Need help solving CDF (Cumulative distribution function)

In summary, the conversation is about solving the cumulative distribution function (CDF) by hand and using Wolfram Alpha. The tricky part is finding the integral of e^(-z^2/2) and it is suggested to use the error function. The corresponding probability density function (PDF) is the normal density function.
  • #1
invictor
6
0
Hello, I am new. I been looking on the net for a guide how to solve the CDF by hand, i know the answer and I am about to crack this baby but I got stuck...

Im trying to calculate Cumulative distribution function by hand:
[itex]\int^{1}_{-1}\frac{1}{2\pi} e^{\frac{-z^{2}}{2}} dz[/itex] or wolfram alpha: integrate 1/sqrt(2*pi) * e^(-z^2 /2) dz from -1 to 1

Anyway, this is the tricky part, how do this? (I left out the lefthand part above part for easier readability):

[itex]\int e^{\frac{-z^{2}}{2}} dz = [/itex]

[itex]u = \frac{-z^{2}}{2} [/itex]

[itex]du = -z dz[/itex]

[itex]\frac{du}{-z} = dz[/itex]

[itex]\int e^{u} \frac{du}{-z} = [/itex]

then? How do i need to do?. Can any friendly soul here show me step by step how to solve this?

best regrads
invictor
 
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  • #2
You can define
[tex]\operatorname{erf}(x) = \frac{2}{\sqrt{\pi}} \int_0^x e^{-t^2} dt[/tex]
and express the result in terms of that, getting [itex]\operatorname{erf}(1/\sqrt{2})[/itex] as the answer.

You cannot give a (more) exact answer than that (otherwise we wouldn't be needing erf in the first place).
 
  • #3
Ok so now you have the CDF in terms of the error function erf(/frac{1}{/sqrt{2}}) what is the associating PDF?
 
  • #4
The density function for the error function is the normal density function, which is the integrand you started with.
 
  • #5
Thanks i got it now :)
 

Related to Need help solving CDF (Cumulative distribution function)

1. What is a Cumulative Distribution Function (CDF)?

A Cumulative Distribution Function (CDF) is a statistical tool used to describe the probability distribution of a continuous random variable. It shows the probability that a random variable takes on a certain value or falls within a certain range of values.

2. How do you calculate a CDF?

The CDF is calculated by taking the integral of the probability density function (PDF) of the random variable. The result is a function that maps each possible value of the random variable to the probability that it will fall below that value.

3. What is the difference between a CDF and a PDF?

A CDF shows the cumulative probability of a random variable taking on a certain value or falling within a certain range of values. A PDF, on the other hand, shows the probability of a random variable taking on a specific value. In other words, a CDF provides information about the probability distribution of a random variable, while a PDF provides information about the probability of a specific outcome.

4. How is a CDF used in statistics?

CDFs are commonly used in statistics to calculate the probability of a random variable falling within a certain range of values. They are also used to compare the distribution of different variables and to make predictions about future outcomes based on past data.

5. What is the relationship between a CDF and a survival function?

A survival function is the complement of a CDF, and it shows the probability that a random variable will exceed a certain value. In other words, a survival function shows the probability of survival beyond a certain point. The two are related in that they both provide information about the distribution of a random variable, but they show different aspects of it.

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