Probability random variables

In summary, the conversation discusses finding the associated mean and variance for a random variable with a given distribution function. The attempt at a solution involves integrating and summing over different intervals, but there are issues with convergence. Clarification is needed on the function and the notation used.
  • #1
Gott_ist_tot
52
0

Homework Statement



A random variable has a distribution function F(z) given by
F(z) = 0 if z< -1
F(z) = 1/2 if -1 <= z < 2
F(z) = (1-z^{-3}) is 2 <= z

Find the associated mean and variance.

The Attempt at a Solution


I drew the distribution function. I started with the associated mean (if I can figure that out the variance should follow.) I have:

E[Z] = [tex] \sum [/tex] zp(z)

p(z) = P[X = z]

Therefore,
p[X = -1] = P[X= -1] - P[X<-1]
= F(-1) - lim F(1-1/n)
= 1/2 - (1-2)
= 3/2

Sorry, if I messed up badly somewhere. The class is taught without a book and I can't seem to get anything out of my notes for this homework. Thanks.
 
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  • #2
Your notation is confusing to me, but for starters 1/2 - (1-2) isn't -1/2.
 
  • #3
Sorry, for the bad arithmetic. That is fixed. The problem statement is a piecewise function. As for the rest of the notation. I don't know how else to write it. My professor said this usually isn't presented in textbooks in this manner but he thinks it is a good way to do it. So the notation may be quite strange. If there is a specific part I might be able to explain what I am doing.
 
  • #4
Gott_ist_tot said:

Homework Statement



A random variable has a distribution function F(z) given by
F(z) = 0 if z< -1
F(z) = 1/2 if -1 <= z < 2
F(z) = (1-z^{-3}) is 2 <= z

Find the associated mean and variance.

The Attempt at a Solution


I drew the distribution function. I started with the associated mean (if I can figure that out the variance should follow.) I have:

E[Z] = [tex] \sum [/tex] zp(z)

p(z) = P[X = z]

Therefore,
p[X = -1] = P[X= -1] - P[X<-1]
= F(-1) - lim F(1-1/n)
= 1/2 - (1-2)
= 3/2

Sorry, if I messed up badly somewhere. The class is taught without a book and I can't seem to get anything out of my notes for this homework. Thanks.
What are you adding over? The way you have written F it looks like a piecwise function and you should be integrating, not adding:
[tex]E[Z]= \int_{-1}^\infty zF(z)dz= \int_{-1}^2 \frac{z}{2}dz+ \int_2^\infty (z- z^{-2}dz[/tex]
Unfortunately, it looks to me like that last integral won't converge. Are you sure it wasn't [itex]F(z)= (1- z)^{-3}[/itex] if z> 2?

If F is only defined for integer z (then you should have told us that),
[tex]E(Z)= \sum_{-1}^\infty nF(n)= \sum_{-1}^2 \frac{n}{2}+ \sum_2^\infty (n+ n^{-2})[/tex]
Again, there is a problem with the final sum: it doesn't converge.
 

1. What is the definition of a probability distribution?

A probability distribution is a mathematical function that assigns a probability to each possible outcome of a random variable. It shows the likelihood of each possible outcome occurring.

2. What is a random variable?

A random variable is a variable whose value is determined by the outcome of a random event. It can take on different values, and its probability is described by a probability distribution.

3. What is the difference between a discrete and a continuous random variable?

A discrete random variable can only take on a finite or countably infinite number of values, while a continuous random variable can take on any value within a given range. Examples of discrete random variables include the number of children in a family, while examples of continuous random variables include height and weight.

4. How is the expected value of a random variable calculated?

The expected value of a random variable is calculated by multiplying each possible outcome by its corresponding probability and then summing them all together. It represents the average value of the random variable over a large number of trials.

5. Can you give an example of a real-life application of probability random variables?

One example is in the field of finance, where probability random variables are used to model stock prices and calculate the expected return on investment. By understanding the probability of different outcomes, investors can make informed decisions about which stocks to invest in.

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