I have blogged a few times about the course that I am teaching this semester (intro prob and stats for engineers). It’s a lot of fun to teach. I enjoy sharing some stories about statistics in the real world. I am posting a short list of the links I shared with my students this semester:
- When teaching Bayes rule, I took a tangent to discuss how statistics is important for understanding the health insurance debate. I tried to make the point that preventative health care never saves money (as some were claiming earlier in the year), since the basic premise is that many people are screened to treat just a few for disease. I referred my students a Shields & Brooks interview on the PBS New Hour. David Brooks explains this concept better than I can. PolitiFact.com provides a nice analysis on their Truth-O-Meter.
- Before Election Day, we discussed how many people are driven by their civic duty to vote, particularly in years when there is a Presidential Election. With the additional drivers on the road, there are additional risks. A JAMA article indicates that these risks are statistically significant, when comparing election traffic to a set of equivalent control days. See the image below.
- Statistics is aimed at using small samples to make inferences about an entire population. Sometimes, samples are too small (the so-called Law of Small Numbers). Wayne Winston writes about the significance of small samples in analyzing NBA data. A great defense of small samples.
- A colleague shared an interesting link with me about how the max heartrate formula was devised. You have seen this formula that appears everywhere (Max heartrate = 220 – your age, a linear model). Sadly, this formula is not based on a rigorous statistical analysis. A doctor literally eyeballed it, and drew a line through a few points. Sadly, this poor implementation of linear regression is widely used, which on some level undermines what I was teaching in class.
- Richard Florida of The Atlantic wrote an article about the geography of obesity using linear regression, using state-level obesity data. His article has many nifty images, one of which is reproduced below.