And how Randy must work with his colleague Keith Harrison on agreeing where Anton is so that they can finally catch him. Because Cohen’s Kappa is a measurement of agreement, you see …
So when Keith says yes,
Randy says no.
When Keith says high,
Randy says low.
But sometimes they do agree.
And believe Anton must be up a tree.
Will they get him? Must you find out?
To see, you know how to go about.
Yeah, you know I had to pimp it again …
A poem about Randy chasing Anton and being sensitive about it!
You may be right,
You may be wrong,
But why not let me.
Finish my song.
So many covariates.
To see if he is in or out.
By the lake or another sea.
Or by a mountain is he about?
But one day I’ll get him.
I know I will.
One of these days,
I’ll … oh wait, is he by that hill?
So we’ve talked a little about ROC analysis but did you know there are other sensitivity analyses to be done? Yup, there are! Well, you don’t have to be so sensitive about it! But anyway, this is done by adding and subtracting certain variables from your model. Like say, Randy pinpoints Anton to a justified dimension. Okay … but which justified dimension. Adding another variable, such as climate, can help with that. Like say Anton is in a warmer climate in that justified dimension. Then Randy can deduce that he may be in Houston or Atlanta, as opposed to say, Philadelphia or Detroit. And speaking of Houston, here’s a kitten in a cowboy hat.
Awe, now — cute and sensitive! There we go — I’ll let you just enjoy that until next time then.