Finding best fit for a parabolic segment

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Discussion Overview

The discussion revolves around finding the best fit for a set of points that appear to define a parabolic segment. Participants explore various mathematical approaches to determine the fitting function, including polynomial and power functions, while considering the nature of the data and its representation.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant presents a set of points and asks how to find the scale and origin of a fitting parabola.
  • Another participant questions whether the points must represent a parabola and suggests using a scatter plot to analyze the data visually.
  • A proposed general form for conic sections is shared, indicating a method to derive coefficients from selected points.
  • One participant provides a polynomial equation that fits the data but also mentions a linear approximation as a potential fit.
  • Another participant, after analyzing a larger dataset, expresses doubt that the points represent a parabola, suggesting they may follow a different functional form, specifically y = x^(1/m) with m around pi/2.
  • Concerns are raised about the concavity of the functions being considered, with discussions on how different values of m affect the curvature.
  • Participants discuss the tools used for graphing and fitting the data, including Graphmatica and Mathematica, while also mentioning the challenge of having an infinite number of points without a generating function.

Areas of Agreement / Disagreement

There is no consensus on whether the set of points must represent a parabola, as some participants suggest alternative functional forms. The discussion remains unresolved regarding the best fitting function and the nature of the data.

Contextual Notes

Participants express uncertainty about the assumptions underlying their analyses, particularly regarding the shape of the data and the appropriateness of different fitting functions. The discussion includes references to specific mathematical forms and their implications for the data's representation.

ktoz
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I have a set of points

x y
0 0
1 1
2 3
3 4
4 6
5 8
6 9
7 11
8 14
9 16
10 18
11 21
12 24
13 27
14 30

That seem to define, fairly closely, a parabolic segment. How do I find the scale and x/y origin of the parabola that most closely fits these points?
 
Last edited:
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Must the set of points be a parabola? What does the scatter plot of the points look like?

You might try picking a few points to use for a set of equations based on the general form for conic sections; solve the set of equations for the coefficients and make your best judgement. the relationship of the coefficient values determine what kind of conic section the chosen set of points represent. You might want to try the points which you believe are the most important for your system. Some kind of matrix or linear algebra/curve fitting software would make the task efficient.

Your general form would be like:
[tex]\[<br /> Ax^2 + By^2 + Cxy + Dx + Ey + F = 0<br /> \][/tex]
 
[tex]y = 1.1\cdot10^{-8}x^7 + 2.5\cdot10^{-7}x^6 + 3.2\cdot10^{-6}x^5 + 3.1\cdot10^{-5}x^4 + 0.00063x^3 + 0.036x^2 + 1.34x - 0.0981[/tex] although y=1.8x isn't bad either.
 
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symbolipoint said:
Must the set of points be a parabola? What does the scatter plot of the points look like?

After working with a larger data set, I don't think it is a parabola. It looks very similar to one when I manually overlay a true parabola in Adobe Illustrator but it can't be made to fit exactly. It's doesn't appear to be an asymptote either.

Your general form would be like:
[tex]\[<br /> Ax^2 + By^2 + Cxy + Dx + Ey + F = 0<br /> \][/tex]

When calculated a different way, the points seem to follow

y = x^(1/m)

Where m is in the neighborhood of pi/2
 
?

The results show a concavity towards the y axis. A function such as [tex]x^{1/m}[/tex] with m > 1 gives a concavity towards the x axis.

PS: Could you PM me the datasheet?
 
Gib Z said:
[tex]y = 1.1\cdot10^{-8}x^7 + 2.5\cdot10^{-7}x^6 + 3.2\cdot10^{-6}x^5 + 3.1\cdot10^{-5}x^4 + 0.00063x^3 + 0.036x^2 + 1.34x - 0.0981[/tex] although y=1.8x isn't bad either.

Thanks Gib.

Did you derive that with Mathematica? I only posted a few data points because there are an infinite number :)

When I calculate them a different way, it appears to be a simple power function with an exponent of 1/m. I tried i/phi which diverges too quickly and 1/(pi/2) which is better but still diverges a little too quickly.
 
I used Graphmatica. How do you have an infinite number of points without some sort of generating function...however you do it, send it over.
 
Werg22 said:
?

The results show a concavity towards the y axis. A function such as [tex]x^{1/m}[/tex] with m > 1 gives a concavity towards the x axis.

The original data set was a bit contrived to get it to oriented it that way.

PS: Could you PM me the datasheet?

Will do. Thanks for taking a look.
 
Hey ktoz, I'll take a look another time, it's time for me to go to bed... it's 6:19 AM here. :zzz:
 
  • #10
Holy whack...Worst I've ever done was 5:30 :)
 

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