Should data mining newcomers have to learn programming at the same time? Here is a contrarian view, which advocates a GUI (“drag and drop”) environment. Even though the popularity of R (and recently, Python) is increasing.
I have just finished my Big Data course for 2017, and noted some concepts that I want to teach better next year. One of them is how to interpret and use the coefficient estimates from linear regression. All economists are familiar with dense tables of coefficients and standard errors, but they require experience to read, and are not at all intuitive. Here is a more intuitive and useful way to display the same information. The blue dots show the coefficient estimates, while the lines show +/- 2 standard errors on the coefficients. It’s easy to see that the first two coefficients are “statistically significant at the 5% level”, the third one is not, and so on. More important, the figure gives a clear view
of the relative importance of different variables in determining the final outcomes.
The heavy lifting for this plot is done by the function sjp.lm from the sjPlot library. The main argument linreg is the standard results of a linear regression model, which is a complex list with all kinds of information buried in it. Continue reading
I teach a course on Data Mining, called Big Data Analytics. (See here for the course web site.) As I began to learn its culture and methods, clear differences from econometrics showed up. Since my students are well trained in standard econometrics, the distinctions are important to help guide them.
One important difference, at least where I teach, is that econometrics formulates statistical problems as hypothesis tests. Students do not learn other tools, and therefore they have trouble recognizing problems where hypothesis tests are not the right approach. Example: when viewing satellite images, distinguish urban from non-urban areas. This cannot be solved well in a hypothesis testing framework.
Another difference is less fundamental, but also important in practice: using out-of-sample methods to validate and test estimators is a religious practice in data mining, but is almost not taught in standard econometrics. (Again, I’m sure PhD courses at UCSD are an exception, but it is still rare to see economics papers that use out of sample tests.) Of course in theory econometrics formulas give good error bounds on fitted equations (I still remember the matrix formulas that Jerry Hausman and others drilled into us in the first year of grad school). But the theory assumes that there are no omitted variables and no measurement errors! Of course all real models have many omitted variables. Doubly so since “omitted” variable includes all nonlinear transforms of included variables.
Here are two recent columns on other differences between economists’ and statisticians’ approaches to problem solving.
I am not an econometrician by Rob Hyndman.
Differences between econometrics and statistics: From varying treatment effects to utilities, economists seem to like models that are fixed in stone, while statisticians tend to be more comfortable with variation, by Andrew Gelman.
Proving self-driving cars are safe could take up to hundreds of years under the current testing regime, a new Rand Corporation study claims. Source: Self-driving cars may not be proven safe for decades: report The statistical analysis in this paper looks fine, but the problem is even worse for aircraft (since they are far safer per mile than autos.) Yet new aircraft are sold after approx 3 years of testing, and less than 1 million miles flown. How?
From the report:
we will show that fully autonomous vehicles would have to be driven hundreds of millions of miles and sometimes hundreds of billions of miles to demonstrate their reliability in terms of fatalities and injuries. Under even aggressive testing assumptions, existing fleets would take tens and sometimes hundreds of years to drive these miles.
How does the airline industry get around the analogous statistics? By understanding how aircraft fail, and designing/testing for those specific issues, with carefully calculated specification limits. They don’t just fly around, waiting for the autopilot to fail!
My upcoming BGGE course will have some major projects on climate change negotiation, so I’ve been reading about recent developments more than usual. As usual, Bjørn Lomborg has some intriguing ways of slicing the numbers. Unlike the old days, GCC deniers won’t get much comfort from him, though.
To be sure, Europe has made some progress towards reducing its carbon-dioxide emissions. But, of the 15 European Union countries represented at the Kyoto summit, 10 have still not meet the targets agreed there. Neither will Japan or Canada. And the United States never even ratified the agreement. In all, we are likely to achieve barely 5% of the promised Kyoto reduction.
To put it another way, let’s say we index 1990 global emissions at 100. If there were no Kyoto at all, the 2010 level would have been 142.7. With full Kyoto implementation, it would have been 133. In fact, the actual outcome of Kyoto is likely to be a 2010 level of 142.2 – virtually the same as if we had done nothing at all. Given 12 years of continuous talks and praise for Kyoto, this is not much of an accomplishment.
The Kyoto Protocol did not fail because any one nation let the rest of the world down. It failed because making quick, drastic cuts in carbon emissions is extremely expensive. Whether or not Copenhagen is declared a political victory, that inescapable fact of economic life will once again prevail – and grand promises will once again go unfulfilled.
Paul Kedrosky reproduces some data on supposedly fast growth industries:
According to a new study, here are the best and worst performing industries of the last decade as measured in revenue percentage change terms. Here are the leaders:
Some of these are doubtless valid, but the top 4 are all industries that had virtually no revenue at all in the 1990s, since they basically did not exist were not measured until Internet companies started to go public. It’s easy to have an astronomical growth rate if you make the base number small enough. Startups do this a lot – “our revenue grew 1500% in our first 2 years.” That could mean they had $1000 of revenue in year 1, and $15000 in year 3!