Beta regression is a regression model for a variable of interest that is continuous and restricted to the interval $(0,1)$. The dependent variable is beta distributed, therefore it is suitable for predicting and analysing probability, proportions or odd ratios. Data transformation enables the use of the model also for variables with values in interval $[a, b]$, where $a < b$. With beta regression we avoid the restrictions of linear regression like homoscedasticity and symmetry. Here we study the beta regression model, maximum likelihood estimation for regression parameters, and Fisher information matrix. We cover hypothesis testing, confidence intervals and different diagnostic measures in order to check the goodness-of-fit of the estimated model. We apply beta regression to real-life data and give a detailed description of the analysis steps in RStudio.
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