Multicollinearity in Logistic Regression Model -Subject Review-

The logistic regression model is one of the modern statistical methods developed to predict the set of quantitative variables (nominal or monotonous), and it is considered as an alternative test for the simple and multiple linear regression equation as well as it is subject to the model concepts in terms of the possibility of testing the effect of the overall pattern of the group of independent variables on the dependent variable and in terms of its use For concepts of standard matching criteria, and in some cases there is a correlation between the explanatory variables which leads to contrast variation and this problem is called the problem of Multicollinearity. This research included an article review to estimate the parameters of the logistic regression model in several biased ways to reduce the problem of multicollinearity between the variables. These methods were compared through the use of the mean square error (MSE) standard. The methods presented in the research have been applied to Monte Carlo simulation data to evaluate the performance of the methods and compare them, as well as the application to real data and the simulation results and the real application that the logistic ridge estimator is the best of other method.