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Opara Jude
Osuji George A
Ogbonna Chukwudi J
Ekezie Dan Dan
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Opara Jude
Osuji George A
Ogbonna Chukwudi J
Ekezie Dan Dan
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International Journal of Statistics and Mathematics IF 2015: 4.232

Estimation of bivariate linear regression data via Jackknife algorithm

Accepted 23 May, 2014.

Citation: Osuji GA, Ekezie DD, Opara J, Ogbonna CJ (2014). Estimation of bivariate linear regression data via Jackknife algorithm. International Journal of Statistics and Mathematics 1(1): 009-015. 

Copyright: © 2014 Ekezie 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 cited.

Abstract

This paper is on the estimation of bivariate linear regression data using Jackknife algorithm. Jackknife delete-one algorithm was used to provide estimates of bivariate linear regression coefficient. The data used for this research were collected from Orji Town Primary School, Owerri North Imo State Nigeria. The data were on heights and weights of 20 randomly selected pupils in primary five and six. Partial estimates, Pseudo-Values, Jackknife Estimates, Jackknife Standard Deviations, and the Jackknife Standard Error were also computed. The result of the analysis revealed that the bias result was positive, which implies that the coefficient of correlation over-estimates the magnitude of the population correlation. For the regression, the jackknife parameters are linear functions of the standard estimates, which means that the values of  can be perfectly predicted from the values of . The jackknife predicted values and the confidence interval were also estimated.

Keywords: Jackknife algorithm, Bivariate linear regression, Partial estimates, Pseudo-Values