Using joint probability mass functions

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SUMMARY

This discussion focuses on the application of joint probability mass functions (pmf) in statistical analysis, specifically addressing problems related to the distribution of sums of random variables. The analysis reveals that the marginal pmfs for two variables X and Y are equal, leading to the conclusion that their sum S follows a Poisson distribution with parameter λ=1. The discussion also highlights the independence of X and Y, demonstrating that they are not independent due to differing probabilities. Key calculations include determining the value of α and expected values for the variables involved.

PREREQUISITES
  • Understanding of joint probability mass functions (pmf)
  • Familiarity with Poisson distribution and its properties
  • Knowledge of factorials and their role in probability calculations
  • Basic concepts of independence in probability theory
NEXT STEPS
  • Study the derivation of the Poisson distribution from joint pmfs
  • Learn about the properties of marginal pmfs and their applications
  • Explore the concept of independence in probability and its implications
  • Investigate advanced statistical techniques for analyzing joint distributions
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Statisticians, data analysts, and students studying probability theory who are looking to deepen their understanding of joint probability mass functions and their applications in real-world scenarios.

Runty_Grunty
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I know assignment questions and such aren't meant to be placed in here, but there's no spot in the "homework section" that's meant for statistics. Move this to the appropriate section if necessary; I couldn't find it.

Here I have an image of the questions we are meant to answer.
Assignment3Q4.jpg


The first five parts have been finished, courtesy of a helper from http://www.mathhelpforum.com/math-help/f8/using-joint-probability-mass-functions-multiple-parts-162646.html" (I give him credit for this).
a)
It is clear from the definition of a marginal pmf that the two would be the same, given that we are summing either from 0 to infinity of i, or j to calculate either marginal.
P_X (X=x)=\sum_{y=0}^\infty P(X=x,Y=y) = \sum_{x=0}^\infty P(X=x,Y=y) = P_Y (Y=y)

b)
Letting S=X+Y to calculate P(S=k), we note;
For:
k=0 \implies X=0,Y=0 \text{ so } P(S=0)=P(X=0,Y=0)=\frac{\alpha}{(1)!}
Whereby the definition of factorials 0!=1!

k=1 \implies X=0,Y=1;X=1,Y=0 \text{ so } P(S=1)=2 \cdot P(X=1,Y=0)=2\cdot \frac{\alpha}{(1+1+0)!} = \frac{2\alpha}{1\cdot 2}=\frac{\alpha}{1!}
Since P(X=1,Y=0)=P(X=0,Y=1).
k=2 \implies X=0,Y=2;X=2,Y=0;X=1,Y=1 \text{ so } P(S=2)= 2 \cdot \frac{\alpha}{(1+2+0)!}+\frac{\alpha}{(1+1+1)!}=\frac{3\cdot \alpha}{3!}=\frac{\alpha}{2!}
So iterating for all k, we end up with the given formula.

c)
For any pmf, we know that the sum over all possible values equals 1. So we can solve for \alpha
1=\sum_{k=0}^\infty \frac{\alpha}{k!}=\alpha \cdot e^1
Therefore, \alpha=e^{-1}, so S is Poisson distributed with parameter \lambda =1.

d)
P(X=0)=\sum_{y=0}^\infty \frac{e^{-1}}{(1+y)!}=e^{-1}\sum_{y=1}^\infty \frac{1}{(y)!}=e^{-1}(e^1-1)=1-e^{-1}
By the definition of independence,
P(X=0,Y=0)=P(X=0)P(Y=0)
LHS: P(X=0,Y=0)=P(S=0)=e^{-1}=0.3679
RHS: P(X=0)P(Y=0)=(1-e^{-1})^2=0.399 \neq \text{LHS}, as X and Y have the same distribution.
Hence, they are not independent.

e)
Since S \sim \text{Poi}(1) its expected value is \lambda =1
Using the fact from a), X and Y have the same distribution and hence same expected value;
E(S)=1=E(X)+E(Y)=2\cdot E(X)
Therefore, E(X)=0.5.

f) and g) have not been answered yet, so I could use a hand on them. Also, if any mistakes have been made, can you point them out?
 
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Did you try the hint for (f)? How far did you get?

For (g), use the fact that P(X > Y) = P(X < Y). (Why?) And then observe that exactly one of {X = Y}, {X < Y}, and {X > Y} are true.
 

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