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diffraction-limited t1_jdqhhfy wrote

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?

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sudu1988 OP t1_jdqleo1 wrote

Yeah, I've tried and inverse square law didn't word well. On the original article you can find more info https://damovs.com/rental-market-of-apartments-in-paris-in-2023/

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diffraction-limited t1_jdqlikk wrote

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

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sudu1988 OP t1_jdqlt0v wrote

Well, the results looked much more off. But you are right, I should add some information on the mean squared error. In my article I didn't wanted to shock people with it, because it's kind of more informative post.

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diffraction-limited t1_jdqm5ej wrote

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:)

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sudu1988 OP t1_jdqmwis wrote

No worries. Will give it another try later. It's maybe really worth it.

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Jolly_Scholar7367 t1_jdt0rnh wrote

Very cool, if you can expand this to other cities that would be very interesting

Also, if you could plot this against the mass of garbage outside each, that would be great /s

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Jatzy_AME t1_jdrc5in wrote

Price probably falls off faster than inverse square. The available space grows quadratically, but as you get further you lose more prestige, proximity with important landmarks etc.

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diffraction-limited t1_jdsegyp wrote

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?

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Jatzy_AME t1_jdseszr wrote

Sure! Deciding which model to use a posteriori based on the shape of the data is definitely not ideal.

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diffraction-limited t1_jdsfqv5 wrote

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

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rackelhuhn t1_jdrttnl wrote

With this much data I would just fit a flexible regression like LOESS

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exipolar t1_jdrwf45 wrote

Anyway to fit it to a Zipf curve?

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sudu1988 OP t1_jdrwxed wrote

I will try to redo the plot with the suggestions posted here.

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dontlookwonderwall t1_jdsdu0n wrote

Eyeballing it, it looks like there might be a lot of heteroskedasticity too, especially with one bed apartments which seem to be a bit all over the place, which probably affects the predictory power of the regression.

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MarchelloO t1_jdrtj2k wrote

Hi. Why the residuals of that exponential fit is important to know ?

I am learning Data Analysis so I would like to know to improve myself. Thanks in advance for the answer .

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diffraction-limited t1_jdsez93 wrote

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

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