Publications

As a data miner, I can say with 100% confidence that I have discovered two patterns when 1) submitting a Regression Model Proposal to a prospective client, and 2) handing out the final presentation deck to the client: The prospective client always looks at the last page of the proposal for the cost of the project; and the client always looks for the page with the R-squared for the regression model built.

As a consulting statistician presenting the realized regression model, I have an effortful job explaining (rather, re-teaching) to otherwise bright clients, who inform me of the statistics courses under their ("black") belts, some basics concepts that they never fully understood at the outset. At this point, I know that building the model is not the hard part of the project, but presenting the results is. Invariably, the first obstacle is explaining why their "pet" variables are not in the model. The second hurdle is to absorb their shock when they discover their misunderstanding of R-squared.

Accordingly, I always scrounge for statistical tidbits as aids in helping me explain (by show-and-tell) statistical concepts, which the clients have unwittingly misunderstood for too long. My latest statistical tidbit: I build two OLS regression models, regressing Y1 on X1, and regressing Y2 on X2, using the data in Table 1, below. Both OLS regression models are identical! Which Model is Better?

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