Interaction Terms (Regression)

Rohen Shah explains interaction terms and non-linear regression

[@ClaudioHernandezGarcia]

I echo the comment section. Thank you Rohen for deciding to create this video. My professor is trash when it comes to how to communicate this subject to the students. Eternal blessings.

[@Diagknowstics]

Thank you so much for your kind words! It really means a lot to me, and motivates me to keep making more in the future :).

[@frankkkmate4267]

This is 100 times better than what our econometrics lecturer did, he was reading the slides to us the whole time while explains nothing. You are a champion!

[@Diagknowstics]

Thank you so much for the kind words! :)

[@ClaudioHernandezGarcia]

I think we attend the same institution 😅

[@wangyex]

This man is doing fantastic job explaining this compared to my prof...... Super helpful!

[@Diagknowstics]

Thank you for the kind words!! Feel free to share with your classmates :)

[@hassannamichi4833]

Thanks for your teaching, God bless

[@Diagknowstics]

Thanks for the kind words!

[@sidlevy7828]

Thank you for this easy explanation. This helped me understand regression analysis with dummy variables.

[@Diagknowstics]

Thanks for the kind words! Glad it helped!

[@user-xe9cn9ze4z]

this is amazing thank you!!

[@Diagknowstics]

Appreciate the kind words!

[@user-ov1to6cs7i]

thank you Professor

[@Diagknowstics]

Glad it was helpful!

[@sangcheolsong6530]

One question about three-way interaction terms. Let's label each variable A(main variable), B(1st moderator), C (2nd moderator). I'm interested in (hypothesize) the relationships A-B and A-B-C. Should all two-way (AB, AC, BC) and three-way interaction terms (A * B * C) be included in a regression model and result or would be it fine to include some of interest (AB, ABC) only?

[@Diagknowstics]

Great question! Yes, to get an unbiased estimate, you would have to include all of those interaction terms in your regression. Hope that helps!

[@michaelpappas3857]

Thanks for such a wonderful explanation!

[@Diagknowstics]

Glad it was helpful!

[@anarchonomics]

Thank you so much man! So clear and concise.

[@Diagknowstics]

Thank you! Glad it helped!

[@malgorzatakowalczyk8651]

Thank for sharing. Quick point. Why to bother with any interaction therm ?. Simply, fit two lines. One for female (y_f=b0_f+b1_fX_f) and one for male (y_m=b0_m+b1_mX_m). Compute difference in tangents = b1_m - b0_m. The result should be the same without any quadratic term. I am just interested why to introduce an additional term. Cheers Piotr

[@Diagknowstics]

Thank you for your great question! You're absolutely right that it is "equivalent" to doing two simple regressions. That said, in the real world of research there are usually many variables to consider simultaneously, and the number of equations can go up exponentially in that case. If you want to see for example how the impact varies simultaneously by a combination of one's race, gender, age, you would need 100+ equations depending on how many race and age groups there are. And it's much easier to have one big regression rather than going back and forth between 100+ equations, especially when it comes to reporting outputs. Hope that helps!

[@freddyfrancoisateba4419]

You are a wonderful one,Thank you!

[@Diagknowstics]

Thanks for the kind words! :)

[@ismaildemir773]

Thank you so much! Best explanation

[@Diagknowstics]

Thank you for the kind words! Glad it helped :)

[@rizalashari2106]

Thanks for the explanation it's so crystal clear

[@Diagknowstics]

Thanks for the kind words!

[@mtahir9181]

Thank you, very clear explanation.

[@Diagknowstics]

Anytime! Glad it helped :)

[@sodeclase]

do you have any videos on logistic regression?? or poisson ??

[@frankunderwood6427]

You are my hero

[@Diagknowstics]

Thanks for the kind words!

[@lorettavillas9704]

You are amazing thank you

[@Diagknowstics]

Thank you for the kind words! :)

[@alisaja11]

nice video!!! thanks

[@Diagknowstics]

thanks for watching! :)

[@EndG00gle]

Lol if you’re female. 10 points less in your GRE. Cold.