This study investigates the problem of using ordinarily least squares (OLS) estimators in the presence of multicollinearity in regression analysis. As an alternative of OLS is ridge regression, which it is believed to be superior to least-squares regression in the presence of multicollinearity. Properties of ridge regression estimators and Methods of selecting biased ridge regression Parameter are discussed. We use data simulation to make comparison between Methods of ridge regression and (OLS) Method. According to a results of this study, we found Method of ridge regression are better than OLS Method when the Multicollinearity is exist. Our results have shown that the system stabilizes in a region of k