Beta regression
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Beta regression is a form of regression which is used when the response variable, y , takes values within (0, 1) and can be assumed to follow a beta distribution. It is generalisable to variables which takes values in the arbitrary open interval (a, b) through transformations. Beta regression was developed in the early 2000s by two sets of statisticians: Kieschnick and McCullough in 2003 and Ferrari and Cribari-Neto in 2004.
Beta regression is a form of regression which is used when the response variable, y , takes values within (0, 1) and can be assumed to follow a beta distribution. It is generalisable to variables which takes values in the arbitrary open interval (a, b) through transformations. Beta regression was developed in the early 2000s by two sets of statisticians: Kieschnick and McCullough in 2003 and Ferrari and Cribari-Neto in 2004. g(\mu_i) = x_i^T\beta_i = \eta_i, where g is a link function. It is also notable that the variance of y is dependent on \mu in the model, so beta regressions are naturally heteroscedastic.
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