Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications

A Comparison of Three Estimation Methods In Linear Regression Analysis

Authors
Xianghong Luo
Corresponding Author
Xianghong Luo
Available Online January 2017.
DOI
https://doi.org/10.2991/icmmita-16.2016.92How to use a DOI?
Keywords
Linear Regression; Least Ordinary Square; Method of Moment; Maximum Likelihood Estimate; Hypothesis Testing; R-square
Abstract

The paper begins with an introduction of some crucial definitions apropos of regression analysis. Then it discusses briefly the concept of R-square that verifies the accuracy of a regression model and of Hypothesis Testing that tests hypothesis made concerning the population. The main part of the paper then focuses on three estimation methods that estimate the parameters of a regression model: Ordinary Least Square, Method of Moments, and Maximum Likelihood Estimation. The paper concludes with a discussion on the advantages and disadvantages of each method and the possible applications of linear regression.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
Series
Advances in Computer Science Research
Publication Date
January 2017
ISBN
978-94-6252-285-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmmita-16.2016.92How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xianghong Luo
PY  - 2017/01
DA  - 2017/01
TI  - A Comparison of Three Estimation Methods In Linear Regression Analysis
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 498
EP  - 502
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.92
DO  - https://doi.org/10.2991/icmmita-16.2016.92
ID  - Luo2017/01
ER  -