I am working on a housing problem in which I use dichotomous and ratio data to predict housing production (units constructed in a year-ratio) in a 17 year time period. At this time, I am using OLS and as I get better at stats, I shall attempt this problem using time-series analysis. That said, I have used R to standardize all of my ratio predicting data and left the dichotomous data raw. And I have also transformed the response variable to a Natural log to normalize the distribution (i.e. many, many zeros>>yes, I know Poisson or Zero-populated counts in the future).
I have read the post on "interpret coefficients from a quantile regression on standardized data" and also the "convert my unstandardized independent variables to standardized." Based on those, I think that can do the following interpretation based on the following output. The variable
region_id is dichotomous,
supply is standardized.
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.687e+00 2.171e-01 12.379 < 2e-16 *** region_id 1.805e+00 1.383e-01 13.049 < 2e-16 *** supply -2.205e+01 2.204e+00 -10.005 < 2e-16 ***
For every on city that is located in the Houston region, you can expect that annual housing production will increase by 1.8%.
For every one-unit increase in the standard deviation of housing supply, you can expect that annual housing production will decrease by -22.05%.
I am not a stats or math person at all, but I have been using R for the past three years and I am quite familiar with OLS, but if you throw up an equation it will look "appropriately" Greek to me. :)