The Geopolitics of the Pepsi Center

This post by Jeronimo Cortina.

Today R. G. Ratcliffe from the  Houston Chronicle devotes a few paragraphs to talk about the seating arrangements of the 2008 Democratic National Convention. I got curious and decided to check out the Delegate seating for the Pepsi Center. A quick analysis suggests that most of Clinton’s Delagates got the Second row. Only 7 or 5 (depending on how you count ) out of the 22 States in which Clinton won got “floor level” seats (PA, MI*, FL*, NM, IN, NV, NH). However, almost all of these states but Indiana are considered as potential “swing” states.

McCain’s home state (AZ) it’s at the very back, but probably its delegates have a better view than some of their Texas counterparts whose view is being blocked by a network television interview platform… but McCanin is not doing great in AZ where almost 30% of the registered voters surveyed are undecided!! wouldn’t be more important to have AZ on the floor than AK and Guam?… Just asking.

Comment on “What are you going to do with your Ph.D. in Statistics?” conference

This post is by Matt Schofield.

The conference consisted of two panels discussing various aspects of the working life of statisticians. The statisticians on the first panel were all currently working in academia, while the statisticians on the second panel were all working in industry.

Academic Panel

If we want to pursue a career in academia, research should be something we enjoy. Panelist mentioned that teaching, while often a burden, should not be something that makes our lives miserable. As Eric Bradlow said, the remuneration for being an academic is not enough compensation for hating teaching and being miserable.

The panelists agreed it was important to work in a department where the people valued and respected the research that you did. The panelist’s research ideas came from a number of different sources, including collaborators, seminars and conferences (and they encouraged us to attend the latter two).

The panelist’s discussions reminded me that perhaps the most important aspect in choosing potential academic departments is finding a good fit. An important part of working life (I think) is being valued and finding collaborators, not only in the department you work in, but also in other departments around campus.

Industry Panel

Communication is a big part of working in industry. Although teaching students is not usually required, consulting with collaborators and colleagues is. There is not as much flexibility in industry as with academia (research must be in the companies interests), however, the compensation is usually much better.

All industry panelists agreed that statisticians must be excited by data. Many of the big companies (such as google, AT&T, etc) have an abundance of data. In order to thrive in these environments data should challenge and excite you.

The reception after the conference was a good chance to meet and talk with the panelists and ask questions about jobs in both academia and industry. It was a good time for me (as a postdoc) to evaluate what direction I hope to take my statistics career. Congratulations should go to the Columbia post-graduate statistics students for organizing such a successful conference.

Quantitative Methods for Negotiating Trades in Pro Sports

I recently had some thoughts about negotiating trades in the NBA. Specifically, I heard that the Lakers and the Bulls were having daily discussions about a trade involving Kobe Bryant, for at least a week; that seemed like a long time to me. Was this week-long series of conversations productive and/or necessary? Are there no quantitative methods for structuring trade negotiations that could have been used to save these teams some time and energy? I’ve outlined a potential solution, which can probably be improved using methods from the literature on (1) statistical models for rankings and (2) bargaining and negotiating.

My idea is this: First, construct a list of all possible trades to be made between two teams, which would involve closely examining the entire roster of each team, accounting for salary cap restrictions that preclude certain trades, and including or excluding specific key players. Then, instruct each team to rank these possible trades from the most desirable trade to the least desirable trade. These two sets of rankings will naturally be each other’s approximate inverse, because a very good trade for one team will most likely be a very bad trade for the other team (Kobe Bryant for Chris Duhon, anyone?). Lastly, the negotiations consist of each team taking turns eliminating the lowest-ranked trade from their respecitve lists, until the two lists have only one trade in common. If this trade is rejected by either team, then – and here comes the part that I think could be powerful – no trade can be made between these two teams until at least one of them changes their rankings. It is a framework that could be used to, at the very least, save two teams some time when negotiating a trade.

The only similar setting that I can think of is when opposing lawyers eliminate potential jurors from a jury pool (they call these “peremptory challenges”). Does anyone know of another situation in which opposing agents rank items and eventually must agree on a compromise? Maybe there is something in the bargaining literature.

The statistical question of interest is this: What is the percentile of the rank (for each team) of the jointly optimal trade? (That is, the last trade that remains on both lists after eliminations are made). It would be nice if, in the pro sports example, both teams could improve significantly. This would probably only happen in an “apples for oranges” type of trade. Some preliminary work in the Lakers-Bulls example shows that the jointly optimal trade is in the 47th percentile for the Lakers and the 48th percentile for the Bulls – not too great for either team. A bunch of assumptions were made in this example, though, so it’s probably not too informative right now. If a probability model is used to generate the two sets of rankings, then the pair of percentiles of the jointly optimal trade, (p_1,p_2), would be a random variable of interest.

