# Interpreting coefficients of beta regression

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!

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 $$\exp(b_2)$$".