Wandering

I have taken a wandering route to this point of my statistical education. Math was a favorite subject through most of elementary and secondary school. I was selected as the most outstanding math student in my high school my senior year. I then proceeded to attend college taking only the one statistics course required to attain my major in psychology. Yet two years after graduating, I felt the itch to give some college math courses a try. I dipped my toe in the college level work with a calculus for business majors course. I enjoyed it and did not find it especially difficult. So I moved on to "full blown" calculus. That was slightly more challenging but not at all the monumental challenge it is often portrayed to be. Two more semesters of calculus followed accompanied by linear algebra, discrete methods, ordinary differential equations, and probability. I finally hit a speed bump when I took the real analysis class. That one just didn't click.

I wish I had known sooner just what statistical methods had to offer. But that's not entirely fair to myself. Part of my statistical education has been learning just how recent many statistical methods are. Even for methods that have been mathematically known for quite some time, only the recent advances in computer processing power and storage capacity have made them feasible to use. Even with the millions of transactions a second that computers can now achieve, even on the desktop, my binary analysis class was still  warned that the logistic regression that we were to perform could possibly take days to complete. Of course, the storage capacity has made it possible to have a class dataset with 1,255,429 records (I may remember this number for quite some time given the many times I've recently checked to ensure I did not loose any of those records) and over 300 variables to be analyzed.