5. \( y=e^{-x^{2}}, \quad y=0, \quad x=0, \quad x=1 \)
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Extra Insights
The function \( y = e^{-x^2} \) is known as the Gaussian function and plays a crucial role in statistics, particularly in normal distribution. The area under the curve from \( x = 0 \) to \( x = 1 \) can be computed using definite integrals. This area represents the probability of a random variable following a normal distribution being between these two points, and it's approximately 0.3413 when calculated. When evaluating the definite integral to find the area under this curve, many make the mistake of trying to find a simple antiderivative. In reality, the integral cannot be expressed in elementary terms, requiring numerical methods or special functions like the error function (\( \text{erf} \)). So, don't worry if you can't find the antiderivative directly—numerical approximation techniques can be your best buddies!
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