Given probability density function find its cumulative distribution function

In summary, the problem is to find the CDF of a random variable X given its PDF, and the person tried two methods to compute it. The second method, with the correction of a missing parenthesis, is the required CDF for X.
  • #1
sofanglom
2
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Hi :) Here's my problem along with what I've done.

Here is the problem:

View attachment 8716

That is the p.d.f. of a random variable X.

I have to find the cdf. I don't know which I should do so I tried it two ways. First:

$\int_{-1}^{1} \ \frac{2}{\pi(1+x^{2})} dx = {{\frac{2}{\pi} arctan(x)]}^{1}}_{-1}=1$

Second:

$\int_{-1}^{x} \ \frac{2}{\pi(1+t^{2})} dt = {{\frac{2}{\pi} arctan(x)]}^{x}}_{-1}=\frac{2(arctan(x)+\frac{\pi}{4}}{\pi}$

Which one is the required CDF for X?
 

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  • #2
Hi, and welcome to the forum!

Which one is the required CDF for X?
The second one, except for the missing closing parenthesis. That is, the CDF is $\dfrac{2}{\pi}\arctan x+\dfrac12$.
 

FAQ: Given probability density function find its cumulative distribution function

What is a probability density function (PDF)?

A probability density function is a mathematical function that describes the probability of a continuous random variable taking on a specific value within a given range. It is represented by a curve and the area under the curve represents the probability of the variable falling within that range.

What is a cumulative distribution function (CDF)?

A cumulative distribution function is a mathematical function that shows the probability of a random variable being less than or equal to a specific value. It is the integral of the PDF and is represented by a step function.

How do you find the CDF from a given PDF?

To find the CDF from a given PDF, you can integrate the PDF function from negative infinity to the desired value. This will give you the probability of the random variable being less than or equal to that value.

Can the CDF be used to find the probability of a specific value?

No, the CDF only gives the probability of a random variable being less than or equal to a specific value. To find the probability of a specific value, you would need to take the difference between the CDF values at that value and the previous value.

What is the relationship between the PDF and CDF?

The PDF and CDF are mathematically related, with the CDF being the integral of the PDF. The PDF gives the probability density at a specific value, while the CDF gives the probability of the random variable being less than or equal to that value. In other words, the CDF is the cumulative sum of the PDF values.

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