In a simple model,

Therefore, I have decided to use beta regression with boundaries from 0 to 1 (i used

Output for linear regression model:

Output for beta regression model:

How can I interpret the parameter

Thank you! M.

*is a continuous (normally distributed) variable predicting***x***. Since***y***values are proportions ranging from 0 to 1 (0%-100%), simple linear regression may give out-of-bounds estimates for some predicted values (i.e., lower than 1 or higher than 1).***y**Therefore, I have decided to use beta regression with boundaries from 0 to 1 (i used

*command in betareg R package; the software is however not important). While it is easy to interpret the unstandardized regression parameter from a linear model (see below linear model output: B = 0.126 indicating an increase by 12.6% of y if x rises by 1), I am not sure how to understand, transform, or use the parameters from betareg model to get a meaningful interpretation of the coef (see below - Beta regression output).***betareg()**Output for linear regression model:

*lmMod = lm(formula = y ~ x)*
Code:

```
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.57936 0.10849 -5.340 9.57e-07 ***
x 0.12591 0.01354 9.296 4.07e-14 ***
```

*betaMod = betareg(formula = y ~ x)*
Code:

```
Coefficients (mean model with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.85712 0.52580 -9.238 <2e-16 ***
[B]x[/B] 0.56796 0.06498 8.740 <2e-16 ***
Phi coefficients (precision model with identity link):
Estimate Std. Error z value Pr(>|z|)
(phi) 7.686 1.184 6.491 8.54e-11 ***
```

**in the beta regression output (together with the intercept)? Is there a way how to use***0.567***and get the***0.567***(i.e., if x increases by 1, y increases by XX, since y is in %, the interpretation is easy).***increase of the absolute value in y*Thank you! M.

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