Personal blog on early retirement, a little traveling, and analytics projects. I write about what I learn as I am learning it.
The usual image of an artificial neural network:
What follows is a paper that I wrote in the Spring of 2001 for an “Introduction to Neural Networks” class that I took as part of my Master’s degree. It is mostly a review of someone else’s paper on the subject, except that I wrote the network in Excel and ran it on that year’s football season games. Fun, fun.
Continue reading “A neural network approach to college football rankings”In my last post about Fourier-Motzkin elimination I promised a real example involving baseball, and I keep my promises. This is the story of how a dusty 19th-century math trick ended up picking my daily fantasy lineups — and how I stumbled into a genuine Fourier-Motzkin elimination example without fully realizing that’s what I was doing until much later.
Continue reading “A Fourier-Motzkin Elimination Example: DraftKings Lineups”
Just finished reading “Your Money or Your Life” (1992), by Joe Dominguez and Vicky Robin:
I first heard of the book last year from the Mr. Money Mustache site
Fourier-Motzkin elimination is one of those methods that gets a page and a half in the linear programming textbooks, right before the simplex method shows up and steals the whole show. I was first introduced to it while working as an OR analyst at Cargill, and my honest first reaction was: nice, but why would I ever use this? It took me years (and one very stubborn model) to change my mind. This post is the explanation I wish someone had given me back then: what the method actually does, how the mechanics work, and a small worked example showing a model before and after the elimination.
Continue reading “Fourier-Motzkin Elimination: An Old Trick I Still Use”
(I wrote this paper in 2007 for a Statistics class I took while trying to do a Ph.D. I am sharing it here for posterity.)
McNemar’s test is a non-parametric method used on nominal data to determine whether the row and column marginal frequencies are equal. It is applied to 2×2 contingency tables with a dichotomous trait with matched pairs of subjects.
Simpson’s paradox is a statistical paradox in which the successes of several groups seem to be reversed when the groups are combined. This seemingly impossible result is encountered often in social science statistics and occurs when a weighting variable, which is not relevant to the individual group assessment, must be used in the combined assessment.
The paper evaluates the potential effect of Simpson’s paradox in McNemar’s test results and conclusions.
Continue reading “McNemar’s test and Simpson’s Paradox (and the “hot hand” in basketball)”