I can eliminate the multicollinearity from the Hald dataset. I’ve seen it said that this is impossible. Nevertheless I conjecture that we can always do this – provided the data is not linearly dependent. (I expect orthogonalization to fail precisely when X’X is not invertible, and to be uncertain when X’X is on the edge of being not invertible.)
The challenge of multicollinearity is that it is a continuum, not usually a yes/no condition. Even exact linear dependence – which is yes/no in theory – can be ambiguous on a computer. In theory we either have linear dependence or linear independence. In practice, we may have approximate linear dependence, i.e. multicollinearity – but in theory approximate linear dependence is still linear independence.
But if approximate linear dependence is a continuum then it is also a continuum of linear independence.
So what’s the extreme form of linear independence?
What happens if we orthogonalize our data?
The procedure isn’t complicated: use the Gram-Schmidt algorithm – on the design matrix. Let me empahsize that: use the design matrix, which includes the columns of 1s. (We will also, in a separate calculation, see what happens if we do not include the vector of 1s.)
Here we go….
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