The analysis of data assuming values in the real open interval (0;1) is a common issue in quantitative research when the effect of selected variables on the conditional expectation of a percentage or rate is considered. In the literature, various alternative methods to model ratios and percentage data have been proposed (see e.g. Papke and Wooldridge, 1996 and Kieschnick and McCullough, 2003). ...

Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta reg...

BACKGROUND AND PURPOSE Ischemic stroke lesion volumes have proven difficult to analyze due to the extremely skewed shape of their underlying distribution. We introduce an extension of generalized linear models, beta regression, as a possible method of modeling extremely skewed distributions as evidenced in ischemic stroke lesion volumes. METHODS The NINDS rt-PA clinical trials measured ischem...

This paper proposes a regression model where the response is beta distributed using a parameterization of the beta law that is indexed by mean and dispersion parameters. The proposed model is useful for situations where the variable of interest is continuous and restricted to the interval (0, 1) and is related to other variables through a regression structure. The regression parameters of the b...