Numbers

When I tell people about my work, by far the most common response is “Oh, I hated statistics in college.” We’ve been over that before. Sometimes someone will ask me to explain the Monty Hall problem. Anyway, another one I’ve been getting a lot lately is whether I watch the show Numbers. I’ve never seen it (I don’t have cable), but I’m a little curious about it–does anyone out there watch it? Is it any good? Just wondering….

Statisfaction

My friend Mark Glickman (I call him Glickman; Andrew calls him Smiley) has some fun statistical song parodies. When I was taking Bayesian Data Analysis in graduate school, he came in as a guest lecturer one day and sang them for our class. It was really fun–I don’t think there’s anywhere near enough silliness in most statistics classes. Click here for some music and lyrics.

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More on Matching

Dan Ho, Kosuke Imai, Gary King, and Liz Stuart have a new paper on matching methods for causal inference. It has lots of practical advice and interesting examples, and I predict that it will be widely read and cited. Check it out here.

…and on a completely unrelated note, Happy Birthday, Mom!!

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SAFE

I don’t need art to be work-related. In fact, I generally prefer that it’s not. But there’s an exhibition at MOMA called SAFE: Design Takes On Risk, that looks pretty cool. Items range from practical (chairs with well-placed hooks to hide a purse) to pseudo-practical (suitcase-like containers to keep bananas from getting bruised) to borderline neurotic (slip-on fork covers). And earplugs. Lots of earplugs. For those who don’t live in the city or just don’t want to shell out the $20 entrance fee, there’s an online exhibition: http://moma.org/exhibitions/2005/safe/.

Geek Alert

Last week I substitute professed a mathematical statistics course for a friend who was out of town. I was sort of dreading it: interpretation of confidence intervals, Fisher information, AND hypothesis tests, all in one class, less than 24 hours before the start of Thanksgiving break. I didn’t have high hopes for the enthusiasm level in the room. BUT it was actually pretty fun. The Cramer-Rao inequality? It’s really cool that there’s a derivable bound on the variance of an unbiased estimator, and even cooler that that bound happens to be the inverse of the Fisher information. It’s not the kind of stuff that comes up much in my own work or that I’d want to do research on myself, but I got a kick out of teaching it.

More on Teaching

It’s College Week at Slate: Click here for the thoughts of several prominent academics on improving undergraduate education, sometimes with the aid of a magic wand. I of course first read “Learn Statistics. Go Abroad” by K. Anthony Appiah. I completely agree with Dr. Appiah’s view that many college graduates can’t evaluate statistical arguments, leaving them unequipped to make informed decisions in areas such as public policy. He writes “So I favor making sure that someone teaches a bunch of really exciting courses, aimed at non-majors in the natural and social sciences, which display how mathematical modeling and statistical techniques can be used and abused in science and in discussions of public policy.” Again, I agree completely. But (as we’ve discussed here and here) teaching those kinds of courses is really hard, and probably requires that magic wand.

Como se dice “I hate statistics”?

As every statistician knows, many people hate our field. How many times have we all heard “You do statistics? I HATED that class in college!” (I remember one of my college professors complaining indignantly that no one would presume to tell an artist that he hated art.) There are all sorts of factors that probably contribute to the unpopularity of statistics: it’s often one of the few quantitative courses required for social science majors, who may be less into mathy subjects to begin with; it’s not always well-taught (although what subject is?); the logic of hypothesis testing isn’t terribly intuitive. Lately I’ve been wondering if statistics’ bad reputation is confined to the US or if it’s more universal. My own experiences really don’t help to answer that question: Sure, most of the “I hate statistics” comments I hear come from Americans, but that’s not surprising given that I live in the US. And many of the international people I know are work-related, so of course they tend not to hate statistics. Anyway, my wondering about this is self-serving. Starting in January I’ll be teaching intro statistics at Pompeu Fabra University in Barcelona, and I’ve been wondering what the students will be like. I’m hoping not to hear “me disgusta estadística” too much….

Hardly Statistical At All

I’m sorry. You come to this blog seeking deep thoughts and insight, and I give you links and rants. Or gratuitous plugs for things that appeal to me, which is what today’s post contains. There’s a new-ish magazine/literary journal called n+1. It’s full of deep thoughts and insight on various topics, from travel to domestic violence to the vicious cycle that is dating. And it’s called n+1–how cool is that?

