Actually I was being nice; because Steve didn’t want any comment on motive. My conclusion after seeing the show?… Thompson’s data is his legacy- then add what you both say to get the big picture.

Data becomes something other then it is meant to be. This seems to be contagious in climate science!

Mathematically yes, but in practice the nearly-singular matrix will give you numerical problems.

If so, you will encounter the same numerical problems trying to orthogonalize the regressors.

So no gain.

Unless, of course, you are willing to give lower priority to the lower ranking PCs, per Hotelling 1933, but contra Jolliffe 1982.

Orthogonalizing the regressors merely changes the basis for the space spanned by the regressors. OLS picks the same point from this space, and you get exactly the same forecasts, R2 and regression F test for the collective significance of the regressors.

Mathematically yes, but in practice the nearly-singular matrix will give you numerical problems.

Jacoby defines what he means by “good data”

If we get a good climatic story from a chronology, we write a paper using it.

He goes to say that he evaluates the quality of the data:

The quality can be evaluated at various steps in the development process. As we are mission oriented, we do not waste time on further analyses if it is apparent that the resulting chronology would be of inferior quality.

It appears to me that Jacoby is definining data of inferior quality to be that which does not provide a good climatic story. It sadens me that Jacoby does not find fault with his reasoning.

If we get a good climatic story from a chronology, we write a paper using it. That is our funded mission.

It just struck me reading that. That reads more like the mission of a publishing house than a scientific team. There’s something intangibly just plain wrong about the way he chose to express that. It’s almost like he’s talking about a sci-fi novel.

Does this choice of words on the framing level bother anyone else?