Integral of a normal distribution

In summary, the conversation discusses finding the integral from -∞ to +∞ of e-x2dx. The student suggests using a double integral in reverse to solve for the integral, transforming it into polar coordinates, and then questioning why the boundaries cannot be r from -∞ to +∞ and θ from 0 to π. The reason for this, as pointed out by the student, is that the "r" in rdrdθ comes from the absolute value of the Jacobian determinant, resulting in the correct answer of zero when using these boundaries. The student also mentions using latex code when writing equations and decides to post their thoughts regardless.
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Homework Statement


(Scroll to bottom for the true question)

Suppose we are to find the integral from -∞ to +∞ of (let’s just say) e-x2dx

Homework Equations


∫∫f(x)g(y)dxdy = (∫f(x)dx)(∫g(y)dy)

The Attempt at a Solution


We can square the integral we want to solve for then use my relevant equation (in reverse) to write the answer to the integral as the square root of this double integral:
∫∫e-(x2+y2)dxdy

Where the x and y boundaries are ±∞

Now we transform into polar coordinates so that the integral becomes:

∫∫e-r2(rdrdθ)

Now we could make the (inner) r integral run from 0 to ∞ and the (outer) θ integral run from 0 to 2π (since these boundaries cover the whole plane) which would give the famous answer.Now my question is about the boundaries of the polar integral... why can’t we have made r run from -∞ to +∞ and θ run from 0 to π? This also seems to cover the plane, but doing this gives an answer of zero. What is the reason this is wrong?

Thanks.

Well actually I just realized the “r” in rdrdθ comes from the absolute value of the ”Jacobian determinant” so it should actually be |r| dr dθ which does give the correct answer.

I’m going to post this anyway since I already typed it out. Maybe someone has more insight to share.
 
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Use latex code when writing equations.
 

1. What is the formula for the integral of a normal distribution?

The formula for the integral of a normal distribution is ∫(1/(√(2πσ²))e-(x-μ)2/(2σ²) dx, where σ is the standard deviation and μ is the mean.

2. How is the integral of a normal distribution related to the probability density function?

The integral of a normal distribution represents the area under the curve of the probability density function, which is a measure of the likelihood of a continuous random variable falling within a certain range of values.

3. Can the integral of a normal distribution be solved analytically?

Yes, the integral of a normal distribution can be solved analytically using the cumulative distribution function, which is a standard mathematical function. However, for practical purposes, it is often calculated using numerical methods.

4. What is the significance of the integral of a normal distribution?

The integral of a normal distribution is important in statistics and probability as it allows us to calculate the probability of a random variable falling within a certain range of values. It also helps in determining the mean, standard deviation, and other characteristics of a normal distribution.

5. How is the integral of a normal distribution used in hypothesis testing?

In hypothesis testing, the integral of a normal distribution is used to calculate the p-value, which is the probability of obtaining a test statistic at least as extreme as the one observed, assuming the null hypothesis is true. The p-value is then compared to a predetermined significance level to determine the validity of the hypothesis.

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