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I have implemented a beta regression and am a little confused on how I should interpret the coefficients of my model. For context, both my independent variables and dependent variable are expressed in percentage form, ranging from [0, 1]. The only exception is one independent variable which takes the binary value of 0 or 1. Also, I used a logit link. Does anybody mind sharing how I could interpret the coefficients here in this beta regression? I've never worked with a dataset like this before; any help would be appreciated!

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You interpret a logit-link beta regression output in a same way that you would interpret a logit-link logistic regression. We are modelling the expectation of the Beta-distributed Random Variable $Y$, via a logit link. Your model is something like:

$$ \text{logit} ( E[Y] ) = b_0 + b_1x_1 + b_2x_2 + \cdots $$

Let's say that $x_1$ is your $[0,1]$ proportional predictor, and $x_2$ is your binary predictor.

Interpretation of $x_1$: it's probably not useful to do the usual "an increase in $x_1$ by 1 results in a ..." interpretation. You may want to say something like "and increase in $x_1$ by 1 percentage point (i.e. 0.01) results in an increase in the odds by a factor of $\exp(0.01 \times b_1)$."

Interpretation of $x_2$: basically a standard interpretation: "$x_2 = 1$ (or whatever the class label is) corresponds to an increase in the odds by a factor of $\exp(b_2)$".

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