Nested F-tests in R (STAT 320 chapter 3 lab_multicolinearity video 3 of 3)

[@bingbro21]

Good morning Professor, huge thanks for creating these videos for us. They are extremely helpful. I was wondering if you've covered scaled power transformations in later chapters? I found it quite difficult to understand. Thnak you very much !

[@algorithmo134]

Hi Professor, at 4:05 what does SSM mean and what does RSS mean and why is SSE full in the last row?

[@AmeliaMcNamara]

SSM is the sum of squares for the model. Since we're comparing two models, we have the SSM for the full model and SSM for the reduced. RSS is the residual sum of squares, otherwise known as the SSE or sum of squares of the error. SSE full is the sum of squares of the errors from the full model. I've organized my models in increasing order of complexity (small to large) so the full model is the one at the bottom.

[@algorithmo134]

@@AmeliaMcNamara At 3:51, I see that the value of 49.704 is found by subtracting the RSS from model 1 and model 2 but the formula presented is SSM full - SSM reduced in the numerator. I dont see how since neither model 1 nor model is the full model so can you please explain how do you get (SSM full -SSM reduced) = 49.704?