Integrating a set of data points

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SUMMARY

The discussion centers on calculating the area under a curve formed by a set of data points using Maple's PolynomialInterpolation() function. The user encounters inaccuracies when integrating the resulting polynomial over the specified interval, leading to implausibly large area estimates. Solutions proposed include using the trapezoidal rule or Simpson's rule for equally spaced points, or opting for spline interpolation in Maple for more accurate results.

PREREQUISITES
  • Familiarity with Maple software and its PolynomialInterpolation() function
  • Understanding of numerical integration techniques such as the trapezoidal rule and Simpson's rule
  • Knowledge of spline interpolation methods
  • Basic concepts of curve fitting and area under the curve calculations
NEXT STEPS
  • Research the implementation of the trapezoidal rule in Maple
  • Explore Simpson's rule for numerical integration in Maple
  • Learn about spline interpolation techniques in Maple
  • Investigate the limitations of polynomial interpolation for data fitting
USEFUL FOR

Data analysts, mathematicians, and engineers who need to accurately calculate areas under curves from discrete data points using numerical methods in Maple.

Mu naught
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Homework Statement



I have a set of data points, and I need to find the area under the curve they form. I am trying to use Maple for this, by using the PolynomialInterpolation() to fit a curve to my list of points. I then would just integrate this function over the appropriate interval.

The problem is it that although the function it spits out gives me accurate range when I type in values manually, when I try to integrate it over the correct interval it spits out a huge number which is vastly larger than the area under the curve could be.

So now I don't know how to do this... is there another way to get the area?


The Attempt at a Solution

 
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An interpolating polynomial will be exact at the interpolating points but may fluctuate wildly between them consequently not being at all appropriate. If the points are equally spaced on the x-axis you might try the trapezoidal formula or Simpson's rule. Alternatively you could let Maple use a spline interpolation and work out the integral both.
 

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