원문정보
초록
영어
The multiple linear regression model contains more than one predictor variable and it shows the relationship among multiple variables. In the existing research field of rural-urban income disparity, the method of multiple regression analysis is mainly employed. But the linear relationship among variables is estimated mainly depending on principal component analysis. Principal component analysis is used to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The principal component analysis is widely used for feature extraction to reveal the most main factors from the multiple aspects. A multiply linear regression model integrating principal components analysis is proposed to address on the income gap between the city and country. The influential factors are given and the analysis results are discussed in this paper. The experimental results on income data from 1990 to 2013 show that the proposed method is effective in predicting the income ratio and analyzing the influential factors.
목차
1. Introduction
2. Multiple Regression Analysis Integrating Principal Component Analysis
2.1. Multiple Regression Analysis
2.2. Principal Component Analysis
3. Predicting Rural-Urban Income Disparity Ratio by Integrating Multiple Linear Regression Model and Principal Component Analysis
3.1. Prediction Model
3.2. Feature Variables
4. Example Analysis
4.1. Experimental Dataset
4.2. Principal Component Analysis on Experimental Dataset
4.3. Predicting by Multiple Regression Analysis Model
5. Conclusion
References