Winter 2016/2017: Personal Dashboard
Spring 2016 project: Dream Analysis in R
Self-quantifying is the tracking of one's biological, physical, or behavioral information. It consists of data acquisition through technology (wearable sensors, mobile apps, software interfaces...), and subsequent statistical analysis.
Over the past few years, self-quantifying has evolved into a movement where people around the world get together and present their experiences and findings. See quantifiedself.com for more information.
In our meetings at USC, presentations can include reviewing a tracking app or device, demonstrating an analytics tool that can be applied to self quantification, getting feedback on study design, or presenting findings.
Fall 2015 Final Project - Reading Habits
Conclusions: In analyzing Pew's research study on reading habits, we were unable to come up with a good predictive model for how many books a person will read based on their demographics. We did, however, have success predicting whether someone would read or not, and whether they owned an e-reading device/preferred digital formats.
The most significant factors were education level and sex (higher educated people and females tend to read more). State level relationships (state spending on education, graduation rate) do not give insight on reading habits within this dataset. County-level information is slightly more significant, but not quite as much as we expected.