Stats Made Easy

This month’s issue (June 2011, volume 8, issue 2) of the Royal Statistical Society magazine Significance [motto “statistics making sense” 🙂 ] features two intriguing articles on commuting.

The first one details Martin Griffiths, a math lecturer at U Manchester, “trying to pull out of the drive” (pp 89-91).  This poor fellow must wait upwards of 2 minutes just to get on the road from his property.  Imagine the frustration of a somewhat random stream of cars blocking your way out.  Then, just as you see a gap, another car comes along to fill it.  Griffiths provides a very impressive formula for average wait time based on a Poisson distribution. He factors in the average number of cars passing by as well as the time taken to pull out into the flow.  The bottom line: It does not pay to be timid. You’d best mind the gap (inside joke for anyone who’s traveled London’s subways) and make a move!

The second study* comes from a biker, Dr. Jeremy Groves, who spends up to 2 hours or more commuting to his work at Chesterfield Royal Hospital.  Thankfully his ride gets considerably shorter in summer when he needn’t wear baggy outerwear, which creates a real drag (Groves estimates 30% more wind resistance).  This cycling enthusiast bought a new bicycle recently – one that featured a carbon frame, as opposed to the steel one he’d bought second-hand.  Being a fan of randomized (“randomised” in UK spelling) trials, Groves completed a series of runs with one or the other of his bikes – measuring the times taken for the ride from his home in Sheffield to his work at hospital.  Seeing his run chart starting off very raggedly at the high end in January, I transcribed only the latter 28 runs (14 of each) for the chart shown.  Obviously from the overlap in the least-signficant-difference (LSD) bars the results remain inconclusive.  (If you have trouble seeing this, click the graph for a larger view of it.)  The difference is less than 1 minute in favor of the very costly carbon-framed bicycle.  Given a 3.5 minute standard deviation under summer conditions, it would take 400 total runs (200 each) to resolve whether this is a true advantage, according to a power calculation I did with the aid of Design-Expert® software.  Dr. Groves has moved on to another experiment for this summer – he plans to randomly load a 4 kg weight on his bicycle (the carbon one, I presume).  Aside from being a glutton for punishment, I suppose this fellow wonders how much it slows him down when he must carry in his laptop.

* “Bicycle weight and commuting time: A randomised trial,” pp95-96.