Sayı: 3, 659–673. https://ajindex.com/articles/529842