Correlation and independence in Probability

In summary, the two variables X and Y are not independent because their expected values are not equal. Furthermore, X and Y are uncorrelated because their random variables are linearly dependent.
  • #1
vikkisut88
34
0

Homework Statement


Let X be a random vairable which can only take three values: -1,0,1 and they each have the same probability. Let Y also be a random vairable defined by Y = X2. Show that
i) X and Y are not independent
ii) X and Y are uncorrelated


Homework Equations


To show that two random variables are independent, you show that E(XY) /= E(X)E(Y)
To show that two random variables are uncorrelated, you show that [tex]\rho[/tex]x,y = 0


The Attempt at a Solution


I have found E(X) = 0 and E(Y) = 2/3 and so E(X)E(Y) = 0, however i don't know how to calculate E(XY) as I don't know how to construct the joint probability mass function table for X and Y
 
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  • #2
Why are you dealing with expected values? Why not just deal with the prob. mass functions, i.e. X and Y are independent if p(x, y) = pX(x) pY(y).

Determining p(x, y) is straightforward. For example, p(0, 1) = P{X = 0 and Y = 1} = P{X = 0 and X2 = 1} = P{X = 0} P{X2 = 1 | X = 0} = 0 since P{X2 = 1 | X = 0} = 0, which I hope you'll agree with.
 
  • #3
Okay so I'll work with pmf's instead, but then for example finding P{X=0 and Y=0} = P{X=0 and X2=0} = P{X=0}P{X2=0|X=0} would that be equal to 1/3 * 1 as P{X2=0|X=0} that is one?
 
  • #4
Yes. What else could it be?
 
  • #5
In fact, since they are uncorrelated (at least you want to show that) you expect that E(XY)=E(X)E(Y).
 
  • #6
exactly and that's why I'm really confused...i think this must be a special case or something.
 
  • #7
I think you're making a very common error by assuming that two random variables are uncorrelated if and only if they are independent. This statement is in fact not true, as this problem illustrates. For arbitrary random variables, X and Y independent implies uncorrelated, but uncorrelated does not necessarily imply independent, unless further assumptions are made. An assumption that makes this implication true is that X and Y are Gaussian, which is not true for the variables you are given.
 
  • #8
Which is why as e(ho0n3 said, you have do deal with the joint pmf and not the expectations to show that X and Y are not independent.
 
  • #9
but that is what i indeed went away and did and this gave me that E(XY) = E(X)E(Y) which implies independence. That is the only way i know how to/have been taught how to work out whether two random variables are independent or not. Therefore is there another way?
 
  • #10
Like I said, that doesn't necessarily show that the RVs are independent, as this problem illustrates. If X and Y are discrete random variables (like in this problem), to show that they're independent, show that P( X = x, Y = y ) = P( X = x ) P( Y = y ).
 
  • #11
ah okay - thank you! i had misread what had been previously typed about that, i do apologise
 

What is correlation in probability?

Correlation in probability refers to the relationship between two random variables. It measures the strength and direction of the linear relationship between the variables. A correlation coefficient of 1 indicates a perfect positive correlation, while a correlation coefficient of -1 indicates a perfect negative correlation. A correlation coefficient of 0 indicates no correlation between the variables.

What is independence in probability?

Independence in probability refers to the lack of relationship between two random variables. If two variables are independent, knowing the outcome of one variable does not provide any information about the outcome of the other variable. In other words, the occurrence of one event does not affect the probability of the other event occurring.

Can two variables be correlated and independent at the same time?

No, two variables cannot be both correlated and independent at the same time. Correlation measures the relationship between variables, while independence indicates the lack of a relationship. If two variables are correlated, they are not independent, and vice versa.

How is correlation different from causation?

Correlation does not imply causation. Just because two variables are correlated does not mean that one causes the other. There may be a third variable that is causing the observed correlation between the two variables. It is important to consider other factors and conduct further research before concluding a causal relationship between two variables.

How is correlation calculated?

Correlation is calculated using a formula called the correlation coefficient, which represents the strength and direction of the linear relationship between two variables. The most commonly used correlation coefficient is Pearson's correlation coefficient, which ranges from -1 to 1. Other correlation coefficients include Spearman's rank correlation coefficient and Kendall's tau coefficient.

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