regression and Bayesian ridge regression models

Abstract

The task of selecting variables is important to build a model that leads to accurate predictions, especially when the data under study suffer from the problem of linear multiplicity, and the presence of many phenomena in our daily life, especially the social suffer from this problem, In order to compare the methods of estimating the parameters of the regression model of the biased ridge regression and the Bayesian ridge regression. As a comparison of the two methods through the mean squares error MSE and the probability value P-Value. In order to apply the methods in practice, a random sample of 100 women's fertility was withdrawn to study the factors affecting the number of children born as dependent variable and many independent random variables is  (woman age , the age at married , graduate of woman ,  graduate of husband , women's weight, women's use of contraception, women's smoking, Age of Husband, job of husband, Marriage Period, Number of Dead Children, Hours of Exercise per Week, hours of female sleep per day, women taking drugs, duration of breast feeding, mother's job), data were suffering from a linear multiplicity problem, It was found that the Bayesian regression method has the best estimation methods for owning the MSE .