Hello, thank you very much for the very good and helpful post. I have a
question regarding quasi-binomial regression / fractional logistic regression:

I have a dataset on the proportion of inner urban development at the
level of municipalities (values from 0 to 1).

Now I want to estimate the proportion of inner urban development using
other independent variables. Since the dependent variable can only have
values from 0 to 1, quasi-binomial regression/ fractional logistic regression is a good choice -
according to your post.

In your post you explain how to estimate the marginal effects of more
than one independent variable by holding the other independent variables
constant. In your example you do this for different catecorial variables.
This is very helpful. However, I need help on how to calculate the marginal effects when you have four or
maybe more continuous independent variables (for example degree of
urbanization [values from 0 to 1], urban dispersion [index with
values from 0 to 100], population growth [percentage change],
building density [dwelling per built-up area]).

Can you give me advise how to perform quasi-binomial regression / fractional logistic regression and to calculate marginal effects in this case?

Best regards
Sebastian