Chapter 4

Discussion in 'CT3' started by Bharti Singla, Jun 24, 2016.

  1. Bharti Singla

    Bharti Singla Senior Member

    Hii all. Anyone plz clarify... are the proofs of moments (mean, variance) of various distributions required for examination?? I mean can these proofs are tested in exam CT3?? plz rply asap.
    Thankyou
     
  2. Yes,proofs are imp and you should know them,as they are needed for detailed studies in CT6.
     
  3. Bharti Singla

    Bharti Singla Senior Member

    but the proofs are not given in chptr 4 .. from where I can get them?
     
  4. If you know the general formlas for Mean and Variance in statistical terms and also good with calculus you can derive it on your own.You can also refer to Fundamental Of Statistics by Gungupta Dasgupta Vol-1.
     
    Bharti Singla likes this.
  5. Bharti Singla

    Bharti Singla Senior Member

    ohk. I wil do it on my own.
     
    Varsha Agarwal likes this.
  6. Bharti Singla

    Bharti Singla Senior Member

    hii .. can anyone provide the proofs of mean, variance of negative binomial distribution? I shall be thankful.
     
  7. Bharti Singla

    Bharti Singla Senior Member

    plz anyone rply
     
  8. shdh

    shdh Ton up Member

  9. Bharti Singla

    Bharti Singla Senior Member

    ya..thanku... it is ok if we find moments by using MGFs. But I am not able to find moments of some of distributions without using MGFs.. Is it sufficient if we use MGFs for finding moments in the exam?
     
  10. shdh

    shdh Ton up Member

    I think that you can use it to find them out, but it would be better if you learned the proofs both with and without MGFs.

    Warm Regards,
    Shyam
     
  11. Bharti Singla

    Bharti Singla Senior Member


    ok. I will try again without using MGFs.
     
  12. John Lee

    John Lee ActEd Tutor Staff Member

    The whole point of MGFs is to make finding the means and variances of distributions easier. Why would we not use them?

    In the IFoA exams do test first principles means and variances but of simple non-standard distributions.
     

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