We have now moved this discussion from the syntax to estimate parameters or test for effects with SAS GLM to a discussion on general linear-model statistical analysis. Your new questions really have nothing to do with SAS procedures anymore, but with linear modeling. I can't really get into the many issues to consider in interpretation. I will just give a few comments.
>
> 1. Continuous variables:
>
> If I get a significant positive main age effect, a
> significant positive BMI main effect, a
> non-significant interaction effect for age*BMI, and a
> significant F-test for testing joint hypothesis of
> age and age*bmi, does it imply that age or BMI in
> itself have a significant effect on the outcome
> whereby an increase in either age or BMI lead to an
> increase in the outcome and not the interaction
> between age and BMI.
The joint effect is significant because of the significant main effect. The joint effect could be significant if either one of the simpler effects were significant. With multiple predictors, always start with the interaction and move down to main effects. With a non-sig. interaction (your situation), the change in the response with increase in one predictor does not depend on the other predictor. You may way to re-run the procedure without an interaction term to get estimates of the main-effect parameters.
>
> 2. Categorical variables:
>
> If the main effect of age on smoking (conceptualized
> as the age-trend for non-smokers) is significant and
> positive, main effect of smoking is significant and
> positive and the age+age*smoke effect (conceptualized
> as the age trend for smokers) is significant and
> positive, does it mean that for non-smokers as well
> as smokers with age there is an increase in the
> outcome. Also, we can compare the estimates between
> smokers vs. non-smokers to see which effect is
> stronger.
The joint effect could be significant, once again, if the main effect or interaction (or both) were significant. For your new questions, focus on the main effects and interactions (and temporarily forget about the joint effect). If there is a significant age*smoke interaction (always start with interactions), focus on that interaction. It means that the response variable changes with with age differently for smokers and non-smokers. In an early post I showed you how to use the noint option to directly get the intercept and slope for smokers and non-smokers. Look at the parameter estimates for age for smoker and non-smoker to see which is larger.
> Please advise.
>
> Thank you!!