Very well described and explained David. Thank you for creating great examples that explain TFL in an understandable inspiring and visual way.
@russellthescout96392 ай бұрын
love it. can you do a dedicated econometrics series?
@oluchukwuobi-njoku22043 ай бұрын
Thanksss
@sangcheolsong65305 ай бұрын
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?
@Pixova6 ай бұрын
easy & simple, thanks
@86harbhajan11 ай бұрын
Excellent
@eeef501 Жыл бұрын
thank you! very helpful to see it with an image
@siddiqamahdi6000 Жыл бұрын
Super
@jasonthorne4124 Жыл бұрын
thanks so much !
@joshdsilva3234 Жыл бұрын
what happens if you only standardize the dependet variable?? . how do you interpret it then?
@henrikmader Жыл бұрын
easy and simple. Thank you
@JulietTheLifeCoach Жыл бұрын
Very lucid explanation! Thank you so much :)
@pen9y131 Жыл бұрын
Great video!
@ken-yo2hz Жыл бұрын
Brilliant explanation for college!
@ephantusmaingi9500 Жыл бұрын
Good one
@memonbr1153 Жыл бұрын
simple, easy to understand thanks
@leonardolombardelli4779 Жыл бұрын
I have one question: how did you calculate the correlation? Did you use Pearson, Spearman or what?
@shanshanzhang9827 ай бұрын
I have the same question. Have you solved the problem?
@muskduh2 жыл бұрын
Thank you
@brazilfootball2 жыл бұрын
This is a great explanation, thank you!
@jackladewig33432 жыл бұрын
your an absolute legend i am a second year psychology student trying to figure this out in stats and it helped so much
@irayoung10742 жыл бұрын
Are you THE David Wallace? CEO of Dunder Mifflin?
@sonja711792 жыл бұрын
very helpful, thank you :)
@valnorman14532 жыл бұрын
thank you for explaining the term "partial"!!
@manuelballon4192 жыл бұрын
Will you please post your references.
@davidwallace34112 жыл бұрын
The most important reference here is probably: Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254-284. doi.org/10.1037/0033-2909.119.2.254
@nadeeshparmar41432 жыл бұрын
This was a very lucid explanation! Thank you:)
@dalkeiththomas93522 жыл бұрын
wow thanks
@emilyfien38522 жыл бұрын
Thank you so much for this! Psyc student with an arts background... very helpful
@davidwallace34112 жыл бұрын
Glad it was helpful!
@geoidconsulting18342 жыл бұрын
Great video! Would have loved a PPT in addition.
@pushpakjmehta2 жыл бұрын
Thank you! Very simplified understandable explanation...
@tha52893 жыл бұрын
Good overview explanation.
@stephko57763 жыл бұрын
thank you!
@stephko57763 жыл бұрын
Thank you!
@hemanthkumar423 жыл бұрын
How its affect the interpret if you include reference variable? If you include reference variable, the parameters(b) will assign accordingly. so, how its different from excluding reference variable? Kindly clarify my doubt...
@hemanthkumar423 жыл бұрын
Hence, multicollinearity doesn't affect the accuracy instead it will affect the coefficient of individuals right?
@davidwallace34113 жыл бұрын
It's not about the accuracy for the overall sample or the individual, it's really about making the regression results more difficult to interpret. The weights still mean the same thing, but I will have a hard time “eye-balling” the regression and understanding how they relate to each other. If the collinearity is big enough, one of the predictors may actually change signs from its predictor with Y. This DOES NOT MEAN that there is a negative relationship (esp. if it is contrary to what the correlation is telling us); this is then just an artifact of the way regression handles highly redundant factors. Generally speaking, regression gives most of the "credit" to the predictor with the stronger correlation with Y. The predictor with the weaker correlation with Y will have a weaker B, to the point that it may change signs if the collinearity is high enough. This is because by holding X1 constant, I’m actually holding a lot of X2 constant. The key is, if multicollinearity is going on, be very careful about interpreting regression coefficients individually - you have to look at the big picture of all the predictors in the variate.
@hemanthkumar423 жыл бұрын
@@davidwallace3411 thank you so much for your clear explanation sir.....
@charlesekeoma53843 жыл бұрын
Thank you
@rekhasharma22653 жыл бұрын
Great explanation!!
@ericahawleymckee30593 жыл бұрын
Thank you for sharing your wisdom, Dave!
@davidestebanrojasospina12783 жыл бұрын
Thank you very much, you solve a big doubt that i had!