eng Ansari Education and Research Society Journal of Ultra Scientist of Physical Sciences December 2008 20 3 723 728 jusps-B 1397 article Muhammad Iqbal Al-Banna Bin Ismail 1 Zulhanif 2 Ismail Bin Mohd (muhammad_iqbal_albanna@yahoo.com) 3 Department of Economics, Faculty of Management and Economics, Universiti Mayalsia Terengganu (UMT), MALASIA Department of Statics, FMIPA Universitas Padjadjaean, Bandung (INDONESIA) Department of Mathematics, Faculty Science and Technology, Universiti Malaysia, Terengganu (UMT) MALAYSIA <p style="text-align: justify;">Multicollinearity is a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated. In this situation the coefficient estimates and significance tests for each predictor involved may be underestimated. However many case in the econometric models reported with few observations multicollinearity which could be misleading inferences based on regression models. Ridge estimators are often used to alleviate the problem of multicollinearity. Ridge regression, based on adding a smally quantity k, to the diagonal of a correlation matrix of highly collinear independent variables, can reduce the error variance of estimators, but at the expense of introducing bias. Because bias is a monotonic increasing function of k, the problem of the appropriate amount of k to introduce as the ridge analysis increment has yet to be resolved This paper proposes alternative method for estimate regression coefficients used Bayesian method via Gibss sampling.</p> <p style="text-align: justify;">&nbsp;</p> https://ultraphysicalsciences.org/paper/1397/ Bayesian Analysis Multicollinearity Ridge Regression Gibss Sampling