Discrete Random Variable Probloem

In summary, the conversation discusses the probability mass function and distribution functions for a discrete random variable X and a derived random variable Y. The calculation for the probability mass function of Y is shown, and the terminology and notation for the cumulative distribution function is clarified. Further clarification and work is needed to determine the CDF for X and Y at specific values.
  • #1
BlueScreenOD
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Homework Statement



Let X be a discrete random variable with probability mass function p given by:

a ...| -1 .| 0 ..| 1 ..| 2
-----+-----+-----+-----+---
p(a) | 1/4 | 1/8 | 1/8 | 1/2

and p(a) = 0 for all other a.

a.) Let random variable Y be defined by Y = X^2. Calculate the probability mass function of Y.

b.) Calculate the distribution functions for X and Y in a = 1, a = 3/4, a = pi - 3

Homework Equations



n/a

The Attempt at a Solution



a.) I know that if X = 2, Y = 4. And if X = 0, Y = 0, so
Py(4) = Px(2) = 1/2 and
Py(0) = Px(0) = 1/8

But what about -1 and 1? Does this mean that Py(1) = Px(-1) + Px(1)?

b.) Since we're only dealing with whole numbers, is it true that the probability distribution function for X and Y on a = 1, a = 3/4, a = pi - 3 is equal to PX(1) + PX(0) and PY(1) + PY(0) respectively?
 
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  • #2
Yes, P(Y=1) = P(X^2=1) = P(X=+-1) = P(X=1) + P(X = -1)

For part b you aren't "dealing with whole numbers", whatever that means. The cumulative distribution functions for X and Y are defined for all real numbers. Your notation is confusing. I assume you are using Py and Px for the probability mass functions of X and Y. You need to give some notation for the cumulative distribution function (CDF). Let's call the CDF of X by the name F(x). We don't say "the function of X in a". You ask for F(a) which is P(X <= a). Once you have that straight you can probably tell whether your answers are correct.
 
  • #3
Thanks for your help! The terminology and the notation are really what get me. I'll have to keep working on it. Thanks again!
 

FAQ: Discrete Random Variable Probloem

What is a discrete random variable?

A discrete random variable is a type of variable that takes on a countable number of distinct values. These values can be whole numbers or fractions, but they cannot take on any infinite or continuous values.

How is a discrete random variable different from a continuous random variable?

A discrete random variable can only take on specific, countable values, while a continuous random variable can take on any value within a given range. For example, the number of children in a family is a discrete random variable, while the height of a person is a continuous random variable.

What is the probability mass function of a discrete random variable?

The probability mass function (PMF) of a discrete random variable is a function that assigns a probability to each possible value of the variable. It is often denoted as P(X=x) or f(x), where X is the random variable and x is a specific value.

How is the mean of a discrete random variable calculated?

The mean or expected value of a discrete random variable is calculated by multiplying each possible value of the variable by its corresponding probability and then summing all of these products. In mathematical notation, it can be written as E(X) = ΣxP(X=x).

Can a discrete random variable have a normal distribution?

No, a discrete random variable cannot have a normal distribution because a normal distribution is a continuous distribution that can take on any value within a certain range. However, a discrete random variable can have a distribution that is approximately normal if the number of possible values is large enough.

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