The first course I ever took in statistics was in the math department, over thirty years ago, and Dr. Spitznagel, at Washington University in St. Louis taught me a good deal despite my best efforts, assisted by Fraternity Row, to major in partying (please don’t tell my mom). So, math people, thanks for that.

HOWEVER … please, please, please do me a favor and realize that mathematics is not statistics.

On the list of people who annoy me, mathematicians who pretend to be statisticians come in at number two. If you are giving an explanation of any statistical concept and spend three-fourths of your article or more on deriving the equations to obtain the results and skim over in the remaining few pages the interpretation of the output and the situations in which it should be applied, then you are a mathematician. That’s a perfectly fine thing to be and you should teach courses in mathematics and we will both be happy.

As the official word-chooser of this blog, I am going with the wikipedia definition of statistics as,”the science of the collection, organization, and interpretation of data”.

I just read an article where DOZENS of pages into equation after equation, the author gave an example with values for specificity and sensitivity, without explaining either one. This was the first bit of information in the whole paper that one could actually apply and yet he didn’t even tell you what it was. No wonder people hate math and statistics! I would, too, if this is how it was explained to me every day.

If I were me, which, I, in fact, am, I would start explaining logistic regression by discussing when you would use it and a bit about how to interpret the overall model. Since I am, in fact, me, I did that several days ago.

Next, I would give a bit of information on useful ways of interpreting your logistic regression results:

Two useful measures are sensitivity and specificity.

Sensitivity is the percent of true positives, for example, the percentage of people you predicted would die who actually died. (Only in statistics could this be considered a positive outcome.)

Specificity is the percent of true negatives, for example, the percentage of people you predicted would NOT die who survived.

There, now, see how easy that was? You’re welcome.

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