Inspired by Dan Meyer‘s 2012 Annual Report, I decided to start tracking data on myself in February. Thanks to the Keep Track Pro android app on my phone, I have been tallying each and every coffee I drank since February 18. I’ve had a lot of fun self-analyzing through this activity and am trying to figure out a way to get students doing something similar in AP Statistics this year.

On to some graphs!

First, I decided to do a bar chart on the counts of coffees I drank in a day. I’d never gone above three cups in a day, but somehow survived the 18 days I had no coffee.

With this preliminary information in mind, I decided to think about the Law of Large Numbers and a moving average. The basics of the theorem suggest that over time, the average of some experimental outcome will approach the “true” value. I wanted to infer what my “true” average coffee intake per day was. Using some Microsoft Excel formula wizardry, I calculated an average after each day of the tracker, and decided to plot this cumulative average over time.

I noticed an interesting pattern here. As the Law of Large Numbers suggested, the cumulative average began to settle by May at around 1 coffee per day. Thinking about my routine during the school year, I would normally have a coffee at home before work, and every once in a while, maybe one or two times a week I would have another after work at home. Then I noticed that the graph became unsettled again after school ended and revealed an upward trend. At this point, I wanted to examine in a little more detail the reasons for the instability. Instead of looking at the cumulative average, more Excel wizardry enabled me to look at a running average, only looking at the prior 14 day period. This displayed a higher resolution of my caffeination habits.

I noticed a few regions of interest in this graph. First, around late March to early April, the running average reveals a drop in my drinking habits. I roast my own coffee, and I bet this period corresponds to a time when I had forgotten to re-order beans to roast and had to wait for them to come in. Secondly, I notice that about 2 weeks after school ends, my moving average starts to increase drastically. The red trendline begins to settle around 2 cups per day. This corresponds to what happens when I am at home without much structure. I either make several cups of coffee during the day or hang out at the coffee shop. The instability in the cumulative average (blue) is understandable based on understanding the running average (red).

This is the greatest thing I have ever seen. This lends itself perfectly to standard deviations/z-scores with student surveys, etc. I wonder if you could try to run a correlation between amount of coffee and how a good a day was (1-5) scale. Could be a good exercise with kids. “How many days would you need for the relationship to be reliable.” And just for yourself. How much value does a cup of coffee add to your happiness in a day. A little bit more work, but possibly worth exploring.

so many grammatical and punctuation errors. Yikes. Forgive me.

I was thinking of doing a personal project something like what you described.

Do you mean getting students to rate their days with respect to their own coffee drinking? Maybe “juice” would be a better beverage for that experiment.

I think you can do a project with surveys with kids that ask them how many bags of takis they eat in a week. Then from that data set you can put them into a distribution and analyze it/teaching standard deviations and what not.

I was thinking about the correlation piece would just be fun to do for yourself, but it you can get kids on board that would be awesome.

Awesome. I was already going to do a survey so I think I can add a question about “how many bags of hot cheetos or takis do you eat in an average day?”

I was going to use the survey repeatedly over the first 5 or so weeks as we learn displays for categorical and quantitative variables and how to compare distributions.