Probability Density Function for F(x)=k(1-1/x2)

In summary, the conversation revolves around finding the probability density function for a continuous random variable given a specific CDF. The process involves setting the CDF equal to 1 and differentiating to find the PDF. There is disagreement over the specific steps and one poster accuses another of being arrogant.
  • #1
XodoX
203
0

Homework Statement



F(x)=k(1-1/x2), 1[itex]\leq[/itex]x<2

Homework Equations





The Attempt at a Solution



How do I get the probability density function here? Simply take the derivative of this function ?

1[itex]\int[/itex]2 = k(1-1/x2)

Supposed to be 1 at the bottom and two at the top.
 
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  • #2
You want to find a value of k so that the integral of your function over [1,2) is equal to 1.
 
  • #3
Well, it says specify the probability density function. So I'm guessing just show it, don't solve it. But I don't get what you mean, though. Solve for k and that needs to be equal to 1 then?
 
  • #4
XodoX said:
Well, it says specify the probability density function. So I'm guessing just show it, don't solve it. But I don't get what you mean, though. Solve for k and that needs to be equal to 1 then?

A probability density function need to have integral 1 over it's domain. Integrate your function from 1 to 2 factoring the k out. Then set the result equal to 1. Find k.
 
  • #5
Well, but in my post it already was 1 and 2. Ok, then factor the k out.

Do you mean like this?

k∫(1-...)dx=1
 
  • #6
If F(x) is supposed to be the CDF, you need to find the value of k that makes that true. Then you differentiate to find the PDF. If F(x) is supposed to be a PDF, you need to integrate and equate that to 1, to find k. It is not clear which is the case.

RGV
 
  • #7
F(x) is the distribution function for a continuous random variable. Sorry, guess I forgot to mention it.
 
  • #8
XodoX said:
F(x) is the distribution function for a continuous random variable. Sorry, guess I forgot to mention it.

Ray Vickson is right. If F(x) is a cumulative distribution function, then you want to determine k by setting F(2)=1. Then differentiate to find the probability density function. I had thought it was the probability density function, in which case the procedure you outlined in post 5 is correct.
 
  • #9
Dick said:
Ray Vickson is right. If F(x) is a cumulative distribution function, then you want to determine k by setting F(2)=1. Then differentiate to find the probability density function. I had thought it was the probability density function, in which case the procedure you outlined in post 5 is correct.


Thanks. But why 2? Why not also 1 ? And why does it have to equal one again?
 
  • #10
  • #11
Ok, so I just plug in 2 for x but I still use 1 and 2 when I take the integral, so the domain stays the same.
 
  • #12
XodoX said:
Ok, so I just plug in 2 for x but I still use 1 and 2 when I take the integral, so the domain stays the same.

I don't understand why you want to integrate anything. Somebody has already done the integration [itex]\int_1^x f(t) dt [/itex] for you, and their answer is F(x). All you need to do is find the right value of k.

RGV
 
  • #13
Because that's what the book says I have to do to get the probability density function..
 
  • #14
XodoX said:
Because that's what the book says I have to do to get the probability density function..

How do you get the density function f(x) from the CDF F(x)?

RGV
 
  • #15
I don't know.
 
  • #16
XodoX said:
I don't know.

This is so basic, so if you don't know it there is something seriously wrong. My recommendation: quit the course now, you have no chance of passing.

RGV
 
  • #17
You said, in your very first post,
XodoX said:
How do I get the probability density function here? Simply take the derivative of this function ?
Yes! What everyone was telling you before was how to find k so that you know what the cumulative function is.
 
  • #18
Ray Vickson said:
This is so basic, so if you don't know it there is something seriously wrong. My recommendation: quit the course now, you have no chance of passing.

RGV
You have been extremely arrogant. If you don't have anything meaningful to say, then just don't say anything. You are not helpful or contributing anything. You are the most arrogant poster I have seen on here.
 

What is a Probability Density Function (PDF)?

A Probability Density Function (PDF) is a mathematical function that represents the likelihood of a continuous random variable taking on a specific value within a given range. It is used to describe the probability distribution of a continuous random variable.

How is a PDF different from a Probability Mass Function (PMF)?

A PDF is used for continuous random variables, while a PMF is used for discrete random variables. This means that a PDF gives the probability of a variable falling within a specific range of values, while a PMF gives the probability of a variable taking on a specific value.

What is the area under a PDF curve?

The area under a PDF curve represents the probability of a random variable falling within a specific range of values. This area is always equal to 1, as the total probability of all possible outcomes must equal 1.

How is a PDF related to a Cumulative Distribution Function (CDF)?

The CDF is the cumulative sum of probabilities for a given variable, while the PDF is the derivative of the CDF. In other words, the PDF gives the rate of change of the CDF at a specific point.

How is a PDF used in statistics and data analysis?

PDFs are used to model and analyze continuous data in many statistical techniques, such as regression analysis and hypothesis testing. They are also used in machine learning algorithms to make predictions based on probability distributions.

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