Article Fingerprint
ReserarchID
7RH7O
This paper examines a class of regression estimator with cum-dual product estimator as intercept for estimating the mean of the study variable Y using auxiliary variable X. We obtained the bias and the mean square error (MSE) of the proposed estimator. We also obtained MSE of its asymptotically optimum estimator (AOE). Theoretical and numerical validation of the proposed estimator was done to show it’s superiority over the usual simple random sampling estimator and ratio estimator, product estimator, cum-dual ratio and product estimator. It was found that the asymptotic optimal value of the proposed estimator performed better than other competing estimators and performed in exactly the same way as the regression estimator, when compared with the usual simple random estimator for estimating the average sleeping hours of undergraduate students of the department of statistics,
N.A. Adegoke. 2015. \u201cA Class of Regression Estimator with Cum-Dual Product Estimator as Intercept\u201d. Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 15 (GJSFR Volume 15 Issue F3): .
Crossref Journal DOI 10.17406/GJSFR
Print ISSN 0975-5896
e-ISSN 2249-4626
Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.
Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.
Total Score: 102
Country: Nigeria
Subject: Global Journal of Science Frontier Research - F: Mathematics & Decision
Authors: F.B. Adebola, N.A. Adegoke (PhD/Dr. count: 0)
View Count (all-time): 186
Total Views (Real + Logic): 4322
Total Downloads (simulated): 2128
Publish Date: 2015 05, Mon
Monthly Totals (Real + Logic):
This paper attempted to assess the attitudes of students in
Advances in technology have created the potential for a new
Inclusion has become a priority on the global educational agenda,
This paper examines a class of regression estimator with cum-dual product estimator as intercept for estimating the mean of the study variable Y using auxiliary variable X. We obtained the bias and the mean square error (MSE) of the proposed estimator. We also obtained MSE of its asymptotically optimum estimator (AOE). Theoretical and numerical validation of the proposed estimator was done to show it’s superiority over the usual simple random sampling estimator and ratio estimator, product estimator, cum-dual ratio and product estimator. It was found that the asymptotic optimal value of the proposed estimator performed better than other competing estimators and performed in exactly the same way as the regression estimator, when compared with the usual simple random estimator for estimating the average sleeping hours of undergraduate students of the department of statistics,
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.