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nacho-man

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(please refer to attached image)

The question appears to be simple enough, but i have two queries

A) does E[X1 X2] mean the same as E[X1 | X2]

B) If not/so, how exactly do I go about computing this. I've seen a few formulas in my lectures notes for computing conditional expectations for discrete random variables,

however I find it difficult to understand and apply the notation/procedure.

Any help is appreciated!

EDIT 2: okay, is this correct

for E[X1 X2]

i do:

(-1)(-1)*(1/6) + ...

That is multiply each (X1,X2) and then multiply that by the probability of its occurrence, and add them all up?

The question appears to be simple enough, but i have two queries

A) does E[X1 X2] mean the same as E[X1 | X2]

B) If not/so, how exactly do I go about computing this. I've seen a few formulas in my lectures notes for computing conditional expectations for discrete random variables,

however I find it difficult to understand and apply the notation/procedure.

Any help is appreciated!

edit: ok, after some more research, I've found that

E(X1 X2] simply means The expectations of X1 and X2 multiplied by each other.

so, what I want to ask now is this.

is the PMF of X1, given that table:

X1 | -1 | 0 | 1 |

px(X1)| 1/3 | 0 | 1/3 |

And finally, how do i find out if X1 and X2 are independent?edit: ok, after some more research, I've found that

E(X1 X2] simply means The expectations of X1 and X2 multiplied by each other.

so, what I want to ask now is this.

is the PMF of X1, given that table:

X1 | -1 | 0 | 1 |

px(X1)| 1/3 | 0 | 1/3 |

And finally, how do i find out if X1 and X2 are independent?

EDIT 2: okay, is this correct

for E[X1 X2]

i do:

(-1)(-1)*(1/6) + ...

That is multiply each (X1,X2) and then multiply that by the probability of its occurrence, and add them all up?

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