Deriving the least squares regression estimators

Поділитися
Вставка
  • Опубліковано 3 лип 2024
  • MA26620: Applied Statistics

КОМЕНТАРІ • 3

  • @TimReddick
    @TimReddick 4 роки тому +2

    Excellent video! My textbook didn't explain this derivation as clearly. In the video, we set out to find the critical points that would allow us to then find local min, local max, and saddle points. How do we intuitively know that the simultaneous equations that we've found and solved lead to a minimum? Is it because we logically know that there is no maximum for SSE, i.e. the squared sum of residuals has no bound, so the solution we found must be a minimum? Therefore, there's no need to test the critical points? How do we rule out a saddle point?

    • @1UniverseGames
      @1UniverseGames 3 роки тому

      How can we obtain intercept and slope of B0 and B1 after shifting line l to l'. Do you have any book to suggest to solve such problem and can you help please with kind suggestions

  • @phillcom3
    @phillcom3 2 роки тому

    why do these things never give a simple flow chart of how to do I can't remember any of what you just showed and subsequently dotn remember any of tit waste of my 14 mins trying to watch it you showing of some differentiation. not useful for me in my engineering need of the regression for some stats. and specifically how do if ind b2? what more do I need to do ?