At Play in the Fields of Data

I continue reading / skimming my way through Gelman and Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models. I very much like the tone of the book. It is practical... not doctrinaire. Plenty of examples. R code where you need it. Confidence intervals are plus or minus 2 standard errors, not 1.96 or whatever Student's t requires. Think about scales and units and log transformations... Don't just think about them: Try them out! Mess around with the data. Make some plots comparing your confidence intervals. Questions of causation are yet to come, but I anticipate that Gelman and Hill are not structural purists, nor identification cops. They want you to think about your problem, know your data, and especially be aware of other related results.

Everything has been comfortably familiar until the chapter on simulation of probability models. Toto, we're not in Kansas anymore... welcome to the land of Bayes.

Mountaintop madness

Of all the creatively awful ways humans have figured out for messing up the natural environment, mountaintop removal coal mining may be the most appalling and senseless. Of course, we have managed to mess things up on a massive scale many times before: deforestation of large portions of whole continents is a good example. But amazingly, trees and forests grow back: witness the northeastern United States. Mountaintops do not grow back, except possibly on a geologic time scale. And adding insult to violent injury, the mining spoils kill the streams, and the coal itself has a second and even a third round opportunity to kill the planet: globally through CO2 emissions, and locally through coal ash disposal. I guess there are people who look at pictures like these and can only see dollar signs; others turn away and see votes. Now and then somebody stands up and does the right thing...

Winter Olympics Update

Viktor Ahn: Athlete of the games.

Winter Olympics Update

Women's figure skating: Very fine. I thought Kim Yu-na was better, but then, I can't tell a Lutz from a Salchow...
Women's half-pipe skiing: Less than half-worth watching, like any half-pipe event.

How big is it?

A question that comes up (or that should come up!) in empirical research is: How big is the effect? How important? How does this effect compare with that one? Deirdre McCloskey calls it the question of "oomph." For example, in explaining variation in earnings across individuals, which has more oomph: differences in gender, in education, or in work experience?

To answer, we need estimates of the partial effects, but also a way to scale the units to make comparisons between apples and oranges. One conventional way to do this is to standardize regression coefficients by calculating the effect of a one-standard-deviation change in each variable. But as I realized while teaching this to my econometrics students this week, the comparison is tricky when some of your explanatory variables are qualitative (0-1), such as gender. What does it mean to predict the effect of a standard deviation of female-ness? (Yeah, OK, maybe something, but still...)

During my midterm today I started reading Gelman and Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models (they really could have used a catchier title!). It's interesting to read a first-rate non-economist statistician on the techniques we economists use routinely. I have already gained one tip that helps with the problem at hand- the problem of oomph. Whereas standardized coefficients usually look at the effect of a one standard deviation change in X, Gelman recommends scaling regression coefficients by two s.d. Why? This makes comparison with the 0-1 effects of dummy variables more reasonable. When the mean of a dummy variable is around 0.5 (e.g., female), then one s.d. of it is also 0.5, so a 0-1 switch is 2 standard deviations of the dummy. And it's pretty close even when p = 0.2: s.d. = 0.4. Voila! I can compare the size of the effect of gender on earnings with the effect of experience or education.

Nifty! And practical! And easy... And did I mention that all their examples are cleanly coded in R? That's especially handy for ECON 41/42 at Santa Clara University. It's a fat book, and it gets harder, but I'll keep reading.

Winter Olympics Update

Ice dancing: Gorgeous!
Interview of Bode Miller: Shameful
Aerials skiing: Yawn
Curling: Haven't seen it, haven't missed it
Caddy ELR commercial: The perfect 60-second commercial; narrative, pacing, acting, cinematography, music, and theme all come together as they rarely do in 2-hour movies. Did I mention that it is almost insufferably obnoxious? The ironic tone does not cancel out the self-satisfied jingoistic swagger of the thing. But hey, if it gets bros with dough to consider a plug-in hybrid, I'm all for it...

India's thrifty mission to Mars

Now if only we could convince them to send Sandra Bullock along...

The budget of India’s Mars mission, by contrast, was just three-quarters of the $100 million that Hollywood spent on last year’s space-based hit, “Gravity.”

Umiak kayak song redux

Three years ago I posted the lyrics I remembered from a grade school song in which one learns a few "eskimo words." I now realize that this post has garnered a number of comments over the intervening period. At the time, I promised to follow up with a post of the musical notes for the melody, which I then shamefully forgot to do. Anyway, here's a link to a midi based on what I recollect of the cheesy but catchy "Native American"-style melody, in the key of A minor. This only goes through the tricky "mukluk" verse... you can improvise the balance in the same style. Don't forget to include a dramatic ritardando and hushed pianissimo for the two lines just before the booming "Learn them if you can!" ending: a tempo, fortississimo!

Melody here.

Lyrics:

Umiak, kayak, mukluk, tupik,
Umiak, kayak, mukluk, tupik,
Umiak, kayak, Eskimo words,
Learn them if you can.

Umiak, a boat for many men,
Kayak, a boat for one man,
Umiak, kayak, Eskimo words,
Learn them if you can.

Mukluk, an Eskimo boot,
Tupik, an Eskimo tent.
If you heard an Eskimo say these words,
You'd know exactly what he meant.

Umiak, kayak, mukluk, tupik,
Umiak, kayak, mukluk, tupik,
Umiak, kayak,
Eskimo words,
Learn them if you can.