Density function for a normal distribution

In summary, the conversation revolves around proving the integral of e^(-x^2/2) from -∞ to +∞ is equal to √(2π), and the method of doing so using multiple integrals.
  • #1
Calpalned
297
6

Homework Statement


I have to prove that ## \int e^{\frac{x^2}{-2}}dx ## from +∞ to -∞ = ##\sqrt{2\pi} ##

Homework Equations


N/A

The Attempt at a Solution


My GSI went from
1) ## \int e^{\frac{x^2}{-2}}dx ## from +∞ to -∞ = ##\sqrt{2\pi} ##
to
2) ## (\int e^{\frac{x^2}{-2}}dx)(\int e^{\frac{y^2}{-2}}dy) ## is equal to ## 2\pi ##
Where did the red part of the function come from?
This leads to another question, how do we convert a single integral to multiple integrals? Thank you.
 
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  • #2
If you start with this:
[tex]
I = \int_{-\infty}^\infty e^{-\frac{x^2} 2} \, dx
[/tex]
you can also write it as
[tex]
I = \int_{-\infty}^\infty e^{-\frac{y^2} 2} \, dy
[/tex]
since the variable of integration is immaterial. Multiplying I with itself gives
[tex]
I^2 = \left(\int_{-\infty}^\infty e^{-\frac{x^2} 2} \, dx\right) \left(\int_{-\infty}^\infty e^{-\frac{y^2} 2} \, dy\right)
[/tex]

This product of two integrals can be written as a double integral. If you can do that, and then show that [itex] I^2 = 2 \pi [/itex], you will
essentially be done, no?
 
  • #3
Calpalned said:

Homework Statement


I have to prove that ## \int e^{\frac{x^2}{-2}}dx ## from +∞ to -∞ = ##\sqrt{2\pi} ##

Homework Equations


N/A

The Attempt at a Solution


My GSI went from
1) ## \int e^{\frac{x^2}{-2}}dx ## from +∞ to -∞ = ##\sqrt{2\pi} ##
to
2) ## (\int e^{\frac{x^2}{-2}}dx)(\int e^{\frac{y^2}{-2}}dy) ## is equal to ## 2\pi ##
Where did the red part of the function come from?
This leads to another question, how do we convert a single integral to multiple integrals? Thank you.

Google is your friend. See, eg.,
http://mathworld.wolfram.com/GaussianIntegral.html or
http://en.wikipedia.org/wiki/Gaussian_integral
 

What is a density function for a normal distribution?

A density function for a normal distribution is a mathematical function that describes the probability distribution of a continuous random variable. It is also known as a probability density function (PDF) and is commonly used in statistics and probability theory.

What does the density function for a normal distribution look like?

The density function for a normal distribution is bell-shaped and symmetrical around the mean. It has a single peak at the mean and tapers off towards both ends of the distribution. The shape of the curve is determined by two parameters - the mean and the standard deviation.

How is the density function for a normal distribution calculated?

The density function for a normal distribution is calculated using the formula f(x) = (1/σ√(2π)) * e^(-(x-μ)^2 / (2σ^2)), where μ is the mean and σ is the standard deviation. This formula is used to determine the probability of a given value occurring within the distribution.

What are the properties of a density function for a normal distribution?

The density function for a normal distribution has several properties, including symmetry, unimodality, and the total area under the curve being equal to 1. It also follows the 68-95-99.7 rule, where approximately 68% of the values fall within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations.

When is the density function for a normal distribution used?

The density function for a normal distribution is used in a variety of fields, including social sciences, natural sciences, and engineering. It is commonly used to model natural phenomena such as heights, weights, and test scores, and is also used in statistical analyses and hypothesis testing.

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