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Here is the context of my problem: I have data samples from measurements. I'm reccording N properties. From those data I would like to perform a sensitivity analysis to focus my study on significant parameters. If I use the sobol method for example, I need to transform my data into a "model" to manipulate it (estimation from regression).

As a result, here is my question: do you know a method (or a toolbox if it exists) to perform a N-dimensions polynomial/spline regression (not linear), in Python language?

I heard something about a catmull-rom method, but I don't understand how to apply it.

Thank you for your help!

lelorrain7
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  • Possible duplicate. Look into this: https://stackoverflow.com/questions/31406975/polynomial-regression-using-python – Philippe Oger Jan 26 '18 at 13:53
  • I am afraid, this question is off topic for SO. [Please see here under #4.](https://stackoverflow.com/help/on-topic) Having said that, explore scipy.interpolate and numpy.polyfit. – Mr. T Jan 26 '18 at 13:57
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    It's not a duplicate because the mentionned topic is only concerning 2 dimensions, not N. – lelorrain7 Jan 26 '18 at 14:02
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    And it's not an issue under #4 because scipy.interpolate is doing linear after 2D, and I don't want linear. => It must be understood "in python, how to switch from 2D to ND". – lelorrain7 Jan 26 '18 at 14:05

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