Various variable types in regression

Question

so I have data containing various variable types and I would like to fit a regression model. However as the types of variables are different, I do not know how to deal with it: I mean, there are positive whole numbers (some quantity), real numbers, and also percentages (or some ratios). Now when I fit the regression (lets stick to linear regression case which is simple and can be then generalized to sth more complex) how do I interpret the regression coefficients, when they are of different types? Some wise transformation could help with that, but there are different rules for modelling real, categorical and compositional data. Thanks for ANY idea and discussion.

Answer 1

You don't need to do anything, at least as long as the variables as encoded as numerical values, i.e. binary variables need to be coded as 0 and 1, or -1 and +1, categorical variables need to be dummy encoded, for things like dates you need to come up with some meaningful coding (e.g. number of days since some fixed date), etc. Regression will handle variables that have different magnitudes by making the regressions coefficients higher, or lower appropriately.