Use of information criteria and detection model methods to select the best linear regression model with application on thalassemia children in Mosul

In this paper we compute Akaike's Information Criteria (AIC), Bayesian Information Criteria (BIC) and Schwarz Bayesian Criteria (SBC) for all possible for independent variables. The three criteria are measurement of the difference between a given model and the real model, and the model with smallest (AIC), (BIC) and (SBC) compared with other models is the best one. This study is applied to data gathered from children infected with Thelasymia disease, and the data are analyzed by using SAS to evaluate which of the models is the best model to determine the best subset of variables that minimize the information criteria among all the variables in the study. We compare the three criteria with model diagnostic like root mean squared error (RMSE), Mallow's and adjusted .