Find the mean and variance of Y^2

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Homework Help Overview

The discussion revolves around finding the mean and variance of Y^2, where Y represents the number of heads obtained from tossing a coin three times. Participants are also exploring a separate question regarding the relationship between an exponential distribution and a geometric distribution.

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Approaches and Questions Raised

  • Participants discuss calculating the mean and variance of Y^2, with some questioning the initial calculations and definitions used. There is also an exploration of how to derive the distribution of int(T) from an exponential distribution.

Discussion Status

Some participants have provided calculations for E[Y^2] and Var[Y^2], while others have raised questions about the correctness of these calculations and the definitions being applied. There is an ongoing exploration of the second question regarding the exponential distribution.

Contextual Notes

Participants are discussing the need to consider probabilities associated with the outcomes of Y, as well as the implications of the definitions of mean and variance in the context of discrete random variables.

BrownianMan
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Let Y by the number of heads obtained if a coin is tossed three times. Find the mean and variance of Y^2.

For the mean I get, (0+1+4+9)/4=7/2, and for variance I get (0+1+16+81)/4 - (7/2)^2 = 49/4. Is this correct?

For the following question, I'm not sure how to begin:

Show that if T has exponential distribution with rate lambda, then int(T), the greatest integer less than or equal to T, has geometric (p) distribution on {0, 1, 2,...}, and find p in terms of lambda.
 
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BrownianMan said:
Let Y by the number of heads obtained if a coin is tossed three times. Find the mean and variance of Y^2.

For the mean I get, (0+1+4+9)/4=7/2

not quite, the definition of mean of a discrete RV is
[tex]E(Y) = \Sigma p_i(Y=y_i) y_i[/tex]

so you need to include the probability of each outcome. The values you worte down would only be true if the probability of a number of heads is the same in each case, p(H=0) = p(H=1) = p(H=2) = p(H=3) which is clearly not true
 


It's suppose to be the expectation of Y^2 though, not Y...
 


Ok, so would E[Y^2] = 3, and Var[Y^2] = 7.5?
 


ok, show us how you got those values
 


E[Y^2] = (1/8)(0)+(3/8)(1)+(3/8)(4)+(1/8)(9) = 24/8 = 3

Var[Y^2] = [(1/8)(0^2)+(3/8)(1^2)+(3/8)(4^2)+(1/8)(9^2)] - 3^2 = 15/2
 


method looks good to me

E[Y^2]

Var[Y^2] = E[(Y^2)^2] - (E[Y^2])^2
 


Thanks!

Any ideas for the second question?
 

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