Random variable distribution question

In summary: In short, you need to find where the CDF and PDF of X are connected.You say (e) is "just derivative". Derivative of what? (I am asking you to write it down, not just think it.)In summary, we discussed finding the pmf and cdf of a random variable x defined on the interval (1,3), with a given probability mass function, and finding g(y) for y=x^2, expectation and variance of y, and the most probable value of y. The pmf was found by integrating f(x) from 1 to 3 and solving for A. The pmf g(y) was found by inserting x=square root of y into f(x) and multiplying by
  • #1
diracdelta
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Homework Statement


Random variable x is defined on interval (1,3) and it has probability mass function f(x) =A(x2 +1=
a) Find PMF, g(y) for y=x2
b)Expectation of y
c)Variance of y
d)Distribution function of y.
e)most probable value of y

The Attempt at a Solution


As far as a), i integrated from 1 to 3 f(x) and found A=3/32
So, for g(y) i inserted x=square root of y into f(x), plus due to it is parabola, it is multiplied by two. No problem there, and also, y is defined on [1,9]
c) and d) is easy, just integrate

d) Distribution function of y, how to find that?
I know that distribution function is F(X):=P(X<=x)= integral from -inf to x f(t)dt
How do i apply it on this very case?

e) Just derivative

Thanks for reading and spending time to answer me :)
 
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  • #2
I am not sure why you multiplied by 2 in part a. What do you mean by it is parabola? x is only defined on the positive side, so there is no duplication in y=x^2.
For d) you want to find the integral of f(y).
##1 = A\int_{1^2}^{3^2}(y+1) dx##
the tricky part in the integral is properly changing dx into dy. Once you do that, you can check your answer to see that it still integrates to 1.
Then you simply put it back together into the form you need:

##F(y) = \left\{ \begin{array}{l l} 0 &\text{for } y \leq 1\\
A\int_{1}^{\sqrt{y}}(x^2+1) dx & \text{for} 1< y < 9 \\
1 &\text{for } y \geq 9 \end{array} \right.##
 
  • #3
diracdelta said:

Homework Statement


Random variable x is defined on interval (1,3) and it has probability mass function f(x) =A(x2 +1=
a) Find PMF, g(y) for y=x2
b)Expectation of y
c)Variance of y
d)Distribution function of y.
e)most probable value of y

The Attempt at a Solution


As far as a), i integrated from 1 to 3 f(x) and found A=3/32
So, for g(y) i inserted x=square root of y into f(x), plus due to it is parabola, it is multiplied by two. No problem there, and also, y is defined on [1,9]
c) and d) is easy, just integrate

d) Distribution function of y, how to find that?
I know that distribution function is F(X):=P(X<=x)= integral from -inf to x f(t)dt
How do i apply it on this very case?

e) Just derivative

Thanks for reading and spending time to answer me :)

The random variable does not have a pmf; it has a pdf or a cdf. I assume you want the pdf in part (a) and the cdf in part (d).

Please write exactly your formula for g(y); what you describe sounds wrong, but I cannot be sure until you show the details.

You say (c) and (d) is easy: "just integrate"; then you go on to say you do not know how to do (d)! In fact, the safest way to get g(y) is to first get G(y) = P(Y ≤ y), then differentiate it to get g(y) = dG(y)/dy. (In other words, if I were doing it I would do (d) first, then do (a)!) In turn, to get G(y), look at what is the event {Y ≤ y} back in terms of X and x.
 
Last edited:

1. What is a random variable?

A random variable is a numerical value that represents the outcome of a random experiment or event. It can take on different values with certain probabilities, and is used to analyze and model uncertain situations in statistics and probability theory.

2. What is a distribution?

A distribution refers to the range of values that a random variable can take on and the corresponding probabilities of each value occurring. It describes the pattern or shape of the data and helps to understand the likelihood of different outcomes.

3. What are the types of random variable distributions?

There are three main types of random variable distributions: discrete, continuous, and mixed. Discrete distributions have finite or countable values, while continuous distributions have an infinite number of possible values. Mixed distributions combine both discrete and continuous values.

4. How is a random variable distribution represented?

A random variable distribution is typically represented using a graph or a mathematical equation. The graph can be a histogram, bar chart, or a line chart, while the equation can be a probability mass function (for discrete distributions) or a probability density function (for continuous distributions).

5. What is the importance of random variable distribution in statistical analysis?

Random variable distribution is crucial in statistical analysis as it helps to understand and predict the outcomes of random events. It also allows for the calculation of important statistical measures such as mean, median, and variance, which are used to make decisions and draw conclusions from data.

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