Calculate Area Under Curve: What Function?

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To calculate the area under a curve when only x and y data points are available, several methods can be employed. First, interpolation between the data points is necessary. Options include using stair-step methods (rectangles), trapezoidal approximations (straight lines between points), or fitting a polynomial or spline curve to the data. The trapezoidal rule provides a straightforward approach, where the area between two points is approximated as A = (x_{j+1} - x_{j}) * (y_{j+1} + y_{j}) / 2. For regularly spaced x values, Simpson's Rule can offer improved accuracy through higher-order interpolation. Visualizing the data is recommended to determine the most suitable method for achieving the desired accuracy.
henil
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i want to calculate area under the curve but i do no not know what function does it satisfies. how should i proceed ?
 
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I'm not sure what you are asking about. Do you have a known function f(x) which you want to integrate, and wonder how to do it numerically?
 
no i don't know f(x) i just have x and y datapoints
 
Then you need to decide how to interpolate between the data points. Some possibilities:
  • Use the points to define "stair-steps" (a series of rectangles)
  • Construct straight lines between the points (a series of trapezoids)
  • Do a least-squares fit to a suitable type of function (polynomial, exponential, etc.)
  • Construct a spline curve that passes through all the points exactly.
 
The best you can do is:
  1. Sort the data points in ascending order in the xn
  2. The best approximation for the area between (xj, yj) and (xj+1, yj+1) is given by A_{j}=(x_{j+1}-x_{j})\cdot\frac{y_{j+1}+y_{j}}{2} (this is called the trapezoid rule)
  3. Add up these areas.
 
henil said:
no i don't know f(x) i just have x and y datapoints
If the x values of the data points occur at regular intervals, you can use a numerical integration technique like Simpson's Rule to perform the calculations. Simpson's Rules are based on using second- and third-order interpolation functions, which is usually more accurate than the simpler trapezoidal rule.

You really should plot your data to see which method would give you the accuracy of result you desire.
 
okay i will try all your suggestions.
thank you for your help.
 
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