Gay Marriage

I don't know if there is any issue with such a generational divide...




It currently appears that it's only a matter time before legalization.



Nate Silver projects when each state will legalize gay marriage via a regression model with the following variables:
1. The year in which (a gay marriage type) amendment was voted upon;
2. The percentage of adults in 2008 Gallup tracking surveys who said that religion was an important part of their daily lives;
3. The percentage of white evangelicals in the state.

He projects about half of states will legalize by 2014 and the rest by 2024. I personally think the status quo bias is a bit stronger than that and it'll take a little longer...

*Edit - Nate Sliver actually projects "the dates when the model predicts that each of the 50 states would vote against a marriage ban." The dates seem a bit more resonable now, but i still would be more conservative in the projections. Though maybe we've hit a tipping point?

Childbirth and Evolution

From Half the Sky:

"One reason women die in childbirth has to do with anatomy, arising from two basic evolutionary trade-offs. The first is that once our ancient ancestors began to walk upright, too large a pelvis made upright walking and running inefficient and exhausting. A narrow pelvis permits fast running. That however makes childbirth exceedingly difficult. So the evolutionary adaptation is that women generally have medium-sized pelvises that permit moderately swift locomotion and allow them to survive childbirth - most of the time.

The other trade-off is head size. Beginning with our Cro-Magnon ancestors, human skull size expanded to accommodate more complex brains. Larger brains offer an evolutionary advantage once a child is born, but they increase the chance that a large-headed fetus will never emerge alive from the mother."

Lag

Improving Self Reported BMI

Many epidemiological studies measure adiposity (obesity) through the surrogate BMI via self reported measures of height and weight. As I have mentioned before, there are many problems with this. But how can we improve on this while maintaining our level of accuracy and not using a direct measure requiring a more intensive doctor visit?

Two ideas to use along with BMI:

1. Waist size - pant size for men, dress size for women

2. Bench press - probably more reasonable for men. Could use max (weight you can bench press once), or weight you can bench press ten times.

Does the NCAA Selection Committee Use Regression Analysis?

The committees job is to pick and rank the 65 best college basketball teams (including those who won their conference tournaments). They want to choose the teams that are most likely to win games in the NCAA tournament. Now, regression analysis seems like a perfect tool for this type of dilemma, but does the selection committee use it?

Based on the slow involvement of statistics in other sports, my guess is no.

When college basketball experts talk about using "computers", I fear they are only talking comparing team's RPIs and Strength of Schedules via an eye test. The problem with these two measurements is that they are co-linear (a statistical way of saying too similar). All teams that have crappy RPIs are going to have crappy SOSs, like Penn State, since SOS is a part of RPI. Do we know how likely these measures predict success? I don't know, but there is a lot data from previous tournaments and it would be easy to determine. However, RPI and SOS would only explain a portion of the variability of the model. There are a lot of other variables that should be considered like record in the last 10 games, previous performance in tournaments, margin of victory, and potential injuries to key players. If these variables could help better predict a winning outcome (explain more of the variability in the model) then they should be included.

Yeah it's a human decision, and the committee does look at these variables and takes them into account. But there is a huge amount of bias in a purely human decision. It should be the human's decision to determine the variables in the regression analysis that makes the final decision. The committee may think that RPI is the most important variable and deserves the most weight. This may be true, but their assumption should at least be tested by regression.

During the selection show last night a committee member started to explain how they chose Arizona/Dayton over the other bubble teams. He emphasized that teams chosen were based on playing a competitive schedule throughout the year and winning quality games away from home. Of course Penn State had two huge road wins (MSU, Illinois) and Arizona didn't have any (and were 1-5 in their last six games). It's quite possible that the regression could have chosen the same 65 teams, but it would be interesting to see the analysis.

Say Goodbye to Humanities?

Cool Article in the NYT on the decline seen in humanities:


It's not too surprising since the cost of education has dramatically increased, students have desired more return for their education. Now many don't have the luxury of studying less directly applicable areas. I also wonder if humanities degrees were artificially high in the 60s and 70s from all those hippies. haha

I agree in the article's point that Obama benefit the humanities field in multiple ways. He is a role model to many and he seems to greatly respect literature and philosophy. He also plans to make it more affordable to go to school through programs like the GI Bill and increased student aid. However, if the economy doesn't turn around, those points will probably be moot.