> -model 5 : 1 2 3 0 -bucket 0 reg

It seems that you left out the 4th column, and it should be like this:

-model 5 : 0 1 2 3 4 -bucket 0 reg

> Now, in my output, the F-stat and R^2 would indicate the degree to which my
> explanatory data fits the data?

Yes, that's right.

> Or, would I look at the Coef # est & tstat of my explanatory variable for this? I ask
> this because the coef est / tstat map of one of my covariates appears to display
> clusters I had anticipated the explanatory variable to modulate.

The Coef # est & tstat for the 5th column should be consistent with the F-stat from the following:

-model 5 : 0 1 2 3 4 -bucket 0 reg

Is it the case?

> Covarying for this particular coefficient data introduces a similar expected cluster in
> what I believe to be the full regression model, but leaving it out of the model removes
> the cluster. What is an explanation for this? If the coef est / tstat map of my explanatory
> variable doesn't show significance at this area but the map of aforementioned covariate
> does, then is it safe to assume that this is just an effect of my covariate? Not sure what
> to interpret from the cluster in the regression subbrik.

Your terminology is confusing. Explanatory variables and covariate can mean the same thing, which can be either categorical or quantitative variables.

Is the above question the same as the one below?

> But if I'm getting a significant cluster in coef #2 t-stat and not in coef#5 t-stat, then that
> is just the contribution of the col 2 covariate, correct?

In your current model, the significance of coef #2 t-stat means the group difference at the center value of column 5. If you remove column 5 from the model, do you get similar significance for column 2?

> I'm not familiar with 3dMVM. How would I use 3dttest++ when my explanatory variable is continuous?

In 3dttest++ you specify the five columns as five covariates.

Gang