Stats Made Easy

The Twin Cities got dumped on today with nearly a foot of heavy, wet snow. The weight of it snapped trees branches around my yard and left our Stat-Ease office powerless due to downed lines. I cannot say that we Minnesotans were not warned, but just before the storm hit, I heard one television weather man, a new guy giving his first forecast, say that the snow would fizzle due to the low pressure sucking in dry air. This reminds me of the movie “Weather Man” a dark comedy starring Nicolas Cage, whose character at one point before going on television decides to get the forecast from the real meteorologist behind the scenes:
> Meteorologist (M): “You should say, ‘We might see some snow…’ “…but it might shift south, miss us.'”
> Cage as Chicago TV weather guy: “I can say it, but I sort of wanna understand it. Why is it?”
> M: “Well, it’s Canadian trade winds.”
> Cage: “Behind all of it?”
> M: “Yeah, this will get pushed by wind out of Canada.”
> Cage: “So what’s it gonna do?”
> M: “I don’t know. It’s a guess. It’s wind, man. Blows all over the place.”
It doesn’t matter how variable snowfall can be for any given area, the new weathercaster in the Twin Cities television market will be in the doghouse now for a long time because he got this biggest storm of the winter wrong. As the move “Weather Man” says in its tag line: “In life, accuracy counts.” On the other hand, in our business of industrial statistics for predictive modeling we allow ourselves an out by this disclaimer: “Statistics means never having to say your certain.” This is lot more realistic!

PS. FYI, here’s a link to a forecasting model devised from neural network analysis of historical records that allows you to predict the ratio of snowfall depth to liquid water, a vary tricky property: University of Wisconsin – Milwaukee (UWM) realtime snow ratio forecast page .