Isaac Scientific Publishing

Journal of Advanced Statistics

Forecasting Students’ Enrollment Using Neural Networks and Ordinary Least Squares Regression Models

Download PDF (451.4 KB) PP. 45 - 57 Pub. Date: December 1, 2018

DOI: 10.22606/jas.2018.34001

Author(s)

  • Egbo, M.N
    Department of Statistics, Federal University of Technology, Owerri, Imo state, Nigeria
  • Bartholomew, D.C*
    Leadway Assurance Company ltd, Iponri, Lagos State, Nigeria

Abstract

Based on the presentation of dynamic and nonlinear data forecast, we discuss the difference in two approaches, Multi-layer feed-forward artificial neural networks and ordinary least squares regression for students’ enrolment forecast in FUTO, Nigeria. A simple procedure to include the Mean square error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) is proposed and tested. The result suggested that the Multi-Layer Feed-Forward Artificial Neural Networks provides better predictions for nonlinear and chaotic systems.

Keywords

Forecasting, mean square, error, neural network, least squares.

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