Calculate Area Under Curve: What Function?

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SUMMARY

To calculate the area under a curve when only x and y data points are available, several numerical integration techniques can be employed. The trapezoidal rule provides a straightforward method by approximating the area between points as trapezoids, while Simpson's Rule offers greater accuracy through second- and third-order interpolation. Additionally, constructing spline curves or performing least-squares fitting can yield more precise results depending on the data characteristics. It is essential to visualize the data to determine the most suitable method for achieving the desired accuracy.

PREREQUISITES
  • Understanding of numerical integration techniques
  • Familiarity with the trapezoidal rule
  • Knowledge of Simpson's Rule
  • Basic skills in data visualization
NEXT STEPS
  • Research the implementation of the trapezoidal rule in Python using NumPy
  • Learn about Simpson's Rule and its application in numerical analysis
  • Explore spline interpolation methods for curve fitting
  • Investigate data visualization tools such as Matplotlib for plotting data points
USEFUL FOR

Data analysts, statisticians, and anyone involved in numerical analysis or data visualization who needs to calculate the area under a curve from discrete data points.

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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