Multiple linear regression - explained with two simple examples

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  • เผยแพร่เมื่อ 29 ต.ค. 2024

ความคิดเห็น • 32

  • @helenadesoba8894
    @helenadesoba8894 2 ปีที่แล้ว +11

    Alas! I found a video I could relate with well when it comes to multiple linear regression. The examples you used gave more clarity. Thanks so much

  • @archer9056
    @archer9056 7 หลายเดือนก่อน +1

    Best channel❤ but underrated ..
    Simple and intuitive explanation of complex concepts..

  • @DasithDm
    @DasithDm หลายเดือนก่อน +2

    this video understand more than others.... sooooo thank u and keep it up............... give example is beter way to undestand.... i kindly request videos that realated to Machine Learning 🥰

  • @tedransom8087
    @tedransom8087 3 ปีที่แล้ว +3

    Thank you! I finally understood how to interpret the coefficients in a multiple linear regression model.

  • @joyceks1882
    @joyceks1882 3 ปีที่แล้ว +6

    i've been looking for this example! so clear and well explained. thank you!!!

  • @fazlfazl2346
    @fazlfazl2346 ปีที่แล้ว

    Great explanation for interaction term. Thank you.
    One question though. Here you explained the meaning of interaction between a continuous variable and a categoric variable. How can we interpret the interaction when both the terms in the interaction are Continuous variables and when both the terms are Categoric variables?

  • @MsJaguarundi
    @MsJaguarundi 3 หลายเดือนก่อน +1

    Why do you change the example midway? You didn't explain how you calculated the equations.

  • @MarinaOrgino
    @MarinaOrgino 6 หลายเดือนก่อน +1

    Can I ask... Where did you get that 30.57 and 3.55?

    • @tilestats
      @tilestats  6 หลายเดือนก่อน

      Have a look at this video
      th-cam.com/video/taPvVyJVc_A/w-d-xo.html

  • @fazlfazl2346
    @fazlfazl2346 ปีที่แล้ว

    This is a great video.
    Does this mean we are actually analyzing men and women differently as we get two different regression lines: one for men and one for women. How will this compare if we run the model stratified by Gender?

    • @tilestats
      @tilestats  ปีที่แล้ว

      Yes, men and women are predicted differently by the model because they have separate regression lines.

  • @hopelesssuprem1867
    @hopelesssuprem1867 ปีที่แล้ว +1

    thank u for a good explanation

  • @nordanvinden5351
    @nordanvinden5351 ปีที่แล้ว +1

    Very good.

  • @SantiagoValdezRoy
    @SantiagoValdezRoy 8 หลายเดือนก่อน

    How is the code “0” or “1” determined? What if you had a third category? Would it be “2”? Only part that I didn’t follow fully.

    • @tilestats
      @tilestats  8 หลายเดือนก่อน

      No, then you add an extra term in the equation. If term 1 = 0 and term 2 = 0 it will represent the baseline group(group 1) If term 1 = 1 and term 2 = 0 it will represent group 2, if term 1 = 0 and term 2 = 1, it will represent group 3.

  • @khaingzar3136
    @khaingzar3136 4 หลายเดือนก่อน

    Which software is used to get the equation for model
    Price = constant + Age.Coefficient + Mileage.Coefficient ?

    • @tilestats
      @tilestats  4 หลายเดือนก่อน

      You have to create the equation on your own and then use the software to estimate the parameter values for the equation. I use R but you can use any other statistical software to get the same parameter values.

  • @OMARRAFIQUE-oz5td
    @OMARRAFIQUE-oz5td ปีที่แล้ว

    Thank you for this. What if we code Gender as 'M' and 'F' and not 0 and 1. Then at 10.50, it will be 12.9xM and not 12.9x1. Then how can we include 12.9xM in the intercept? What I mean is that 0 and 1 in this case are factors, can we multiply 12.9 with a factor (treating factor as a numeric)?
    Also we can choose any other number instead of 0 and 1 e.g. 3 and 9. Then in this case the intercepts will be different. So, this seems arbitrary as the Gender intercepts depend on the way we choose the numbers?

    • @tilestats
      @tilestats  ปีที่แล้ว

      You should always use 0 and 1 to recode a categorical variable because they represent absent or present. However, if you use a software, it will do this automatically.

  • @tomgu1893
    @tomgu1893 11 หลายเดือนก่อน

    if I had a category such as car dealer which has more than just two options (so I can't just put 0 and 1) how would I go about incorporating that?

    • @tomgu1893
      @tomgu1893 11 หลายเดือนก่อน

      say we had dealer a, dealer b and dealer c, where the difference is noticeable between them

  • @salwaabbas549
    @salwaabbas549 2 ปีที่แล้ว +1

    Thank you

  • @dfdfgdfkih
    @dfdfgdfkih 2 ปีที่แล้ว

    Well explained. Thanks a lot!

  • @asleyarmelllevado5352
    @asleyarmelllevado5352 2 ปีที่แล้ว

    do you have the data to to solve the coefficients in your example ?

    • @tilestats
      @tilestats  2 ปีที่แล้ว

      At minute 2:18, 7:03 and 12:38 you have all the data to reproduce the results, including estimating the coefficients.

    • @asleyllevado3050
      @asleyllevado3050 2 ปีที่แล้ว

      I still can't get it where the data is where you calculate the b0 and b1 and b2

    • @tilestats
      @tilestats  2 ปีที่แล้ว +1

      I do not show how to calculate the coefficients by hand. I simply plug in the data in a statistical software to compute these. In this video:
      th-cam.com/video/taPvVyJVc_A/w-d-xo.html
      I show how to calculate parameters by hand, but only for simple linear regression. For multiple linear regression it is a bit more calculations and I currently do not have a video on that.

  • @zu1fuqar
    @zu1fuqar 2 ปีที่แล้ว

    but how to cumpute coefficients in multiple regression?

    • @tilestats
      @tilestats  2 ปีที่แล้ว

      Use a software, or if you must do this by hand I would recommend this page:
      www.statology.org/multiple-linear-regression-by-hand/
      I also have a video on OLS for simple linear regression:
      th-cam.com/video/taPvVyJVc_A/w-d-xo.html