A Newer Equal Part Linear Regression Model: A Case Study of the Influence of Educational Input on Gross National Income
Wen-Tsao Pan 1  
More details
Hide details
School of Business, Guangdong University of Foreign Studies, China
Wen-Tsao Pan   

School of Business, Guangdong University of Foreign Studies, China. Address to No.2 North Baiyuan Avenue, Baiyun District Guangzhou, China. Tel: +286-20-36207878
Online publish date: 2017-08-22
Publish date: 2017-08-22
EURASIA J. Math., Sci Tech. Ed 2017;13(8):5765–5773
Linear Regression Model (LRM) is not only a time – hornored reserch method but a simple and essential analytical technique for social science researchers. However, it cannot reveal the meaning of the extrme value included in genuine data, which constitutes the major concern for researchers in social scineces. To solve this problem, most researhcers turned to quantile regression model. This model, hoever, is not only obscure because it divides data by quantile but difficult to perform because it needs special software. The study therefore proposed a new concept as well as method: equal part linear regression model (EPLRM) which divides the sample data into equal parts and builds LRM on each part so that the research can both observe the data distribution of sample data within each part and compare the results with that of LRM. As the case study shows, the poverty of fiscial expenditure on education would decrease Gross Nation Income (GNI) greatly and the promotion of educational input of private schools and social donation would boost the increase of GNI to a lage degree.
1. Chen Jianbao & Dai Pingsheng (2007). Empirical Analysis of Spatial Characteristics between Education and GDP in Different Regions of China. Education & Economy, (3), 20-25.
2. Equal Part Linear Regression programming in R. (n.d.). Retrieved from http://eplrm.byethost31.com/.
3. Harding, M., & Lamarche, C. (2009). A quantile regression approach for estimating panel data models using instrumental variables, Economics Letters, 104(3), 133–135. doi:10.1016/j.econlet.2009.04.025.
4. Koenker, R. W. & Bassett, G. (1978). Regression Quantile, Econometrica, 46(1), 33-50. doi:10.2307/1913643.
5. Liu Kehui, Yan Xiaojun & Zhao Yishu et al. (2014). Experimental Study of Human Depth Perception Based on SPSS. Journal of North China Institute of Science and Technology, 11(8), 62-66.
6. Meligkotsidou, L., Vrontos, I. D., & Vrontos, S. D. (2009). Quantile regression analysis of hedge fund strategies, Journal of Empirical Finance, 16(2), 264–279. doi:10.1016/j.jempfin.2008.10.002.
7. Pan, W. T., Huang, C. E., & Chiu, C. L. (2016). Study on the performance evaluation of online teaching using the quantile regression analysis and artificial neural network. Supercomputing, (72), 789–803. doi:10.1007/s11227-015-1599-1.
8. Pan, W. T., Leu, Y. H., Zhu, W. Z., & Lin, W. Y. (2017). A Data Mining Approach to the Analysis of a Catering Lean Service Project, Intelligent Automation & Soft Computing, 23(2), 243-250. doi:10.1080/10798587.2016.1203564.
9. Watanabe, S., Nakamura, A., & Juang, B. H. (2014). Structural Bayesian Linear Regression for Hidden Markov Models, Journal of Signal Processing Systems, 74(3), 341-358. doi:10.1007/s11265-013-0785-8.
10. Yu. R. Y. and Rue, H. (2011). Bayesian inference for additive mixed quantile regression models, Computational Statistics & Data Analysis, 55(1), 84-96. doi:10.1016/j.csda.2010.05.006.
11. Zhang Yi. (2007). An Empirical Analysis on Educational Input and GDP Development Cointegration. Journal of Changchun Normal University, 26(8), 20-23.