OJAppS  Vol.3 No.4 , August 2013
Forecasting Number of Students in University Department: Modeling Approach
ABSTRACT

In this study, the mathematical models of dynamics of student populations in the university departments are formulated. As a case study, we employ the data of registration section from Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok (KMUTNB), Thailand, from calendar year 2006 to 2010. Using regression analysis, descriptive model and explanatory model are derived. The descriptive model is linear with R2 = 0.8864. Using log-transformation, the explanatory model gives the nonlinear approximation with R2 = 0.8293. The model predicts that the number of students of Department of Mathematics, KMUTNB has a tendency to linearly increase with slope of 20 with 95% CI (6.8417, 33.1583). The application of the models in educational management is discussed.


Cite this paper
N. Patanarapeelert and K. Patanarapeelert, "Forecasting Number of Students in University Department: Modeling Approach," Open Journal of Applied Sciences, Vol. 3 No. 4, 2013, pp. 293-297. doi: 10.4236/ojapps.2013.34037.
References
[1]   R. Q. Lavilles and M. J. B. Arcilla, “Enrollment Fore casting for School Management System,” International Journal of Modeling and Optimization, Vol. 2, No. 5, 2012, pp. 563-566. doi:10.7763/IJMO.2012.V2.183

[2]   S. Choudhuri, C. R. Standridge, C. Griffin and W. Wen ner, “Enrollment Forecasting for an Upper Division Gen eral Education Component,” Proceedings of the 37th ASEE/ IEEE Frontiers in Education Conference, Milwaukee, 10 13 October 2007, pp. T3E-25-T3E-28.

[3]   D. Y. Young and L. J. Redlinger, “Modeling Student Flows through the University’s Pipelines,” Proceedings of the 41st Forum of the Association for Institutional Research, Long Beach, 5 June 2001, pp. 1-13.

[4]   J. D. Logan and W. R. Wolesensky, “Mathematical Me thods in Biology,” John Wiley & Sons, Hoboken, 2009.

[5]   M. F. Triola, “Elementary Statistics,” Pearson Education, Inc., Boston, 2004.

[6]   R. Peck, C. Olsen and J. L. Devore, “Introduction to Sta tistics and Data Analysis,” Brooks/Cole Cengage Learn ing, Boston, 2012.

[7]   G. G. Woodworth, “Biostatistics: A Bayesian Introduc tion,” John Wiley & Sons, Inc., Hoboken, 2004.

 
 
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