What happens when you square a standard normal random variable? (CDF method to find PDF)

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  • Опубликовано: 18 ноя 2024

Комментарии • 18

  • @이상윤-n7d
    @이상윤-n7d 2 года назад +6

    Such a great video! It is awesome that you show your complete thought process instead of just giving refined presentation. Extremely comprehensible.

    • @billkinneymath
      @billkinneymath  2 года назад

      Wish all my students thought that way. Some just want me to tell them what to do as quick as possible.

    • @이상윤-n7d
      @이상윤-n7d 2 года назад

      @@billkinneymath I think learning to think is more important than learning a certain topic 😄

  • @whereisyourstar.
    @whereisyourstar. Год назад +2

    You are indeed a life saver! Thank you so much for that great instructive video.

  • @JulieLarkin
    @JulieLarkin 6 месяцев назад +1

    Thank you for the video! I appreciate the thorough explanation.

  • @dilloninmotion
    @dilloninmotion 9 месяцев назад +2

    Super super helpful. Thank you.

    • @billkinneymath
      @billkinneymath  9 месяцев назад

      You're welcome! Thanks for watching!

  • @sanathgunawardena832
    @sanathgunawardena832 2 года назад +2

    You, sir, are a lifesaver.

  • @satheeshsimhachalam7563
    @satheeshsimhachalam7563 6 месяцев назад +1

    Wonderful stuff sir !!

  • @barondu8876
    @barondu8876 7 месяцев назад +1

    Thanks !!!!!!!!!!!!!!!!!!

  • @nick45be
    @nick45be 2 месяца назад

    So the chi square with 1 degree of freedom is the distribution of the variance of the normal distribution? And for n normal variable why It Is a n-1 dof chi square?

  • @augustodutra3839
    @augustodutra3839 17 дней назад +1

    What if the random variable is general normal and not standard normal?

    • @billkinneymath
      @billkinneymath  14 дней назад

      You get a pretty complicated distribution because you lose the symmetry in the calculation that comes from the mean being zero for standard normal. I did the calculation. The graph looks similar to a chi-square when mu is close to 0 and sigma is close to 1. On the other hand, when mu gets large, the graph looks more like a normal curve.