100 independent binary variables cannot possibly span the space of interest unless you have 2**100 data points in your design, and if you have fewer points, there is a major likelihood that your independent variables will be correlated with one another, thus dramatically increasing the mean square error of your parameter estimates. As an alternative, you would need some sort of major fractional factorial design just to ensure your estimates are balanced and not correlated with each other. But why do you need 100 independent binary variables? And can you really vary 100 independent binary variables in your study? And how do you expect 100 independent binary variables to predict 1000 dependent binary variables? Are these dependent variables all highly correlated with one another? If so, then this might work, but do you know if the dependent variables are correlated with each other? (An example of non-binary dependent variables that are highly correlated are spectra, and so in this case you could possibly predict 1000 dependent variables using 100 independent variables) But anyway, without more details, it seems like your study is: collect huge amounts of data, throw it into SAS and see what the results are; I think there are better ways to go about this. ... View more