about each child: his race, gen- der, family structure, socioeconomic status, the level of his parents education, and so on. But the study went well beyond these basics. It also included interviews with the students parents (and teachers and school administrators), posing a long list of questions more intimate than those in the typical government interview: whether the parents spanked their children, and how often; whether they took them to li- braries or museums; how much television the children watched. The result is an incredibly rich set of data-which, if the right questions are asked of it, tells some surprising stories. How can this type of data be made to tell a reliable story? By sub- jecting it to the economists favorite trick: regression analysis. No, re- gression analysis is not some forgotten form of psychiatric treatment. It is a powerful-if limited-tool that uses statistical techniques to identify otherwise elusive correlations. Correlation is nothing more than a statistical term that indicates whether two variables move together. It tends to be cold outside when   it snows; those two factors are positively correlated. Sunshine and rain, meanwhile, are negatively correlated. Easy enough-as long as there are only a couple of variables. But with a couple of hundred vari- ables, things get harder. Regression analysis is the tool that enables an economist to sort out these huge piles of data. It does so by artificially holding constant every variable except the two he wishes to focus on, and then showing how those two co-vary. In a perfect world, an economist could run a controlled experi- ment just like a physicist or a biologist does: setting up two samples, randomly manipulating one of them, and measuring the effect. But an economist rarely has the luxury of such pure experimentation. (Thats why the school-choice lottery in Chicago was such a happy ac- cident.) What an economist typically has is a data set with a great many variables, none of them randomly generated, some related and