Probability functions: pdf, CDF and inverse CDF (FRM T2-1)

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  • Опубліковано 29 вер 2024
  • [Here is my XLS @ trtl.bz/2AgvfRo] A function is a viable probability function if it has a valid CDF (i.e., is bounded by zero and one) which is the integral of the probability density function (pdf). The inverse CDF (aka, quantile function) returns the quantile associated with a probability, q = F^(-1)(p), whereas the CDF returns the probability associated with a quantile: p = F(q). As mentioned, the =POISSON_INV(.) is not built into excel. Rather, it is available from the wonderful and free add-on published by Charles Zaiontz and available at www.real-statis.... Discuss this video here in our FRM forum: trtl.bz/2O7chQg.
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КОМЕНТАРІ • 27

  • @mayankawasthi6839
    @mayankawasthi6839 6 років тому +1

    great video.i learned a lot from it.i would like to know what work do u do and where?

    • @bionicturtle
      @bionicturtle  6 років тому +1

      Thank you for watching! Bionic Turtle is an FRM study prep program. We provide study materials for the FRM exam on our website www.bionicturtle.com

  • @jayjiang9416
    @jayjiang9416 4 роки тому +1

    You teach better than some professors in the university.

  • @arturoaramburo9365
    @arturoaramburo9365 4 роки тому

    Is there a formula to obtain the CDF directly when SD=1 and mean=0?
    Just wondering if there’s a way besides the “=NORM.S.DIST(XX,TRUE)”
    Thank you..

  • @mortezaabdipour5584
    @mortezaabdipour5584 6 років тому +1

    BTW, Thanks for sharing the knowledge.

  • @alifallah1939
    @alifallah1939 5 років тому +1

    Nice video and good explanation but a bit lengthy and too much of repeating

    • @bionicturtle
      @bionicturtle  5 років тому +1

      okay i appreciate the constructive feedback, thank you

  • @divyas0510
    @divyas0510 6 років тому +3

    Really easy to understand

    • @bionicturtle
      @bionicturtle  6 років тому +1

      Thank you! We always appreciate this kind of feedback!

  • @devon5694
    @devon5694 6 років тому +4

    Brilliantly explained.

  • @electronbob6676
    @electronbob6676 3 роки тому

    Great video! Would love to see you do a similar video using lognormal distribution .... thanks!

  • @rogelioasco6908
    @rogelioasco6908 5 років тому +1

    Thanks for sharing. it was very useful for me.

  • @dvs8321
    @dvs8321 5 років тому +1

    Great presentation and thanks for sharing it.

  • @vkittu1989
    @vkittu1989 4 роки тому

    Count vs Measured. Thanks a lot.

  • @sbakshi9515
    @sbakshi9515 3 роки тому

    how to calculate N'(d)

  • @Blueshockful
    @Blueshockful 4 роки тому

    Thanks so much for sharing

  • @ruili6762
    @ruili6762 6 років тому +4

    Great example, I understand this concept well. Thanks!

  • @andresjimenezbonilla2532
    @andresjimenezbonilla2532 6 років тому +1

    Thanks for sharing it!!

  • @mortezaabdipour5584
    @mortezaabdipour5584 6 років тому

    Is it really true when we ask the probability for an interval from CDF not PDF ?

    • @bionicturtle
      @bionicturtle  6 років тому +1

      as i'm sure you know, for a continuous distribution P{a < X < b) = integral [a,b] of the f(x) pdf, or equivalently, F(b) - F(a) of the CDF

    • @mortezaabdipour5584
      @mortezaabdipour5584 6 років тому

      Yes right.

  • @mohamedhassan8886
    @mohamedhassan8886 5 років тому

    good one but y cant you do problems involving temperature, strings and time intervals too