At that first meeting of Round 2 (after our summer break, and with a lot of new people), we had a great discussion and decided on Mindware. See our review, and from there, you can explore all the other courses we’ve taken and many that are on our list of possibilities.
Early on, after each week’s meeting, I’d send out a summary of our meeting to help with continuity. Below is the one I sent after the first week of the Mindware course. You can see it took some time! Gradually I slacked off on this practice, sending out only what we agreed to cover for the following week, but I think the summaries were useful early on.
Subject: Week 1 notes and next week’s goal
We had an excellent discussion in our new home at Lexington Community Center. We welcomed new participants ___ and ___. We shared the quiz problems that we got wrong the first time around and the subtleties about them, using that to launch into more detail and clarification, and further to related topics.
___ had a good skeptical question about the example where greater volatility in a stock was said to produce greater long-term gains, though that wasn’t an outcome of the basic statistics being presented, and riffing off of that we seemed to agree that stock markets and finance were of particular interest. A couple of keywords to look up if you’re interested:
Black Scholes Model, which won a Nobel prize (noted by ___):
“Black-Scholes is a pricing model used to determine the fair price or theoretical value for a call or a put option based on six variables such as volatility, type of option, underlying stock price, time, strike price, and risk-free rate.”
Capital Asset Pricing Model (also thanks to ___)
In short, it’s clear that intelligent and innovative use of statistical models has huge value in the world of finance.
___ brought up the movie and book Moneyball, and we talked about the use of variables in a model: both the discovery of unsuspected significant variables (as in the Moneyball story), and the culling of redundant or dependent variables. We emphasized that the creation of a useful model is key, and not so much a science. In the lessons, maybe the most relevant item is Validity, in the Correlation section: the degree to which one measure predicts another (in other words, whether the model you create is actually useful in making the predictions you’re interested in). In the Machine Learning field (another fun tangent), the careful selection of inputs is called Feature Engineering.
I brought up some items from the related reading in the book:
Fundamental Attribution Error, and the related item The Interview Illusion (the source for one of the course’s teaser lines). Simply put: we tend to undervalue much contextual information that we don’t consider, and rely on the few bits of information we have in front of us.
Reversion to the Mean (also a source for teaser line, where being on the cover of Sports Illustrated probably means you won’t perform as well afterward.)
Really, everyone present brought interesting things to the conversation. Too much to try to reconstruct. What a great group.
Attendees around the table:
[list of attendees]
For next week:
Lessons 2 and 3
See you at 7pm next Monday in Room 221 of Lexington Community Center. Feel free to post any questions and discussion items to the list.
Over time, we settled on a regular meeting place and time. As we finished each course, I sent out an email to the group saying we’re going to use the next session to pick another course. People came and went as their schedules and interests allowed. As the pandemic hit in 2020, we started meeting on Zoom, and we’ve been doing that since then.
So, that’s the end of the narrative for now. I’ll keep adding more, but first I want to focus on adding to the list of courses we’ve taken, because we think you might enjoy them. It might be easier to use our list than to sort through the thousands of courses available at the various sites.
Good luck, and please feel free to contact me for advice, and please let me know if you’ve found this site useful, or if you have your own experiences to share!