Multiple Linear Regression to Forecast Balance of Trade
DOI:
https://doi.org/10.11113/mjfas.v7n2.255Keywords:
Balance of trade, import and export totals, multiple linear regression, regression model,Abstract
The main objective of this study is to build a regression model by using multiple linear regression (MLR) analysis. MLR will be used when there are two or more controlled variables involved in the relationship. There are four general steps in building a regression model which are checking assumptions, selecting suitable methods of MLR, interpreting the output and selecting the best MLR model. The objective will be evaluated by using a time series data that have been obtained from Monthly Statistical Bulletin Sabah, Department of Statistics Malaysia, Sabah which is from year 2003 to 2009. The monthly data of external trade in Sabah in term of import and export totals for seven years will be analysed by using Statistical Package for the Social Sciences (SPSS) Statistics Version 17.0. Balance of trade for year 2011 and above can be forecasted through the regression model that has been developed by using MLR analysis.References
Department of Industrial Development and Research, (2010, December 8), Retrieved from http://www.sabah.gov.my/didr/aLL_Links/Investment/ultimate/EconomicDev.htm.
R. Peck, C. Olsen & J. Devore (2001), Introduction to statistics and data analysis, California: Duxbury Thomson Learning.
A. Field (2003), Discovering statistics using SPSS for windows, London: SAGE Publications.
J.E. Matson & B.R. Huguenard (2007), Evaluating aptness of a regression model, Journal of Statistics Education 15(2).
Y.P. Chua (2009), Statistik penyelidikan lanjutan II: ujian regresi, analisis faktor dan analisis SEM, Kuala Lumpur: McGraw-Hill Education.
B.L. Bowerman, R.T. O’Connell & A.B. Koehler (2005), Forecasting, time series, and regression, 4th edition, California: Thomson Brooks/Cole.
R.I. Levin and D.S. Rubin (1994), Statistics for management, 6th edition, Englewood Cliffs, N.J.: Prentice Hall.
