Discrete Random Variable Probloem

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SUMMARY

The discussion focuses on the calculation of the probability mass function (PMF) and cumulative distribution functions (CDF) for a discrete random variable X with a defined PMF. The PMF of Y, defined as Y = X^2, is derived from the values of X, leading to specific probabilities for Y. The conversation highlights the importance of proper notation and terminology in probability theory, particularly in distinguishing between PMF and CDF, and emphasizes the need for clarity in mathematical expressions.

PREREQUISITES
  • Understanding of discrete random variables and their probability mass functions
  • Familiarity with cumulative distribution functions (CDF)
  • Basic knowledge of mathematical notation in probability theory
  • Ability to perform calculations involving squares of random variables
NEXT STEPS
  • Study the properties of cumulative distribution functions (CDF) for discrete random variables
  • Learn how to derive probability mass functions (PMF) from transformations of random variables
  • Explore the concept of joint distributions and their applications in probability
  • Review notation and terminology used in probability theory for clarity in communication
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Students studying probability theory, educators teaching discrete random variables, and anyone looking to improve their understanding of probability mass functions and cumulative distribution functions.

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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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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.
 
Thanks for your help! The terminology and the notation are really what get me. I'll have to keep working on it. Thanks again!
 

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