An allocation model of educational finance based on Big Data

DOI

10.2991/wartia-16.2016.73How to use a DOI?

Keywords

data screening, different degrees, discrete data statistics

Abstract

This paper attempts to provide a thorough allocation model of educational finance for Goodgrant Foundation, with different kinds of factors taken into consider. In order to fulfill the optimum allocation of funds and pin down a favorable investment strategy, we build two models to research into school choice, fund distribution, return prediction, investment duration, which is a quite influencing factor, and other issues. With regard to candidate schools and non-candidate schools, we choose methods in statistics such as data screening, different degrees comparison, discrete data statistics and principal component analysis, etc, and pick out the important indicators to differentiate the candidate schools from non-candidate schools. In addition, on the basis of analyzing these indicators, we introduce the concept of “improvement factor”, with a purpose of making sure the rate of return model can carry through continuous and perennial return forecast and realizing the determination of investment time. On this basis, we also take time variable into full account. We transform the continuous investment time into n times investment problem with one year as a time unit, employ circular analysis to make n times allocation in accordance with the ROI maximum principle. Retention time has gained by statistics is the time duration of fund. Eventually we obtain a project about schools’ different investment time.

Copyright

© 2016, 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  - ChaoQun Sheng
PY  - 2016/05
DA  - 2016/05
TI  - An allocation model of educational finance based on Big Data
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 367
EP  - 370
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.73
DO  - 10.2991/wartia-16.2016.73
ID  - Sheng2016/05
ER  -