Often in many applications, we are interested in examining the impact and relationship of different factors on a variable that is expressed as a ratio and percentage, such as disease rate, percentage of a disease in a particular area, unemployment ratio and inflation rate. To aim it, a appropiate regression model is chosen according to the type of response variable. When the response variable is in form of percentage or ratio, or even defined in the range like (a, b), the beta regression model is proposed. Beta regression model belongs to the family of generalized linear models. In this thesis, first the beta regression model by considering different models on the precision parameter are introduced and parameters of the model have been estimated in classical framework. Then we consider bayseian estimation of parameters. We show performance of the bayesian estimation of the parameters by using a Monte Carlo simulations and applying the bayesian model of beta regression to real data set will be the final application of this research.
