Determining the distribution function F(x): Statistics/Probability

In summary: Thanks for your help!In summary, the equation must be integrated to get the distribution function, but the constants (1/2) come from the density function for x<-1. Additionally, the F(x) for x>1 should be just 1 + Constant.
  • #1
mikebro
3
0

Homework Statement



For the random variable X with probability density function determine the distribution function F(x)

http://img20.imageshack.us/img20/3314/questiontsy.jpg

Homework Equations



Cumulative Distribution Function


The Attempt at a Solution



Integrate f(x) to get the distribution function:
F(X) =
  • 0, x ≤ -1
  • x[tex]^{2}[/tex]/2 + x, -1 < x ≤ 0
  • x - x[tex]^{2}[/tex]/2, 0 < x ≤ 1
  • 0, x > 1


The actual answer is
http://img297.imageshack.us/img297/426/answer.jpg

I understand that the equation must be integrated to get the distribution function, but where do the constants (1/2) come from and how are they calculated?
 
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  • #2
You can't just take a piecewise density function and find the indefinite integral of each piece separately. Notice your F(x) isn't continuous (any integrable density function must have a continuous cumulative distribution function), so it has to be incorrect.

Remember the cumulative distribution function is [itex]\int_{-\infty}^{x}f(t)\,dt,[/itex] where f is your density function (t is just a dummy variable). Now obviously the values of f for x less than or equal to 1 contributes nothing to the cumulative distribution function, so to find the values of F for [itex]-1<x\leq0,[/itex] you need to compute
[tex]\int_{-\infty}^{x}f(t)\,dt = \int_{-\infty}^{x} t + 1\,dt = \int_{-1}^{x} t + 1\,dt. [/tex]
 
  • #3
Thanks, that helps clarify a bit. I solved the integral you posted and got the correct answer, but tried to solve the other 2 and got an answer different than what the professor gave.

To get the F for [itex]0<x\leq-1,[/itex] I used this integral:
[tex]\int_{-\infty}^{x}f(t)\,dt = \int_{-\infty}^{x} 1 - t\,dt = \int_{0}^{x} 1 - t\,dt. [/tex]

but since F(0) = 0, the answer I got isn't what the professor posted. Does the 1/2 carry over from the F for [itex]-1<x\leq0,[/itex] carry over to this one?

And for the [itex]x>1,[/itex] obviously the F is just 0 + Constant, is it just known that the value of this is 1 since it must add up to one?
 
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  • #4
mikebro said:
Does the 1/2 carry over from the F for [itex]-1<x\leq1,[/itex] carry over to this one?

Of course! I mean what you first calculated was the cumulative distribution function for x between -1 and 0, so F(0) = 1/2, not 0. I think you should try drawing the density out to get a geometric feel for the problem for now; this isn't hard once you do a few more of these.

For the last "piece" F(x) should just be 1, but remember you should be able to get this value by computing F(1). The cumulative density function should be continuous, and you should check this at the endpoints of each piece.
 
  • #5
I actually do have a graph of the density function.
http://img64.imageshack.us/img64/8127/graphbo.png

But if F(0) = 1/2, when solving the integral above and doing F(x) - F(0), wouldn't that make it (x^2)/2 + x - (1/2)? What I meant was F(0) = 0 in context of the integral above, then adding the actual value of F(0) from the -1 < x ≤ 0 piece.

I am realizing these seem relatively easy as I work through it, but there's just a few small things I'm unsure of and can't find examples of in my textbook or lecture notes.
 
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1. What is a distribution function?

A distribution function, also known as a cumulative distribution function (CDF), is a mathematical function that describes the probability of a random variable being less than or equal to a given value. In other words, it shows how likely it is for a random variable to fall within a certain range of values.

2. How is a distribution function related to probability?

A distribution function is directly related to probability because it represents the cumulative probabilities of all possible outcomes of a random variable. By calculating the area under the distribution curve, we can determine the probability of a random variable falling within a specific range of values.

3. How do you determine the distribution function?

The distribution function is determined by plotting the values of a random variable on the x-axis and their corresponding cumulative probabilities on the y-axis. This results in a curve that starts at 0 on the left and approaches 1 on the right. The actual distribution function can then be determined by fitting a mathematical function to this curve.

4. What is the importance of the distribution function in statistics?

The distribution function is a fundamental concept in statistics because it allows us to understand and analyze the behavior of random variables. By knowing the distribution function, we can calculate probabilities, make predictions, and make informed decisions based on data.

5. Can a distribution function be used for discrete and continuous random variables?

Yes, a distribution function can be used for both discrete and continuous random variables. For discrete variables, the distribution function is a step function where the probability increases in discrete steps. For continuous variables, the distribution function is a smooth curve that represents a range of values for the random variable.

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