*Dr. Khasraw Abdulla Rashid ^{1} & Dr. Hanaw A. Amin^{2}*

*1 Department of Soil and Water Science, Faculty of Agricultural
Science, University of Sulaimani, 2 Faculty of Science and Science Education,
University of Sulaimani.*

*E- Mail: khasraw.rashid@univsul.edu.iq*

**Original:**08.10.2015

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**Revised:**31.01.2016

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**Accepted:**25.02.2016

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**Published online:**20.09.2016

**DOI Link:**

**ABSTRACT**

Statistical literature has several methods for coping with multicollinearity problem. Ridge regression “RR” is compared with multiple linear regression for studying “Suction Characteristics of Subgrade Soils from North Iraq- SCSSNI”. Multiple linear regression “MLR” of “SCSSNI” data usually encounters a collinearly problem, which adversely affects long term prediction performance. The collinearly problem can be eliminated or greatly improved by using ridge regression, which is a biased estimation method with potentially smaller mean square error “MSE” as an alternative to ordinary least square “OLS”. In this study, ridge regression (a biased estimation method has been evaluated with a constant bias ) and the prediction performance was compared with that of ordinary least square “OLS” based multiple linear regression “MLR”. The bias constant of was selected bu examining the ridge trace. At this point, the estimated coefficients are stable and their variance inflation factors “VIFs” become smaller. To evaluate the robustness of each model the standard error of prediction” SEPs” has been compared, the prediction of original values using MLR model shows slightly better results comparing to that of ridge regression model, which is due to an intentional bias is associated in the ridge model.

**KEYWORDS**

Multiple Regression Model, Ridge Regression Model, Collinearly, SCSSNI.

**FULL TEXT:**