For this reason it is a good idea to get away from regressions, which assume stationarity and linearity, and use instead a copula-co-movement time-fit to a pseudoautoregressive ARMA process. This would capture the non-linear temporal changes, especially as it relates to tree ring variation.

It is remarkable to what extent people rely on linear regressions when much better alternatives exist. The obvious reason, of course, is that linear regressions give the answers people want to hear and we all know what that is. Even statisticians who know perfectly well that regressions fail to capture heteroskedastic invariance continue to use them simply because the results are easy to communicate to whoever controls their budgets.

I then offered an example. I drew a chart of car radios per capita in the US and increased life expectancy. For the sixty year period examined at the time, the correlation coefficient was 0.95 which seems close to perfect. I then asked the good professor to explain why the increase in mumbers of car radios led to increased life expectancy on a causal basis. he could not do it. A researcher must have some proof of relationship between a phenomenon and a result to presume a causal conncection between the two.

I caution young scientists not to assume facts not enetered into evidence and subject to examination. The same caution applies to all things in life.

rats.

it seemed such a cost effective way to end the current downturn…