Any help is much appreciated!
I was required to run a code for class with a provided dataset to find the 5 best linear regression models in terms of AIC. This is my output:
I understand the top 5 are my 5 best models, but there is not an Intercept listed. Is this possible to not have a y-intercept in the model? I was trying to read up on this, but there seems to be some conflicting opinions.
Thank you in advance for the help!
Dan
Yes, it is possible to have no intercept, effectively it means that when all x variables = 0, that y=0.
Of course, the next question to ask is: is it a good idea to fit models with no intercepts. My answer is that I am usually skeptical when someone fits a model with no intercept, without strong justification for doing so.
Thank you!
So when I used AIC to find the "best 5 models", it's telling me the models that are the most "significant" are those that don't have a y-intercept? Could that mean that the y-intercept may not be very statistically significant itself and therefore the "best models" don't include it?
Thanks!
Also, when using Backward Elimination Method, Forward Selection Method, or Stepwise Selection method this was the "best" model:
All three of these methods were the same with a y-intercept (intercept, x1, x6, x9). The best method for AIC was the same except for no intercept (x1,x6,x9).
This is what's confusing me; not sure why the intercept is left out for the models in AIC....
Since you don't show us your code, we don't know why there is not intercept in your models; all I can say is that I think it is a very poor idea to leave out the intercept without strong justification.
when I used AIC to find the "best 5 models", it's telling me the models that are the most "significant" are those that don't have a y-intercept? Could that mean that the y-intercept may not be very statistically significant itself and therefore the "best models" don't include it?
No, I don't think that's what it is saying, at least using the statistical meaning of "significant". It means that using AIC, SAS chose models that did not have the intercept as a model term. AIC and "significance" in the statistical sense are not the same thing.
I am also usually opposed to any form of stepwise regression, as you can read on the internet dozens of people writing about dozens of drawbacks regarding this method.
I would suggest to use PROC PLS to pick up the significant variables ,which would not become over-fit model .
Check the example of PROC PLS in documentation.
@Ksharp wrote:
I would suggest to use PROC PLS to pick up the significant variables ,which would not become over-fit model .
Check the example of PROC PLS in documentation.
I wish I had said that ...
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