Application of Principal Component Regression Analysis in Economic Analysis

DOI

10.2991/msetasse-15.2015.255How to use a DOI?

Keywords

regression analysis; principal component analysis; principal component regression analysis; R software

Abstract

In regression analysis, when the independent variables appear multicollinearity, the general effect of the classical regression method for least square estimates of regression coefficients will be poor, but principal component analysis can overcome this deficiency effectively. In this paper, we combined principal component analysis with classical regression analysis. Firstly the principal component analysis was used to a group of sample data based on the introduction of two statistical methods and R software which can lower the dimension of high variant space, then classical regression analysis was used to the sample data to get the quantitative relationship between the variables, and finally compared the two results in order to explain principal component regression analysis is more accurate than the classical regression analysis.

Copyright

© 2015, 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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TY  - CONF
AU  - Ming-ming Chen
AU  - Jing-lian Ma
PY  - 2015/11
DA  - 2015/11
TI  - Application of Principal Component Regression Analysis in Economic Analysis
BT  - Proceedings of the 2015 3rd International Conference on Management Science, Education Technology, Arts, Social Science and Economics
PB  - Atlantis Press
SP  - 1205
EP  - 1208
SN  - 2352-5398
UR  - https://doi.org/10.2991/msetasse-15.2015.255
DO  - 10.2991/msetasse-15.2015.255
ID  - Chen2015/11
ER  -