Stat/Biostat Departments

I wish there were more connections between statistics departments and biostatistics departments. I’ve been working with survival data recently, and it’s made me realize another gaping hole in my statistical knowledge base. It’s also made me realize that I wish I knew more biostatisticians. And I’m one of the lucky ones, really, because Columbia has a biostatistics department and I do know some people there. Often when statistics and biostatistics departments don’t have close connections, it’s for understandable reasons. When I was in graduate school at Harvard, for example, the statistics and biostatistics departments were (still are, I guess) separated by the Charles River and it took a 45-minute bus ride to travel between the two. I almost never made that trip. Still, there are some great people in the Harvard Biostatistics Department and I’m sure I could have benefited from working with or taking classes from them. Here at Columbia, the biostatistics department is a subway ride away from the statistics department, and if you take the 1 train then there’s that awful subway elevator to contend with (how on earth is that not a fire hazard?). Lots of universities don’t have both statistics and biostatistics departments; of the ones that do there are some with close connections. I just wish that was the rule rather than the exception.

The Bell Curve

I spent too much of one day last week reading this article and everything it links to. Charles Murray, one of the authors of The Bell Curve, also has a piece in the August 2005 issue of Statistical Science called “How to Accuse the Other Guy of Lying with Statistics” (part of a special section “celebrating” the 50th anniversary of “How to Lie with Statistics”–it’s a fun issue).

I haven’t read The Bell Curve myself, so I better stop now.

President’s Invited Address

Rod Little gave the President’s Invited Address at the Joint Statistical Meetings in Minneapolis earlier this month. He was talking about the Bayesian/frequentist “schism” and resolved it in the following way: Bayesian methods are good for inference; frequentist methods are good for model assessment. I like that. (I’m not ashamed of being interested in the frequency properties of my Bayesian models.) He said it better than I can, so check out his slides here.

Overheard in Harvard Square

Fall 2003, while the Boston Red Sox and Chicago Cubs were both still in the playoffs.

Girl on cell phone: But if the Red Sox and Cubs both go to the World Series, that means one of them will have to win. But that’s a probability zero event, so that would, like, unmake existence.

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Bad Graphs

One of the links on this blog is to Junk Charts, which shows and discusses all kinds of good and bad graphics found in various news sources. It reminded me of one bad graph that was printed in Amstat News of all places, showing that statisticians (or at least statistics-related publications) aren’t immune to graphical mishaps.

JSMmap.jpg

State Locations of Joint Statistical Meetings: 1953-2002

The different coding schemes correspond to how many times the JSM has been held in each state between 1953 and 2002. But the coding schemes are a little too psychadelic and make my head swim, in addition to being difficult to differentiate. And you might expect the amount of color to have something to do with the number of JSMs; but no, the darkest states are those with only two meetings, while the much lighter grey states have had five.

The next month Amstat News published a letter to the editor, saying basically the same things.

Terrorist Risk Revisited

There’s a fun little article in the Harvard Magazine on risk perception. David Ropeik and George Gray at the Harvard School of Public Health wrote a book Risk: A Practical Guide for Deciding What’s Really Safe and What’s Really Dangerous in the World around You, which sounds interesting. The article also mentions a study by the University of Michigan transportation Research Institute comparing motor-vehicle deaths in October – December, 2001 (right after the September 11 attacks) to the same period in the previous year. (Click here for a previous post and comments on this topic.) The Michigan study concludes are that there were 1,018 more traffic deaths in late 2001 than in late 2000 — I haven’t read the study myself, so I’m just passing along what they report. (Is 1,018 large relative to the average number of traffic deaths and its variability? I don’t know.)

In a similar vein, I keep telling my mom how much more likely it must be that I’ll be hit by a car or by lightning than be bombed on the subway. I don’t think it makes her worry about me any less.

20-minute wait on the GW…

How does the traffic reporter on the radio know how long the wait to get across a bridge or through a tunnel is? Do people collect data on this? Is the reported wait time merely a function of how long the “line” leading to said bridge or tunnel is? Or are other factors (maybe time of day or the general badness of traffic at the time) involved? Has anyone ever investigated whether these waiting times are accurate? Just wondering.

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Statistical Crystal Ball

Dean Foster, Lyle Ungar, and Choong Tze Chua at the University of Pennsylvania have created a mortality calculator. It’s pretty cool–you enter all kinds of information about your health, habits, family history, etc., and it predicts how long you’ll live. Not to brag, but my predicted life span is 94 years, with upper quartile 103.99.

Dean Foster’s website also links to Northwestern Mutual’s Longevity Game . I like the Penn version better–Northwestern Mutual has multiple choice questions, and sometimes the choices don’t seem all-inclusive. For example, their alcohol question has the following 3 choices:

(a) Don’t drink or never drink more than 2 drinks a day
(b) 3-4 drinks more than 2 times a week
(c) 5 or more drinks at one time, more than once a month

I sometimes have more than two drinks in a day, but I don’t think I have 3-4 drinks more than 2 times a week. If I choose (a), my expected lifespan is 92 years, pretty close to the Foster et al. calculator (which asked for number of alcoholic drinks per day). If I choose (b), it drops down to 88 years (no error bars reported).

I wonder if my numbers are inflated, though–my family history is free of almost everything, but that could just be because my parents are still fairly young.

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