Yeah. That's what my students do. Love to discuss with then why it's not the way forward, haha

It's a visual check if there is still hidden data in your fit. If the residuals seem to be randomly distributed, it's a good first rough check that your model covers all the available data. Usually you find the residuals moving all in one, and then all in the other direction, looks a bit like a wave-y very noisy motion along the x axis.

There are more robust ways to check like Anderson darling if I'm not wrong, but the residuals are easily plotted and a good quick and dirty first check

True. But even if you use some mix of polynomials it might be still better to argue why, rather than using an exponential here no?

Keep us posted! I really like the article btw!

Yeah i get that, but for me that would be the interesting part. Making a model is just one part, choosing if the model is correct is a whole different story :) And i still think that adjusting an inverse square model might be worth trying, no? The price is based on available space, and this correlates with a square and not an exponential function. Not sure why I feel so strongly about that, sorry:)

I read the article. What did not work? Or why do you think the exp formula in the article did work?

Would have loved to see the residuals of that exponential fit. They look a bit off even by eye? Not sure if this is the proper model since with the areal distance the area accessible to build houses raises with a square, so the simplest model I'd try is to use something with an inverse square law no?

Gonna try that right now, thank you!