Comparison Between Iterative Maximum Likelihood Estimators Method And Bayesian Method For Estimating Logistic Regression Model Parameters With Practical Application

Abstract

There are many conditions for using regression in general, sometimes the conditions of using regression are not fulfilled in this case we should find alternative methods of data analysis so that we can predict the phenomenon studied and the most important of these methods is the logistic regression method where logistic regression is one of the methods commonly used, especially in binary data, where we used in this research, two methods to estimate the parameters of the logistic regression model, a method iterative maximum Likelihood Estimators and Bayesian Method We used two criteria for comparison, which is the Mean Square Error, and the mean Absolute Percentage error. Real data was used in this research, which is represented by lung cancer with a sample size (30) taken from the City of Medicine Hospital / Cancer Hospital where the results showed that the method Bayesian the best by Comparison criteria in estimating logistic regression model parameters.