The use of the Biz method and classical methods in estimating the parameters of the binary logistic regression model


Binary logistic regression model used in data classification and it is the strongest most flexible tool in study cases variable response binary when compared to linear regression. In this research, some classic methods were used to estimate parameters binary logistic regression model, included the maximum likelihood method , minimum chi-square method , weighted least squares , with bayes estimation , to choose the best method of estimation by default values to estimate parameters according two different models of general linear regression models ,and different sample sizes ,and building an experiment simulation experience then displaying the results and the analysis using the statistical criteria Mean Squares Error (MSE),to choose the best standard methods for estimators the binary logistic regression model. Generally, The method was found to be the best one among the standard estimation methods, for the purpose of estimating the parameters for binary logistic regression model because it has the less (MSE) for estimators compared to other methods, which indicates the accuracy of the method in estimating the parameters of the model.