International Journal For Research In Mathematics And Statistics

Vol. 1 No. 1 (2015): International Journal For Research In Mathematics And Statistics (ISSN: 2208-2662)
Articles

Jackknife Algorithm on Linear Regression Estimation

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  • Esemokumo Perewarebo Akpos,
  • Bekesuoyeibo Rebecca,
  • Okenwe Idochi
Esemokumo Perewarebo Akpos
Department of Statistics, School of Applied Science, Federal Polytechnic Ekewe Yenagoa, Bayelsa State, Nigeria
Bekesuoyeibo Rebecca
Department of Statistics, School of Applied Science, Federal Polytechnic Ekewe Yenagoa, Bayelsa State, Nigeria
Okenwe Idochi
Department of Statistics, School of Applied Sciences, Rivers State Polytechnic, PMB 20, Bori, Rivers State, Nigeria
DOI: https://doi.org/10.53555/ms.v1i1.910

Published 2015-01-31

Keywords

  • Jackknife algorithm,
  • simple regression,
  • Pseudo-Values,
  • Confidence interval,
  • Bias,
  • correlation coefficient
    • ...More
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How to Cite

Akpos, E. P., Rebecca, B., & Idochi, O. (2015). Jackknife Algorithm on Linear Regression Estimation. International Journal For Research In Mathematics And Statistics, 1(1), 34–40. https://doi.org/10.53555/ms.v1i1.910
Creative Commons License

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

Abstract

In this paper, interest was on the estimation of simple linear regression data using Jackknife algorithm. Thus, Jackknife delete-one algorithm was employed to provide estimates of simple linear regression coefficient. Observations on systolic blood pressure (SBP) and age for a sample of 30 randomly selected patients were collected from Federal Medical Centre Owerri Imo State Nigeria. It was discovered that all errors in the ydirection are normally distributed. The statistical software known as Stata version 9.1 was employed for the ease of the analysis. Pseudo-Values, Jackknife Estimates, and the Jackknife Standard Error were computed. From the analysis, it was revealed that the bias result of the correlation was positive. The result from the OLS shows that SBP on Age of patients is significant. The jackknife standard error and confidence intervals of the Age coefficient based on the distribution     J F  ˆ  are substantially larger than the estimated OLS standard error due to the inadequacy of the jackknife in small samples. Comparing the jackknife coefficients averages J  0  ˆ  and  J  1  ˆ  with the corresponding OLS estimates 0 ˆ   and 1  ˆ  shows that there is a little bias in the jackknife coefficients. 

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