Integration of Gaussian equation

In summary, the conversation is about finding the area under a gaussian cone by solving the integral of 2PIArEXP[-(r^2)/(2sigma^2)] dr. One person's supervisor suggests using 2PI[A sigma^2 EXP(-(r^2)/(2 sigma^2)] between 0 and infinity, which results in the answer 2 PI A sigma^2. Another person explains that this is found by substituting u= r^2/(2sigma^2) and finding the integral of 2\pi {A} \sigma\int_{o}^{\infinity}{e^{-u} du}. They also clarify the steps of the substitution method. Eventually, the original person understands and thanks for
  • #1
kekly
6
0
Hi,

Need help desperately!

I am trying to figure out the area under a gaussian cone by finding the integral of


2PIArEXP[-(r^2)/(2sigma^2)] dr

My supervisor thought it is

2PI [A sigma^2 EXP(-(r^2)/(2 sigma^2)] Between 0 and infinity

and he came up with the answer

2 PI A sigma^2

I hope you can understand that!

I don't think that his integration is right to get to the second step there. But I'm not sure why. I don't think sigma will come down like that. Please help me. I haven't done maths like this for a few years and I'm very rusty at it!

Thanks

Kek
 
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  • #2
I think he has taken
2PIArEXP[-(r^2)/(2sigma^2)] dr

-- AI
 
  • #3
To find [itex]2\pi {A} \int_0^{\infinity}{r e^{\frac{-r^2}{2\sigma^2}}dr}[/itex], let [itex] u= \frac{r^2}{2\sigma^2}[/itex]. Then [itex]du= \frac{r}{\sigma^2}[/itex] so [itex]rdr= \sigma^2du[/itex]. When r=0, u= 0, when r= infinity, u= infinity so the integral becomes [itex]2\pi {A} \sigma\int_{o}^{\infinity}{e^{-u} du}[/itex].
That IS [itex]-2\pi {A} \sigma e^{-u}[/itex] evaluated between u=0 and u= infinity:
[itex]2\pi {A} \sigma[/itex].
 
Last edited by a moderator:
  • #4
rdr = sigma^2 du
the final answer is 2*pi*A*sigma^2

-- AI
 
  • #5
I see what he has done now!

Thanks so much. You guys are lifesavers!

Kek
 
  • #6
HallsofIvy said:
To find [itex]2\pi {A} \int_0^{\infinity}{r e^{\frac{-r^2}{2\sigma^2}}dr}[/itex], let [itex] u= \frac{r^2}{2\sigma^2}[/itex]. Then [itex]du= \frac{r}{\sigma^2}[/itex] so [itex]rdr= \sigma^2du[/itex]. When r=0, u= 0, when r= infinity, u= infinity so the integral becomes [itex]2\pi {A} \sigma\int_{o}^{\infinity}{e^{-u} du}[/itex].
That IS [itex]-2\pi {A} \sigma e^{-u}[/itex] evaluated between u=0 and u= infinity:
[itex]2\pi {A} \sigma[/itex].

Thanks for your help. I am still a little confused. As I said I haven't done integration like this for a long time!

I can't see how you substituted [itex]rdr= \sigma^2du[/itex] back into get [itex]2\pi {A} \sigma\int_{o}^{\infinity}{e^{-u} du}[/itex]. I can see that when r=0, u= 0, when r= infinity, u= infinity. That is fine but where does the sigma come from and where does the r go?

Thanks
kek
 
  • #7
kekly,
Look at the substitution hurkyl makes ...
u = r^2/(2*sigma^2)
find du/dr

-- AI
 
  • #8
I understand the substitution that is made and I can find

du/dr = r/sigma^2

That can then be rearranged to

rdr = sigma^2du

How then do I get to this!

[itex]2\pi {A} \sigma\int_{o}^{\infinity}{e^{-u} du}[/itex]


Forgive me I think it is part of the substiution method that I don't understand. I haven't done it for many years.
 
  • #9
Thanks for all your hlep. I have been playing around with it and I understand it now!

It was not remembering how to use the substitution method that was causing me problems!

Thansk again for all your help!

kek
 

1. What is the Gaussian equation and why is it important in integration?

The Gaussian equation, also known as the normal distribution, is a statistical formula that is used to describe the probability distribution of a continuous random variable. It is important in integration because it allows us to calculate the area under the curve of a normal distribution, which is useful in many fields such as economics, physics, and engineering.

2. How do you perform integration using the Gaussian equation?

To perform integration using the Gaussian equation, you can use the standard normal distribution table or a calculator. First, you need to find the z-score of the value you want to integrate. Then, you can use the table or calculator to find the corresponding area under the curve. Finally, you can use the properties of the normal distribution to adjust the result to the desired value.

3. What is the relationship between the Gaussian equation and the central limit theorem?

The central limit theorem states that the sum of a large number of independent and identically distributed random variables will tend towards a normal distribution. The Gaussian equation is a specific form of the normal distribution, and it is often used to describe the distribution of a sample mean. Therefore, the Gaussian equation is closely related to the central limit theorem.

4. Can the Gaussian equation be used for non-normal distributions?

Yes, the Gaussian equation can be used to approximate the area under the curve of non-normal distributions. This is because many real-life distributions, such as the binomial distribution and the exponential distribution, can be approximated by a normal distribution when the sample size is large enough.

5. What are some practical applications of the integration of Gaussian equation?

The integration of Gaussian equation has many practical applications in various fields. It is commonly used in financial analysis to model stock prices and in risk management to calculate the risk of a portfolio. It is also used in quality control to determine if a process is within acceptable limits. Additionally, it is used in physics to describe the distribution of errors in experimental measurements.

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