Calculating Probabilities using Distribution Function F

In summary, the distribution function F of a random variable X is given and the following probabilities are calculated: P(X<0)=0.1, P(X<=0)=0.2, P(3< X<= 4)=0, P(X>5)=P(X>=5)=0, P(X=-1)=0.1, and P(X=1)=0. However, there may be some uncertainty in the calculations for P(3<=X<=4) and P(X=1) due to the inclusion of boundaries. Further clarification may be needed.
  • #1
mathmari
Gold Member
MHB
5,049
7
Hello...!I need some help...!
Let the distribution function F of a random variable X given in the following attachment. Calculate the following:
P(X=-1), P(X<0), P(X<=0), P(X=1), P(X>5), P(X>=5), P(3<=X<=4).

I think that these are the answers:P(X<0)=F(0-)=0.1, P(X<=0)=F(0)=0.2, P(3<=X<=4)=F(4)-F(3)=0.8-0.8=0, P(X>5)=P(X>=5)=0, P(X=-1)=F(-1+)-F(-1-)=0.1-0=0.1, P(X=1)=F(1+)-F(1-)=0.3-0.3=0,but I am not sure...
I hope you can help me...!
 

Attachments

  • 552876_485386744849993_938291547_n.jpg
    552876_485386744849993_938291547_n.jpg
    8.6 KB · Views: 59
Mathematics news on Phys.org
  • #2
Hi mathmari,

Welcome to MHB! I will try to answer the parts I can, although there are a couple of parts where I'm not quite sure about the answer.

a) $P(X<0)=F(0-)=0.1$ This looks good to me.
b) $P(X \le 0)=F(0)=0.2$ (Yes)

c) $P(3 \le X \le 4)=F(4)-F(3)=0.8-0.8=0$ This one I'm not sure about. The reason why is because usually the bottom boundary is not included. I believe that $P(3< X \le 4)=F(4)-F(3)=0.8-0.8=0$ but I'm not sure about how including 3 affects this. Just something to think about.

d) $P(X>5)=P(X \ge 5)=0$ These mustn't always be equal but in this problem I agree. Looks ok to me.
e) $P(X=-1)=F(-1+)-F(-1-)=0.1-0=0.1$ I would write it as $P(X=1)=P(X \le 1) - P(X <1)$ but yep, the final answer looks good.

f) $P(X=1)=F(1+)-F(1-)=0.3-0.3=0$. Again, $P(X=1)=P(X \le 1)-P(X<1)$. The tricky thing here is that for $X \in [0,1]$ the CDF appears to show that $X$ is continuous, not discrete so it seems like an integral might be needed. Not sure on this one, but that's my comment.

Sorry I couldn't completely help you but hopefully this is a start and someone else can comment as well! Once again, welcome to MHB.
 
Last edited:
  • #3
Thank you! :)
 

Related to Calculating Probabilities using Distribution Function F

1. What is a distribution function F?

A distribution function F is a mathematical function that describes the probability of a random variable taking on a certain value or falling within a certain range of values. It is used to model and analyze the behavior of random variables in various fields such as statistics, physics, and finance.

2. How is a distribution function F different from a probability density function?

A distribution function F is the cumulative sum of a probability density function (pdf) and represents the total probability of a random variable being less than or equal to a certain value. In contrast, a pdf represents the probability density at a specific value. In other words, F(x) = P(X ≤ x) while pdf(x) = P(X = x).

3. What types of distributions can be represented by a distribution function F?

A distribution function F can represent a wide range of distributions, including continuous distributions such as normal, exponential, and beta distributions, as well as discrete distributions such as binomial and Poisson distributions. It can also be used to represent mixed distributions, which combine both continuous and discrete components.

4. How is a distribution function F used in statistical analysis?

In statistical analysis, a distribution function F is used to calculate probabilities and percentiles of a given dataset. It can also be used to compare the distribution of a dataset to a known distribution and determine the goodness of fit. Additionally, it is used in hypothesis testing and confidence interval estimation.

5. Can a distribution function F be used to generate random numbers?

Yes, a distribution function F can be used to generate random numbers by using inverse transform sampling. This method involves generating a random number from a uniform distribution between 0 and 1, and then using the inverse of the distribution function to map it to a value from the desired distribution. This process can be repeated to generate multiple random numbers from the distribution represented by F.

Similar threads

Back
Top