Type this on your command window:
open regress
If you scroll down to line 65 (might be a bit different depending on your version of Matlab), you'll see how regress deals with NaNs:
% Remove missing values, if any
wasnan = (isnan(y) | any(isnan(X),2));
havenans = any(wasnan);
if havenans
y(wasnan) = [];
X(wasnan,:) = [];
n = length(y);
end
You can see that regress removes the entire row of X, if either one or more of the entries in that row is NaN or if the corresponding output y is NaN. This is the correct way to handle missing values -- if you do not know the value of one of the predictors, you have to throw away the entire observation.
