03-19-2014 12:29 PM
I have two models that i need to compare by conducting a F-test but got no clue how
I did google and came across this :
proc reg data = mydata ;
model y = x1 x2 x3 ;
test x3 = 0;
Is this something that i need to do,anyone please?
03-19-2014 12:53 PM
I would suggest to take some basic statistical training to learn why the test statement is redundant and how to interpret the analysis of variance of your model to determine if variable x3 is useful to predict y.
03-19-2014 01:42 PM
my second model is actually more complicated than it shows here in my post. For example lets say it is b0+b1x1+b2X2+....+b6x6
and i know how to test the null hypothesis based on the global F test regardless of whether the individual variables (x3-x6) contribute to the model or not but are satisfactory F and p results for the second model enough to chose it over the 1st model?
so there is no way i can compare these models and based on F and p determine if the complete model is good to go or not without running them separately ?
03-20-2014 07:52 AM
A likelihood ratio test seems in order here. Fit the full and reduced models in PROC MIXED, and get the difference in -2 log likelihood values. This will be distributed as a chi square with degrees of freedom equal to the number of parameters "deleted" by reducing the model.