My question is a bit naive. I'm trying to get the exact & clear meaning of the phrase "predictor variables are fixed and not random in linear regression".
According to my understanding, fixed means that we pre-set values of predictors in an experiment and then observe y for those values; whereas random means that while conducting an experiment we first measure the predictor variables and then measure the y (we can do this at different time points to get different Xs and Ys or a dataset).
However even with this understanding of mine (which I am not sure is right), I am not able to understand clearly why do we require the predictive variables to be fixed and not random. What would go wrong if we observe or measure the Xs instead of keeping them pre-set?
TLDR: What is the meaning of fixed X and also the need to keep them fixed?