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Science International
  Year: 2014 | Volume: 2 | Issue: 2 | Page No.: 37-43
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Number of Parameters Counting in a Hierarchically Multiple Regression Model

H.J. Zainodin, Noraini Abdullah and S.J. Yap

Background: Manually, when a dependent variable is affected by a large number of independent variables, the number of parameters helps researchers determine the number of independent variables to be considered in an analysis. However, when there are many parameters to be estimated in a model, the manual counting is tedious and time consuming. Thus, this study derives a method to determine the number of parameters systematically in a model. Methods: The model building procedure in this study involves removing variables due to multicollinearity and insignificant variables. Eventually, a selected model is obtained with significant variables. Results: The findings of this study would enable researchers to count the number of parameters in a resulting model (selected model) with ease and speed. On top of that, models which fulfill the assumption are considered in the statistical analysis. In addition, human errors caused by manual counting can also be minimised and avoided by implementing the proposed procedure. Conclusion: These findings will also undoubtedly help many researchers save time when their analyses involve complex iterations.
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How to cite this article:

H.J. Zainodin, Noraini Abdullah and S.J. Yap, 2014. Number of Parameters Counting in a Hierarchically Multiple Regression Model. Science International, 2: 37-43.





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