The error function is a mathematical function used in statistics and physics to represent the cumulative distribution function of a normal distribution. It is defined as:
erf(x) = (2/√π)∫e^(-t^2)dt from 0 to x
To prove this function, we can use the definition of the error function and the properties of integrals.
First, we can rewrite the integral as:
erf(x) = (2/√π)∫e^(-t^2)dt from -∞ to x
Next, we can use the substitution u = -t^2 and du = -2tdt to rewrite the integral as:
erf(x) = (-1/√π)∫e^u du from -∞ to -x^2
Using the fundamental theorem of calculus, we can evaluate the integral to get:
erf(x) = (-1/√π)(e^-x^2 - e^-∞)
Since e^-∞ is equal to 0, we can simplify the equation to:
erf(x) = (2/√π)e^-x^2
This is the same equation as the one given in the definition of the error function. Therefore, we have proven the error function.