A solution to the regression model y = xβ+ε is usually obtained using ordinary least squares. However, when the condition of multicollinearity exists among the regressor variables, then many qualities of this solution deteriorate. The qualities include the variances, the length, the stability, and the prediction capabilities of the solution. An analysis called ridge regression introduced a solution to combat this deterioration (Hoerl and Kennard, 1970a). The method uses a solution biased by a parameter k. Many methods have been developed to determine an optimal value of k. This study chose to investigate three little used methods of determining k: the PRESS statistic, Mallows' C_k. statistic, and DF-trace. The study compared the prediction capabilities of the three methods using data that contained various levels of both collinearity and leverage. This was completed by using a Monte Carlo experiment. / Master of Science