The most widely used statistical methods for analyzing categorical outcome variables and Linear Discriminant Analysis and Logistic Regression, If a dependent variable is a binary outcome, an analyst can choose among Discriminant Analysis, Logistic and Multiple Regression. The statistical assumption. In the presence of multicollinearity, the ordinary least squares (OLS) estimator could become unstable due to their large variance, which leads to poor prediction. One of the popular solutions to this problem is Ridge Regression. Logistic Regression makes no assumption about the distribution of the independent variables, which do not have to be normally distributed, linearly related or of equal variance within each group. The purpose of this study was to compare the accuracy of the classifications of group membership of Multiple Regression, Ridge Regression, Logistic Regression and Linear Discrimination Analysis with the mean of classification error, mean of B and mean of C. The results showed that Logistic Regression and Linear Discrimination Analysis performed better than the others.