Distribution of a function of a Uniform distributed variable

Discussion in 'CT3' started by lucky999, Apr 10, 2013.

  1. lucky999

    lucky999 Member

    If R = 300000 - 500000U, and if U ~ Uniform[0,1], then how do we work out the parameters of the distribution of R??

    I know it is uniform, but I seem to have had a mind block!
    I mean if we take E(R) and Var(R), then we can have an "approximate" normal distribution.
    But how do we actually work it out....I know I need to use the tables on page 13...:S

    Thanks in advance
     
  2. John Lee

    John Lee ActEd Tutor Staff Member

    By parameters do you mean the range?

    In which case it's quite straightforward:

    0 < U <1

    So

    300000 - 500000×0 > 300000 - 500000U > 300000 - 500000×1
    300000 > R > -200000

    note the signs swap as we've multiplied by a negative number.
     
  3. lucky999

    lucky999 Member

    Many thanks for your reply John! That makes sense now.

    But what about say:
    R = 300000 - 500000P

    where P ~ Poisson(5)

    How would we find the distribution for R (I assume its Poisson?) and what would be the parameter?


    And suppose R = 300000 - 500000B
    where B ~ Binomial (100,0.25)

    Again how would we find the distribution of R?
     
  4. Calum

    Calum Member

  5. suraj

    suraj Member

    First of all, I am using

    R = 3 - 5P for convenience :)

    Now, if
    P = 0 , 1 , 2 , 3 , 4 ...... then
    R = 3 , -2 , -7 , -12 , -17 ......

    So R is taking Poisson probabilities but for different set of values.
    I am not sure that we can say R is Poisson because it is taking negative values, but we can certainly find its PDF by starting like this:

    Prob.(R = r)
    = Prob.( 3 - 5P = r)
    = Prob.(P = x)

    where, x = (3 - r)/ 5

    So its PDF is [ exp(-5) * 5^x / x! ]

    with mean "-22" and variance "125"
    and range of R is

    r = 3 , -2 , -7 , -12 , -17 ......

    Similarly you can do this for Binomial dist. also
     
  6. John Lee

    John Lee ActEd Tutor Staff Member

    Answers above are good.

    In general, for DRV you just take the same probabilities but apply them to different X's (if that makes sense).

    Whereas for CRV you have to use the function of a RV (at the end of Ch3) to obtain the new f(x).
     

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