I don't do statistics in my day job, but having a solid foundation in the math and the theory and the proofs really had a profound impact on how I approach problem solving generally and root cause analysis specifically (just thinking about e.g. model specification and how you do it is a good exercise). It's still on my bookshelf and I'll pull it out and do a problem set from time to time just to stay sharp - even though, again, I don't do this for my job. Maybe, one day, I'll get a job with a baseball team or something. Understanding heteroskedasticity for example is one of those things where once you "get it", it really opens your eyes to a lot of things and you can't "unsee" it.
PS there are packages in R nowadays but back in the day we used STATA.
PPS if people don't like Wooldridge, what else would you recommend?
Edit: I know you said "Your choices needn't be only math books" but this is a statistics textbook, so it probably doesn't count as "math".
I don't do statistics in my day job, but having a solid foundation in the math and the theory and the proofs really had a profound impact on how I approach problem solving generally and root cause analysis specifically (just thinking about e.g. model specification and how you do it is a good exercise). It's still on my bookshelf and I'll pull it out and do a problem set from time to time just to stay sharp - even though, again, I don't do this for my job. Maybe, one day, I'll get a job with a baseball team or something. Understanding heteroskedasticity for example is one of those things where once you "get it", it really opens your eyes to a lot of things and you can't "unsee" it.
PS there are packages in R nowadays but back in the day we used STATA.
PPS if people don't like Wooldridge, what else would you recommend?
Edit: I know you said "Your choices needn't be only math books" but this is a statistics textbook, so it probably doesn't count as "math".