There’s more to linear/logistic regression than meets the eye!

Over the past two days, I finished what were fundamentally “refresher” courses for me – DataCamp’s “Introduction to Regression in R” and “Intermediate Regression in R” — and doing so brought back so many memories.

There was a lot to “unpack” and I learned about a few new R package libraries that weren’t available when I took STAT 4601 and STAT 4950 in my Graduate program in Statistics at CSU East Bay.

To be honest, those packages make the practice of performing regression analysis much easier, although having to perform many of the core calculations manually did have its advantages.

But, such is the tradeoff of progress — tasks become easier, but some core knowledge may be lost in the process.

This realization brought to mind a quote I think applies:

“It is the old experience that a rude instrument in the hand of a master craftsman will achieve more than the finest tool wielded by the uninspired journeyman.”
— Karl Pearson

I decided to hold off on completing the “Modeling Car Insurance Claim Outcomes” project (#21) until I had completed these two courses. I wanted to make sure that I had worked through the applicable material before tackling it.

So, that’s next on the agenda!