# Matlab Area Under the Curve

## Introduction:

The area under a curve is a fundamental concept in mathematics and has diverse applications in fields such as science, engineering, finance, and more. Computing the area under a curve in MATLAB is a straightforward yet powerful operation, allowing researchers, engineers, and analysts to quantify the accumulation of values represented by a function.

## What is the Area Under the Curve?

The area under a curve represents the integral of a function over a specified interval. Graphically, it is the region between the function's curve and the x-axis, bounded by two vertical lines. This area signifies the total area or accumulation of values represented by the function over that interval.

• For example, in financial analysis, the area under a revenue curve might represent the total revenue generated over a specific period.
• In scientific experiments, it could represent the total charge, energy, or any other quantity accumulated over time or space.

### Computing Area Under the Curve in MATLAB

MATLAB provides efficient tools to compute the area under a curve using numerical integration methods. The integral function is commonly used for this purpose, allowing users to integrate a function over a specified interval.

Syntax of integral function:

Fun: The function to be integrated.

a and b: The lower and upper limits of integration.

Example: Computing Area Under a Curve

Let's consider a simple example to compute the area under the curve of a function using MATLAB.

Suppose we have a function:

f(x)=x2

We want to find the area under this curve between x=0 and x=2. Here's how we can achieve this in MATLAB:

Output:

In this example, we defined the function f(x) = x^2, specified the limits of integration from 0 to 2, computed the area under the curve, and then displayed the result.

### Plotting the Curve and Area in MATLAB

Visualizing the function and the area under the curve can provide deeper insights. We can use MATLAB to plot both the curve and shaded area for better understanding.

MATLAB Code for Plotting:

Output :

In this plot, the blue curve represents the function f(x)=x2, and the shaded region illustrates the area under the curve from x=0 to x=2. The numerical value of the area, as computed earlier, is 2.672.67.

## Different Methods for Computing Areas Under the Curve

In MATLAB, there are various methods available to compute the area under a curve, each suitable for different scenarios based on the function's complexity, desired accuracy, and efficiency requirements.

Let's explore some of these methods:

### Numerical Integration Methods:

Integral Function: This function is the go-to method for most cases. It uses adaptive quadrature to approximate the integral of a function over a given interval. MATLAB automatically adjusts the step size to achieve the desired accuracy.

Quad Function: Similar to integral, quad performs numerical integration using a variety of quadrature methods. Users can specify the desired tolerance for accuracy.

### Trapezoidal Rule:

The trapezoidal rule is a simple numerical method for approximating the integral of a function. It divides the area under the curve into trapezoids and sums their areas.

### Simpson's Rule:

Simpson's rule provides a more accurate estimate by approximating the function with quadratic polynomials. It is particularly useful for functions with smooth curves.

### Monte Carlo Integration:

This method involves using random sampling to estimate the integral. It is particularly effective for high-dimensional integrals and functions with complex shapes.

### Considerations for Area Computation

When choosing a method for computing the area under a curve, several factors should be considered:

Function Complexity: Some methods, like Simpson's rule, are better suited for functions with smoother curves, while others, like Monte Carlo integration, can handle complex and irregular functions.

Desired Accuracy: The tolerance level for error should be considered. Adaptive quadrature methods like integral automatically adjust the step size to achieve the desired accuracy.

Efficiency: Efficiency becomes crucial for large datasets or computationally intensive functions. Methods like the trapezoidal rule offer simplicity and speed.

## Computing Methods for Area Under Curves

In MATLAB, the integral function stands as a cornerstone for computing the area under curves, offering adaptive quadrature to ensure accurate results. Additionally, MATLAB provides alternative methods, such as the trapezoidal rule and Simpson's rule, catering to varying function complexities and efficiency needs.

### Handling Special Cases with Precision

When encountering singularities or discontinuities, MATLAB offers specialized options for precise computations. For singularities within an interval, users can utilize the Principal Value option in integral. Discontinuous functions are efficiently handled by specifying Waypoints, ensuring accurate area calculations.

### Parametric Curves and Higher-Dimensional Integration

For parametric curves defined by equations x=f(t) and y=g(t), MATLAB allows easy computation of the area they enclose. The Green's Theorem method provides an alternative approach for this computation.

### Managing Error Tolerances and Function Definitions

Setting error tolerances is crucial for precise results in scientific computations. MATLAB facilitates this with options such as AbsTol and RelTol, ensuring the desired level of accuracy. Function handles, and anonymous functions simplify the definition and manipulation of functions, enhancing the ease of computation and analysis.

### Statistical Applications: Probability Density Functions

In the realm of statistics, MATLAB's capabilities extend to computing probabilities using the area under probability density functions. This functionality enables researchers and analysts to explore and quantify probabilities with ease, adding another layer of versatility to MATLAB's arsenal.

MATLAB provides numerous visualization options to enhance the representation of the area under a curve:

• Customizing Plot Appearance: Users can customize plot colors, line styles, markers, and annotations to create clear and appealing visualizations.
• Overlaying Multiple Curves: When comparing multiple functions or scenarios, overlaying curves, and shaded areas on the same plot helps make the comparison clear.
• 3D Surface Plots: For functions of two variables, 3D surface plots can visually represent the area under the curve as a surface above the x-y plane.
• Contour Plots: Contour plots can display the area under the curve as contour lines, providing a different perspective on the function's behavior.

### Example: Using Multiple Methods for Area Computation

Let's consider an example where we compute the area under the curve of the function f(x)=sin(x) over the interval 00 to ?. We will use different methods to compare results.

Output:

In this example, we used three different methods (integral, traps, and Simpson) to compute the area under the curve of f(x)=sin(x) over the interval 00 to ?. All methods yield the same result of 2.00002.0000, demonstrating their accuracy.

• Computing the area under a curve is a valuable technique in MATLAB, aiding in various fields of analysis and research.
• Whether analyzing financial data, modeling physical systems, or understanding experimental results, MATLAB's integral function provides a simple yet powerful method to quantify these accumulations.
• MATLAB provides a versatile and powerful environment for computing and visualizing the area under a curve.
• By understanding the various numerical integration methods available, users can choose the most suitable approach based on their specific requirements.

Whether for scientific research, engineering analysis, financial modeling, or educational purposes, MATLAB's capabilities in computing the area under a curve remain indispensable.